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Machining tools are vital components of intelligent manufacturing systems whose state and remaining useful life (RUL) determine product quality. Specifically, the wearing of tool reduces its capability of production yield and diminishes product quality. Therefore, a capability-based RUL prediction approach is proposed in this paper to thoroughly evaluate the state and RUL of machining tools. First, the connotation of tool capability is discussed, and a framework for quality assurance capability-based RUL prediction is proposed. Product quality, which can be used to assess the capability of tool, is modelled and expanded to consider non-geometric dimensioning and tolerancing (non-GD&T) features based on the classic geometric dimensioning and tolerancing (GD&T) system. Second, a physics-based model of process is developed to estimate the non-GD&T features and calculate tool wear. Third, a hybrid data-driven and physics-based model is developed to quantitatively assess the capability of tool based on the comprehensive quality estimation. Finally, a case study of rolling machining tool is carried out to verify the effectiveness and proactiveness of the proposed framework, and the final result highlights its rationality and accuracy in estimating the RUL of machining tools with better interpretation. [ABSTRACT FROM AUTHOR]

For the safety and reliability of the system, Remaining Useful Life (RUL) prediction is considered in many industries. The traditional machine learning techniques must provide more feature representation and adaptive feature extraction. Deep learning techniques like Long Short-Term Memory (LSTM) achieved an excellent performance for RUL prediction. However, the LSTM network mainly relies on the past few data, which may only capture some contextual information. This paper proposes a hybrid combination of Convolution Neural Network (CNN) and LSTM (CNN+LSTM) to solve this problem. The proposed hybrid model predicts how long a machine can operate without breaking down. In the proposed work, 1D horizontal and vertical signals of the mechanical bearing are first converted to 2D images using Continuous Wavelet Transform (CWT). These 2D images are applied to CNN for key feature extraction. Ultimately, these key features are applied to the LSTM deep neural network for predicting the RUL of a mechanical bearing. A PRONOSTIA data is utilized to demonstrate the performance of the proposed model and compare the proposed model with other state-of-the-art methods. Experimental results show that our proposed CNN+LSTM-based hybrid model achieved higher accuracy (98%) with better robustness than existing methods. [ABSTRACT FROM AUTHOR]

Addressing the need for multiple health indicators is critical to improving prediction accuracy and reducing the limitation of reliance on a single health indicator. This paper presents an AutoML model for accurately forecasting the life span of lithium-ion batteries (LIBs) in electric vehicles. Unlike previous studies focusing solely on constant current (CC) and constant voltage (CV) charging, our AutoML approach includes ICA and direct analysis to generate eight novel multi-dimensional health indicators. These novel comprehensive indicators characterizing battery aging, including the position values of CC charging (IC_CC_P), the peak of CV charging (IC_CV_ H), and the peak/position of CC discharging (IC_D_H, IC_D_ P), are chosen based on correlation analysis and combined with four health indicators from direct analysis, i.e., capacity, cycle, cluster, and checkpoint, to improve prediction accuracy. K-means clustering is employed to group similar data points. At the same time, the ruptures library facilitates the implementation of a novel algorithm for change point detection, allowing for the identification of checkpoints within each battery dataset. Experimental validation using NASA's AMES LIB cycle life datasets demonstrates a significant performance improvement from existing recent methods, with 58.93 % lower mean absolute error (MAE) and 52.66 % lower root-mean-square error (RMSE) compared to recent AutoML-based methods. This shows a significant improvement in forecasting the RUL of electric vehicle LIBs using the proposed AutoML-based approach with novel multi-dimensional health indicators. [ABSTRACT FROM AUTHOR]

To achieve the 8000-h proton exchange membrane fuel cell stack (PEM FCS) life target set by the U.S DoE and promote fuel cell hybrid electric vehicles (FCHEVs) massive introduction in the automotive market, using multi-fuel cell stack (MFCS) systems instead of single-fuel cell stack systems seems to be an interesting solution that deserves to be explored. MFCS systems' concept combines several small FCSs modules instead of using a single high-powered FCS module. The modularity in such systems can be exploited through energy management to improve their durability and extend their good energy-efficiency power range. However, FCSs' multiplicity makes it challenging to implement effective energy management strategies (EMSs). This paper proposes a remaining useful life (RUL) prognostic-based EMS to extend MFCS systems' lifetime while keeping their hydrogen consumption reasonable. For this purpose, a prognostic algorithm is developed to predict PEM FCSs' RUL in real-world automotive application scenarios. Then a rule-based EMS allocates the demand between stacks using prognostic results. The proposed strategy's performance is evaluated on a hybrid MFCS/battery system using Matlab/Simulink's environment. Simulation results show that implementing the proposed strategy instead of conventional EMSs can extend MFCS systems' lifetime by at least a factor of 2.35 while keeping their hydrogen consumption reasonable. © 2001 Elsevier Science. All rights reserved. • A post-prognostic decision-making strategy is proposed for MFCS systems. • RUL predictions are performed in real-world automotive scenarios. • The MFCS system's lifetime is significantly extended with the proposed EMS. • RUL is a suitable parameter for adaptive energy management decision-making. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice. [ABSTRACT FROM AUTHOR]

Introduction: Accurately predicting the remaining mechanical equipment is of great significance for ensuring the safe operation of the equipment and improving economic efficiency. Methods: To accurately assess the mechanical equipment degradation, predict its remaining useful life, and ensure efficient, stable, and safe operation, a degradation and life prediction model for mechanical equipment based on multivariate stochastic processes is studied. The study innovatively predicts the remaining life of mechanical equipment using multivariate stochastic processes, and facilitates the correlation analysis between performance indicators based on the characteristics of Copula functions. Results and discussion: The results showed that the Root Mean Squared Error value of the prediction results based on the trivariate Wiener process was 2.58, which decreased by 46.91% and 35.82% compared with the univariate and bivariate Wiener processes, respectively. The prediction value based on the trivariate gamma process was 3.49, which decreased by 44.95% and 40.54% compared with the univariate and bivariate gamma processes, respectively. In conclusion, the degradation and life prediction model with multivariate stochastic processes can effectively assess the mechanical equipment degradation and predict its remaining useful life. This provides an important reference for the maintenance and management of mechanical equipment, improving equipment efficiency and extend its service life. [ABSTRACT FROM AUTHOR]

Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive maintenance (PdM) plays a crucial role within the Industrial Internet of Things (IIoT) by estimating the remaining useful life (RUL) of machines so that proactive maintenance measures can be taken to mitigate potential failures and minimize disruptions. RUL estimation is enabled by gathering and processing the data sensed by sensors mounted on and around the machines at the central server (base station or cloud) after analyzing the failure patterns. However, this approach imposes a significant load on network bandwidth and leads to poor response time for the monitoring system because a large volume of sensed data has to be transmitted to the central server for processing. Moreover, due to the singularity of the computing resource, many problems, such as inefficient resource utilization, frequent offloading, single-point failure, etc., have become major challenges. To address these issues, this article proposes an edge computing-enabled predictive maintenance framework called "Collaborative Predictive Maintenance Framework" (CollabRULe), which first identifies the optimal locations for edge server placement in the deployment region by considering the QoS parameters, such as energy, delay and connectivity. Then, it uses federated learning for predictive maintenance by estimating the RUL of the machines. Simulation results show that the proposed mechanism effectively minimizes the overall network energy consumption and end-to-end delay by ≈ 60 % and ≈ 35 % , respectively, as compared to state-of-the-art approaches. Furthermore, it shows significant improvement in the accuracy of RUL prediction as compared to its counterparts. [ABSTRACT FROM AUTHOR]

Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the Pearson correlation coefficient matrix and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R2). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R2. [Display omitted] • Advocate explainable knowledge-data integration for RUL prediction throughout the life cycle, leveraging multi-sensor data. • Introduce a novel unsupervised learning framework that integrates IGCA and CMSACG to construct HI. • A two-stage prediction framework, fusing MCLF and BR-GDAU, is proposed for predicting the life cycle RUL. • The effectiveness of the proposed framework is validated using both Self-Designed Experiment-Derived and PHM 2012 Prognostic Challenge Bearing Datasets. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction of rolling bearings highly relies on feature extraction of signals. The use of denoising algorithms helps to better eliminate noise and extract features, thereby constructing health indicators to predict remaining useful life. This paper proposes a novel adaptive denoising method based on iterative feature mode decomposition (IFMD) to accurately and efficiently extract fault features. The feature mode decomposition (FMD) employs correlation kurtosis (CK) as the objective function for iterative filter bank updates, enabling rapid identification of fault features. To achieve IFMD, the sparrow search algorithm combines sine-cosine algorithm and cauchy variation (SCSSA) to optimize two key parameters in FMD. During the continuous iteration process of the SCSSA algorithm, filter length and number of modes were determined. IFMD does not require empirical setting of initial parameters. During iterative process, the signal is accurately decomposed and the noise is eliminated. Compared with other optimization algorithms, SCSSA has obvious advantages in iterative rate and global optimization. The envelope spectrum feature energy ratio (ES-FER) is used to select decomposed modes, and the mode with the largest ES-FER is chosen as the optimal mode. Bearing fault diagnosis is realized by envelope spectrum analysis of the optimal mode. The numerical simulations and experimental verifications both validate the effectiveness and superiority of the proposed IFMD in mechanical fault diagnosis. [ABSTRACT FROM AUTHOR]

In contemporary industrial processes, vibration signals collected from bearings often contain significant noise, challenging the efficacy of conventional predictive models in extracting critical degradation features and accurately predicting the remaining useful life (RUL) of bearings. Addressing these challenges, this paper introduces a novel method for predicting bearing RUL under noisy conditions, leveraging a dual-branch multi-scale convolutional attention network (DMCSA) integrated with a dense residual feature fusion network (DRF). Initially, the method applies continuous wavelet transform (CWT) to vibration signals to extract color time-frequency image data, followed by grayscale processing to construct a comprehensive color-grayscale time-frequency image dataset, thereby augmenting the model's input features. Enhanced channel and spatial attention mechanisms, combined with multi-scale convolutions, facilitate superior feature extraction and selection. The model's resilience to noise is fortified by incorporating noise into the training dataset. Subsequently, selected color-gray time-frequency features undergo fusion and relearning through the DRF framework at the model's backend. The crayfish optimization algorithm (COA) is deployed for the astute determination of the model's critical hyperparameters. The proposed DMCSA-DRF model is then applied to predict the health indicator (MSCA-DRF-HI) of the test dataset, culminating in the accurate prediction of the bearings' RUL. Validation experiments demonstrate that our method surpasses comparative models in prediction accuracy under diverse noise interferences, signifying a substantial advancement in predictive performance. [ABSTRACT FROM AUTHOR]

Accurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction has received a lot of attention as the core of prognostics and health management (PHM) technology. Deep learning-based RUL prediction methods are currently the most popular, and in order to solve the problem that most of the current deep RUL prediction studies do not consider the structural information between sensors, we propose a spatiotemporal autoencoder (STAE)-based RUL prediction method. The method extracts the time domain information from the data through the temporal convolutional network. It obtains the structural information of the sensors by converting the time series data into a graph structure by utilizing the maximal information coefficient and then performing the graph representation learning. For the two obtained features, a feature fusion method based on the graph attention mechanism is used for fusion and finally, the new fused features are utilized for RUL prediction. To validate the effectiveness of STAE, we conducted experiments on the simulated dataset C-MAPSS and the real satellite dataset SCS-PSS, and our proposed method outperforms the baseline method on both datasets. The results suggest that considering structural information between sensors in the deep RUL prediction model can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

The durability is a key factor of fuel cell in large-scale application of automobiles. It is great important to real the lifetime and health status of fuel cells, especially in vehicle driving conditions. Based on the experimental data, this paper used the model-based method and data-driven method to predict the life of fuel cells, respectively. The model-based method established the equivalent circuit model according to the mapping law of electrochemical impedance spectroscopy (EIS) and the parameters of the equivalent circuit model were identified by newly considering the phenomenon of voltage recovery, revealing the strong linear correlation between impedance and voltage. The data-driven method selected a combinatorial optimization PSO-LSSVM algorithm and compared it with the prediction results of MATLAB's time series toolbox. The results show the mean square error (MSE), End of Life (EOL) of the model-based method, the time series toolbox and PSO-LSSVM algorithm are 0.019, 8.2 × 10−3 and 4.42 × 10−5, the 260h, 438h and 427h, respectively. Comparing with the real EOL of 421h, the error of PSO-LSSVM algorithm is only 6 h, which means the remaining useful life can be predicted with high accurately. Thus this study provides a reference for fault identification and life prediction of fuel cell health management. • The equivalent circuit model is established to realize the segmented voltage prediction and analysis of fuel cells. • The MSE of combinatorial optimized algorithms PSO-LSSVM to predict voltage is only 4.42 × 10−5. • The method in paper provides a reference for fault identification and life prediction of PEMFC's health management. [ABSTRACT FROM AUTHOR]

A significant number of first-generation offshore wind turbines (OWTs) have either reached or are approaching the end of their operational lifespan and need to be upgraded or replaced with more modern units. In response to this concern, governments, regulatory bodies and industries have initiated the development of effective end-of-life (EOL) management strategies for offshore wind infrastructure. Lifetime extension is a relatively new concept that has recently gained significant attention within the offshore wind energy community. Extending the service lifetime of OWTs can yield many benefits, such as reduced capital cost, increased return on investment (ROI), improved overall energy output, and reduced toxic gas emissions. Nevertheless, it is important to identify and prepare for the challenges that may limit the full exploitation of the potential for OWT lifetime extension projects. The objective of this paper is to present a detailed PESTLE analysis to evaluate the various political, economic, sociological, technological, legal, and environmental challenges that must be overcome to successfully implement lifetime extension projects in the offshore wind energy sector. We propose a decision framework for extending the lifetime of OWTs, involving the degradation mechanisms and failure modes of components, remaining useful life estimation processes, safety and structural integrity assessments, economic and environmental evaluations, and the selection of lifetime extension technologies among remanufacturing, retrofitting, and reconditioning. Finally, we outline some of the opportunities that lifetime extension can offer for the wind energy industry to foster a more circular and sustainable economy in the future. [ABSTRACT FROM AUTHOR]

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R 2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems. [ABSTRACT FROM AUTHOR]

The power dispatching network forms the backbone of efforts to automate and modernize power grid dispatching, rendering it an indispensable infrastructure element within the power system. However, accurately forecasting future flows remains a formidable challenge due to the network's intricate nature, variability, and extended periods of missing data resulting from equipment maintenance and anomalies. Vital to enhancing prediction precision is the interpolation of missing values aligned with the data distribution across other time points, facilitating the effective capture of nonlinear patterns within historical flow sequences. To address this, we propose a transfer learning approach leveraging the gated recurrent unit (GRU) for interpolating missing values within the power dispatching network's flow sequence. Subsequently, we decompose the generation of future flow predictions into two stages: first, extracting historical features using the GRU, and then generating robust predictions via eXtreme Gradient Boosting (XGBoost). This integrated process termed the GRU-XGBoost module, is applied in experiments on four flow sequences obtained from a power grid company in southern China. Our experimental findings illustrate that the proposed flow prediction model outperforms both machine learning and neural network models, underscoring its superiority in short-term flow prediction for power-dispatching networks. [ABSTRACT FROM AUTHOR]

This document discusses the intersection of sustainability and digitalization in manufacturing companies, highlighting the challenges they face in addressing environmental and social impacts. The concept of Maintenance 4.0 is introduced as a way to enhance and advance maintenance processes using technologies such as the Industrial Internet of Things (IIoT), Augmented Reality (AR), Virtual Reality (VR), Big Data Analytics (BDA), and AI. The document also provides an overview of 13 articles included in a special issue on Maintenance 4.0 and sustainability, covering topics such as predictive maintenance, augmented reality, digital twins, and environmental considerations in maintenance activities. The authors emphasize the importance of Industry 4.0 technologies in transforming maintenance processes and supporting sustainable development goals. [Extracted from the article]

Proton exchange membrane fuel cells (PEMFCs) offer high energy conversion rate and low pollution, while low durability of PEMFCs hinder the further development. Accurately predicting degradation trends and remaining useful life (RUL) are crucial to timely detecting problem and improving PEMFCs durability. In this paper, an enhanced transfer learning (Add-TL) based on adder network (AdderNet) and deep domain confusion is proposed for the prediction of PEMFCs. To reinforce the ability of extracting key features from extensive data, AdderNet with attention mechanism is applied. Furthermore, the fused deep domain confusion employs existing data to achieve higher prediction accuracy. The experimental results demonstrate that Add-TL outperforms other classical methods in predicting degradation trend, the optimal adjusted R-square (R2) values for the two datasets are 0.99879 and 0.99906, respectively. When Add-TL is applied to RUL prediction, the accuracy scores for two datasets reach 0.995, which further validating the applicability of Add-TL in PEMFCs prediction. • AdderNet applied for the first time to predict degradation trends and RUL of PEMFCs. • The method proposed in this paper integrates deep learning and transfer learning. • The incorporation of transfer learning the connections between data, aiming to improve the accuracy of RUL prediction. • Add-TL predicted static and dynamic conditions, providing a comprehensive validation. • Add-TL achieves best performance when training on datasets with varying lengths. [ABSTRACT FROM AUTHOR]

Railway signaling electromechanical devices (RSEDs) play a pivotal role in the railway industry. Normal wear and tear of these devices occur during day-and-night operation and even progressively develop into failures. Hence, it is imperative to predict the remaining useful life (RUL) for reliable services. However, there are three existing challenges in addressing the issue. To overcome these challenges, we introduce M 2 BIST-SPNet for RSEDs RUL prediction. The model incorporates advanced spatiotemporal feature extraction mechanisms, including a spatial-temporal attention-based convolution networks (STACN), multiple branches with multi-scale multifrequency module (MBM), and communication module (CM). Extensive experiments conducted on three typical object datasets (i.e., railway safety relay, EMU contactor, switch circuit controller) shows that the proposed approach has demonstrated state-of-the-art performance in long-term multiparameter prediction and RUL prediction. [ABSTRACT FROM AUTHOR]

The degradation state of a bearing can be monitored effectively by using a wear indicator (WI). A WI curve having smoothness and monotonicity can lay a good foundation for predicting the remaining useful life of the bearing. Most traditional models for bearing WI construction, such as time–frequency indicators and signal decomposition, are complicated; for example, some WI construction models need several models to select from, and their fusion depends on the manual experience of engineers. For example, single and mixed traditional time–frequency indicators, such as the root mean square (RMS), Kurtosis, multiple time–frequency domain fusion. However, the mentioned-above time–frequency domain indicators are difficult to adaptively reflect the operating status of the equipment when the operating conditions of the mechanical system change. Some signal decomposition models are combined with other models and rely on manual experience to extract WI, such as the selection of effective intrinsic mode function components function, parameter setting of empirical mode decomposition and ensemble empirical mode decomposition model, etc. Deep learning models, such as stacked auto encoder (SAE) and convolution neural network, have been widely used in bearing health monitoring and WI construction, because its powerful learning and feature extraction capabilities of multiple hidden layer structures. But these deep learning models are designed to use output labels. Particularly when the data volume is large, it requires manpower, material resources, and experienced engineers to label the data, or it is difficult to label and distinguish the categories of data samples. Therefore, to solve these problems and eliminate the need for manual labor, such as labeling data and selecting models for fusion, we propose using SAE without an output label layer to extract WI from original signals directly. However, an extracted WI curve without good monotonicity (Mon) will result in a poor remaining useful life prediction accuracy. To improve the monotonicity of the extracted WI and reduce the complexity of the WI construction model, we propose an unsupervised enhanced SAE without an output layer, named SINSAE, by adding a sine function of an average value which is calculated form start time to current at each hidden layer to eliminate concussion. Moreover, to demonstrate that our proposed model is better than other models, such as the RMS, Kurtosis, multiple time–frequency domain fusion, SAE, SAE without an output layer, and signal decomposition models, the Mon indicator in this study is used to compare the monotonicity of the extracted WI. Lastly, the results of our experiments using different bearing datasets and various working conditions show that the smoothness and monotonicity of the WI curve extracted by the SINSAE is better than that of other models. Moreover, compared to the traditional commonly used single and multiple time–frequency domain indicators, supervised deep learning and basic unsupervised deep models, the unsupervised SINSAE model can increase the Mon indicators from [0.1, 0.8], [0.02, 0.1], [0.1, 0.8] to above 0.9, respectively. [ABSTRACT FROM AUTHOR]

The remaining useful life prediction of Industrial robot RV reducer is challenging due to the strong redundancy, unstable degradation initiation point, and environmental interference that may obscure the key state information during long-term operation. To address this problem, this paper proposes a novel remaining useful life prediction method for robot RV reducer with multi-depth network and multi-kernel support vector data description. Firstly, the degradation features are constructed by the hidden layer node of deep belief network to reduce the interference and redundancy. Secondly, the first predicting time node is determined by multi-kernel support vector data description to locate the stable degradation initiation node. Then, the temporal convolutional network is applied to predict the remaining useful life in the degradation stage, which improves the prediction accuracy. Finally, the effectiveness of the proposed method is verified by the accelerated fatigue experiment of a self-made robot. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction of rolling bearings is one of the intricate and important issues for equipment intelligent maintenance and health management. Various machine learning models and methods have been applied to rolling bearing RUL prediction. However, a single model cannot effectively extract state information and obtain accurate prediction results, and its generalisation is not stable under the condition of small sample data. Therefore this paper proposes an intelligent hybrid deep learning model for achieving accurate RUL prediction of rolling bearings. Firstly, the one-dimensional vibration signal is transformed into the corresponding two-dimensional time-frequency diagram via Continuous Wavelet Transform (CWT). Secondly, the diagram is input into a Multilayer Perceptron (MLP) consisting of a basic three-layer feed-forward network to obtain a one-dimensional feature vector. And lastly, the obtained feature vector is input into an integrated model based on Deep Autoregressive and Transformer to produce the probability distribution and obtain the prediction results of rolling bearing RUL. Extensive experiments on two rolling bearing datasets show that the proposed model outperforms six other comparative models in extracting bearing fault features and predicting bearing RUL, which demonstrates that the proposed model can effectively extract bearing fault features and accurately predict bearing remaining useful life. [ABSTRACT FROM AUTHOR]

For lithium-ion batteries and supercapacitors in hybrid power storage facilities, both steady degradation and random shock contribute to their failure. To this end, in this paper, we propose to introduce the degradation-threshold-shock (DTS) model for their remaining useful life (RUL) prediction. Non-homogeneous compound Poisson process (NHCP) is proposed to simulate the shock effect in the DTS model. Considering the long-range dependence and heavy-tailed characteristics of the degradation process, fractional Weibull process (fWp) is employed in the diffusion term of the stochastic degradation model. Furthermore, the drift and diffusion coefficients are constantly updated to describe the environmental interference. Prior to the model training, steady degradation and shock data must be separated, based on the three-sigma principle. Degradation data for the lithium-ion batteries (LIBs) and ultracapacitors are employed for model verification under different operation protocols in the power system. Recent deep learning models and stochastic process-based methods are utilized for model comparison, and the proposed model shows higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is widely applied in prognostic and health management (PHM) of turbofan engines. Although some of the existing deep learning-based models for RUL prediction of turbofan engines have achieved satisfactory results, there are still some challenges. For example, the spatial features and importance differences hidden in the raw monitoring data are not sufficiently addressed or highlighted. In this paper, a novel multi-head self-Attention fully convolutional network (MSA-FCN) is proposed for predicting the RUL of turbofan engines. MSA-FCN combines a fully convolutional network and multi-head structure, focusing on the degradation correlation among various components of the engine and extracting spatially characteristic degradation representations. Furthermore, by introducing dual multi-head self-attention modules, MSA-FCN can capture the differential contributions of sensor data and extracted degradation representations to RUL prediction, emphasizing key data and representations. The experimental results on the C-MAPSS dataset demonstrate that, under various operating conditions and failure modes, MSA-FCN can effectively predict the RUL of turbofan engines. Compared with 11 mainstream deep neural networks, MSA-FCN achieves competitive advantages in terms of both accuracy and timeliness for RUL prediction, delivering more accurate and reliable forecasts. [ABSTRACT FROM AUTHOR]

Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems. Inaccurate predictions can lead to premature tool replacements or excessive usage, resulting in resource wastage and potential equipment failures. This study introduces a novel tool RUL prediction method that integrates the enhanced northern goshawk optimization (MSANGO) algorithm with a bidirectional long short-term memory (BiLSTM) network. Initially, key statistical features are extracted from collected signal data using multivariate variational mode decomposition. This is followed by effective feature reduction, facilitated by the uniform information coefficient and Mann–Kendall trend tests. The RUL predictions are subsequently refined through a BiLSTM network, with the MSANGO algorithm optimizing the network parameters. Comparative evaluations with BiLSTM, BiGRU, and NGO-BiLSTM models, as well as tests on real-world datasets, demonstrate this method's superior accuracy and generalizability in RUL prediction, enhancing the efficacy of tool management systems. [ABSTRACT FROM AUTHOR]

Bearing is the key component to determine the health of machinery, and it is of great significance to monitor its working status in real time and predict its remaining useful life. In recent years, the RUL prediction method based on deep learning has been widely used and achieved good prediction results. Here, a bearing life prediction method based on convolution neural network (CNN), long short term memory (LSTM) and attention mechanism (AM) is proposed. First of all, the time domain and frequency domain features of the original vibration signals of rolling bearings are extracted, and the extracted feature set is normalized as the input of CNN. The main function of CNN is to extract spatial features and reduce the dimension of the data. Then, using LSTM to extract the information that may be ignored by CNN, the feature information extracted by CNN-LSTM is input to the attention mechanism for weighting, and the key information is screened. And then more accurately represent the degradation characteristics of the equipment, and finally get the bearing remaining life. The performance of the model is verified by two sets of public data sets, and the experimental results show that it is compared with the CNN-LSTM method. The root mean square error (RMSE) index based on CNN-LSTM-AM method is reduced by 14.6 % and 13.8 % respectively, and the score index is increased by 2.0 % and 2.4 % respectively. The results show that the proposed method has higher accuracy in bearing RUL prediction. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of energy storage batteries plays a significant role in ensuring the safe and reliable operation of battery energy storage systems. This paper proposes an RUL prediction framework for energy storage batteries based on INGO-BiLSTM-TPA. First, the battery's indirect health index is extracted by combining principal component analysis and the Pearson correlation coefficient in the battery charge/discharge cycle data. Second, for the problem that the Northern Goshawk Optimization (NGO) algorithm is prone to falling into local optimum, the Gaussian variation mechanism and nonlinear hunting radius are introduced to improve the NGO algorithm, and the Improved Northern Goshawk Optimization (INGO) algorithm is proposed. Finally, the temporal pattern attention (TPA) mechanism is introduced in the bi-directional long short-term memory (BiLSTM), which makes the model weighted to focus on the features of important time steps, and the INGO algorithm is applied to it to build the RUL prediction framework. Based on the CALCE battery dataset, the root-mean-square error (RMSE) of RUL prediction based on the proposed framework is controlled within 1.3%, which provides better prediction accuracy and generalization. [ABSTRACT FROM AUTHOR]

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data‐driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies. [ABSTRACT FROM AUTHOR]

This paper proposed a prediction algorithm for the degraded actuator taking into account the impact of estimation error of hidden index in the closed-loop system. To this end, a unified prediction framework is established to evaluate the hidden degradation information and recursively update the degradation model parameters simultaneously. The advantage is that the prediction framework can comprehensively compensate the estimation error of hidden degradation index caused by system uncertainty. To jointly estimate the degradation information in avoidance of the impact of system uncertainty, a modified adaptive Kalman filter is designed, and the proof of stability is provided. With the priori estimate from the filter, the degradation model parameters are updated by the inverse filtering probability based on Bayes' theorem. It is followed by the computation of the remaining useful life (RUL) prediction utilizing aforementioned hidden degradation information and the latest degradation model. The effectiveness of the proposed RUL prediction algorithm is demonstrated by the degraded actuator in the continuous casting process. • A novel prediction framework is designed for degraded actuator in closed-loop system. • Adaptive degradation model is established by modified adaptive Kalman filter. • The estimation error of hidden degradation index is integrated. • The stability of optimized adaptive Kalman filter is proven. • Recursive prediction algorithm is performed without full life data. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is considered as one of the most promising green energy devices. Although fruitful results can be available for the remaining useful life (RUL) prediction of PEMFC, stochastic uncertainties have never been considered. To tackle this problem, a hybrid method is proposed in this paper. Wiener process with temporal uncertainty and individual uncertainty is adopted to model the degradation of the state of health (SOH), which is then estimated from monitoring voltage with measurement noise using the unscented Kalman filter (UKF), where unknown filtering and model parameters are jointly identified by expectation-maximization (EM) algorithm and Rauch-Tung-Striebel (RTS) smoother. Finally, gated recurrent unit (GRU) network is employed to realize the RUL prediction with the prediction uncertainty quantified by the Bayesian variational inference technology. The proposed method is verified on the experimental data. Results indicate that smaller mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) values can be obtained compared with other methods. When 60% data are used for prediction, the proposed method can achieve a RUL prediction accuracy with 1.63% and 2.17% relative errors under static and dynamic conditions, respectively, which illustrates the feasibility and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

There are developed methods for high-pressure turbine (HPT) blade loads and remaining useful life (RUL) prediction; however, they are ineffective and time-consuming for in-service HPT blades under actual operating conditions. Hence, it is necessary to use an acceptable computational effort to predict the HPT blade's load and in-service lifetime. Drawing from the idea of the usage-based life (UBL) prediction method, this paper first proposes a framework for the life digital twin (LDT) to characterize and track the in-service life consumption of the HPT blades under actual operating conditions. The second work mainly focuses on developing the steady-state and transient load calculation surrogate models for the HPT blade's LDT. Using the developed surrogate models, it can now calculate the steady-state and transient loads of the HPT blade in an acceptable time with the necessary accuracy. The proposed approach is demonstrated on an HPT blade of a typical commercial turbofan engine. Because the operating load of the HPT blade severely affects its in-service lifetime, the application of this approach enables the construction of an LDT of the HPT blade. It can reduce the uncertainty and variability associated with the in-service life prediction of the HPT blade under actual operating conditions. [ABSTRACT FROM AUTHOR]

A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. A solution based on a novel recurrent version of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach. [ABSTRACT FROM AUTHOR]

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing. [ABSTRACT FROM AUTHOR]

This article provides an overview of important considerations in the modeling, operation, management, and diagnosis of lithium batteries. It emphasizes the significance of accurate modeling for battery simulation, optimization control, and life management. The article also highlights the need for reliable operation and management, including effective heat dissipation and equilibrium strategies for battery state of charge. Additionally, it discusses the importance of accurate diagnosis for improving reliability and safety. The article concludes by suggesting prospects for future research in these areas, including modeling analysis, parameter identification techniques, optimization strategies, data-driven methods, and further studies on internal mechanisms. The research was not funded and the authors have no conflicts of interest. [Extracted from the article]

This document is a special issue of the journal "Processes" focused on micro/nano manufacturing processes and optimization techniques. The articles in this issue cover a range of topics including the fabrication of micro/nano-structured surfaces, simulation and experimental investigation of laser manufacturing, and optimization techniques in microscale machining. The articles provide insights into various methods and technologies used in these areas and discuss their advantages, disadvantages, and potential applications. The authors also propose new approaches and techniques to address challenges and improve performance in micro/nano manufacturing processes. [Extracted from the article]

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This study presents methods to handle deep learning regressions with input and output sequences of different lengths. We discuss the Autoregressive one-step prediction framework and introduce an innovative one-time multi-step (OTMS) prediction approach, based on a custom loss function, that predicts all future steps in a single shot. The presented methodologies are then applied to simultaneously predict the State of Health (SoH) trajectory and estimate the Remaining Useful Life (RUL) of lithium-ion battery cells. Accurate estimates of SoH trajectory and RUL are essential for Battery Management Systems (BMSs), electronic systems that guarantee safety while maximizing performance and extending battery lifespan. In this context, the studied methodologies were compared using a rigorous cross-validation approach. The OTMS model showed better predictions in early cycles, while the Autoregressive model performed better in later cycles, suggesting a hybrid approach between these two methodologies as an optimal solution. [ABSTRACT FROM AUTHOR]

Wave overtopping occurring in offshore wind renewable energy structures such as tension leg platforms (TLPs) or semi-submersible platforms is a phenomenon that is worth studying and preventing in order to extend the remaining useful life of the corresponding facilities. The behaviour of this phenomenon has been extensively reported for linear coastal defences like seawalls. However, no referenced study has treated the case of cylindrical structures typical of these applications to a similar extent. The aim of the present study is to define an empirical expression that portrays the relative overtopping rate over a vertical cylinder including a variety of bull-nose type mitigation structures to reduce the overtopping rate in the same fashion as for the linear structures characteristic of shoreline defences. Hydrodynamic interaction was studied by means of an experimentally validated numerical model applied to a non-impulsive regular wave regime and the results were compared with the case of a plain cylinder to evaluate the expected improvement in the overtopping performance. Four different types of parapets were added to the crest of the base cylinder, with different parapet height and horizontal extension, to see the influence of the geometry on the mitigation efficiency. Computational results confirmed the effectivity of the proposed solution in the overtopping reduction, though the singularity of each parapet geometry did not lead to an outstanding difference between the analysed options. Consequently, the resulting overtopping decrease in all the proposed geometries could be modelled by a unique specific Weibull-type function of the relative freeboard, which governed the phenomenon, showing a net reduction in comparison with the cylinder without the geometric modifications. In addition, the relationship between the reduced relative overtopping rate and the mean flow thickness over the vertical cylinder crest was studied as an alternative methodology to assess the potential damage caused by overtopping in real structures without complex volumetric measurements. The collection of computational results was fitted to a useful function, allowing for the definition of the overtopping discharge once the mean flow thickness was known. [ABSTRACT FROM AUTHOR]

Lithium-ion battery management requires an accurate determination of the batteries’ remaining usable lives. A novel hybrid method used for the RUL and short-term capacity prediction of batteries is presented in this manuscript. The proposed technique is combined with a White Shark Optimizer (WSO) with the Multi-kernel Support Vector Machine (MSVM); therefore, it is called the WSO-MSVM technique. The major goal of this WSO-MSVM method is to attain effective future capacities and RUL forecast for the Lithium-Ion Battery (LIB) with reliable management of uncertainty. The hybrid technique solves the difficulty of forecasting the RUL of LIB. This proposal trains the WSO-MSVM model using the LIB data. The trained model is utilized to forecast the capacity and RUL of LIBs. In this proposed system, capacity, current, and voltage are considered in the discharge operation. The proposed technique validates good reliability and prediction accuracy with this suitable mechanism. Compared to the existing techniques like artificial neural network (ANN), ant lion optimizer-optimized artificial neural network, and adaptive network-based fuzzy inference system, the proposed technique shows fewer errors under charge and discharge conditions. In addition, the uncertainty intervals of the proposed WSO-MSVM technique for the predicted RUL may include the actual remaining life of the battery. The proposed technique’s charging error is lower the other exciting methods. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) has gotten considerable attention in the background of Industry 4.0. Battery PHM contributes to the reliable and safe operation of electric devices. Nevertheless, relevant reviews are still continuously updated over time. In this paper, we browsed extensive literature related to battery PHM from 2018 to 2023 and summarized advances in battery PHM field, including battery testing and public datasets, fault diagnosis and prediction methods, health status estimation and health management methods. The last topic includes state of health estimation methods, remaining useful life prediction methods and predictive maintenance methods. Each of these categories is introduced and discussed in details. Based on this survey, we accordingly discuss challenges left to battery PHM, and provide future research opportunities. This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems. [ABSTRACT FROM AUTHOR]

Accurately estimating the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for maintaining the safe and stable operation of rechargeable battery management systems. However, this task is often challenging due to the complex temporal dynamics. Recently, attention-based networks, such as Transformers and Informer, have been the popular architecture in time series forecasting. Despite their effectiveness, these models with abundant parameters necessitate substantial training time to unravel temporal patterns. To tackle these challenges, we propose a straightforward MLP-Mixer-based architecture named "Intra–Inter Patch Mixer" (IIP-Mixer), which leverages the strengths of multilayer perceptron (MLP) models to capture both local and global temporal patterns in time series data. Specifically, it extracts information using an MLP and performs mixing operations along both intra-patch and inter-patch dimensions for battery RUL prediction. The proposed IIP-Mixer comprises parallel dual-head mixer layers: the intra-patch mixing MLP, capturing local temporal patterns in the short-term period, and the inter-patch mixing MLP, capturing global temporal patterns in the long-term period. Notably, to address the varying importance of features in RUL prediction, we introduce a weighted loss function in the MLP-Mixer-based architecture, marking the first time such an approach has been employed. Our experiments demonstrate that IIP-Mixer achieves competitive performance in battery RUL prediction, outperforming other popular time series frameworks, such as Informer and DLinear, with relative reductions in mean absolute error (MAE) of 24% and 10%, respectively. [ABSTRACT FROM AUTHOR]

The degradation of electrical insulating materials has been a subject of interest for decades as they are commonly applied in many fields of electrical engineering. Suitably modeling such a process is important since the known and well-described degradation process reveals the effect of ambient conditions, and this allows us to possibly estimate a material's remaining useful life. However, not many studies are dealing with the effect of the hygrothermal degradation of impregnating mono-component epoxy resins in the context of electrical engineering. Therefore, this study deals with this issue and discusses both the dielectric response (based on the measurement of relative permittivity, dissipation factor, and dielectric strength) and the mechanical response (based on measurements of tensile strength and Shore D hardness) to a hygrothermal degradation experiment. In addition, the results of thermal analyses are presented for the evaluation of the pristine specimen manufacturing process and possible post-curing processes. Furthermore, this study presents several methodologies for modeling the degradation process, including a novel methodology in this area based on Bayesian experimental design. As an outcome, mechanical parameters are proven to be specific in terms of the actual condition of the material and the Bayesian enhanced degradation model seems to be superior to the conventional evaluation methods in this particular study. [ABSTRACT FROM AUTHOR]

Addressing the limitation of traditional deep learning models in capturing the spatio-temporal characteristics of flight data and the constrained prediction accuracy due to sequence length in aero-engine life prediction, this study proposes an aero-engine remaining life prediction approach integrating a kernel slow feature analysis, a Gaussian mixture model, and an improved Autoformer model. Initially, the slow degradation features of gas path performance parameters over time are extracted through kernel slow feature analysis, followed by the establishment of a Gaussian mixture model to create a health state representation using Bayesian inferred distances for quantifying the aero-engine's health status. Moreover, a spatial attention mechanism is introduced alongside the autocorrelation mechanism of the Autoformer model to augment the global feature extraction capacity. Additionally, a multilayer perceptron is employed to further elucidate the degradation trends, which enhances the model's learning and predictive capabilities for extended sequences. Subsequently, experiments are conducted using authentic aero-engine operational data, comparing the proposed method with the standard Autoformer and Transformer models. The results demonstrate that the proposed method outperforms both models in swiftly and accurately predicting the remaining life of aero-engines with robustness and high prediction accuracy. [ABSTRACT FROM AUTHOR]

Sensor degradation poses a critical yet 'often overlooked' challenge in accurately predicting the remaining useful life (RUL) of nuclear reactor pressure vessels (RPVs), hindering safe and efficient plant operation. This paper introduces an approach to RUL estimation that explicitly addresses sensor degradation, a significant departure from conventional methods. We model neutron embrittlement, a dominant degradation process in RPV steel, as a Wiener process and leverage real-world surveillance capsule data for insightful parameterization. Maximum likelihood estimation is utilized to characterize the degradation dynamics in the model. A Kalman filter then seamlessly integrates the degradation model with sensor measurements, effectively compensating for degradation-induced errors and providing refined state estimates. These estimates power a robust RUL prediction framework. Our results expose the profound impact of sensor degradation on conventional RUL predictions. By directly confronting sensor degradation, our method yields substantially more accurate and reliable RUL estimates. This work marks a significant advancement in the field of materials degradation, offering a powerful tool to optimize nuclear power plant safety and longevity. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy. [ABSTRACT FROM AUTHOR]

Gear fault detection and remaining useful life estimation are important tasks for monitoring the health of rotating machinery. In this study, a new benchmark for endurance gear vibration signals is presented and made publicly available. The new dataset was used in the HUMS 2023 conference data challenge to test anomaly detection algorithms. A survey of the suggested techniques is provided, demonstrating that traditional signal processing techniques interestingly outperform deep learning algorithms in this case. Of the 11 participating groups, only those that used traditional approaches achieved good results on most of the channels. Additionally, we introduce a signal processing anomaly detection algorithm and meticulously compare it to a standard deep learning anomaly detection algorithm using data from the HUMS 2023 challenge and simulated signals. The signal processing algorithm surpasses the deep learning algorithm on all tested channels and also on simulated data where there is an abundance of training data. Finally, we present a new digital twin that enables the estimation of the remaining useful life of the tested gear from the HUMS 2023 challenge. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty. [ABSTRACT FROM AUTHOR]

Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a transformer encoder and customized gate control (TECGC) is proposed for simultaneous prediction of tool RUL and tool wear stages. Specifically, the transformer encoder is employed as the backbone of the TECGC model for extracting shared features from the original data. The customized gate control (CGC) is utilized to extract task-specific features relevant to tool RUL prediction and tool wear stage and shared features. Finally, by integrating these components, the tool RUL and the tool wear stage can be predicted simultaneously by the TECGC model. In addition, a dynamic adaptive multi-task learning loss function is proposed for the model's training to enhance its calculation efficiency. This approach avoids unsatisfactory prediction performance of the model caused by unreasonable selection of trade-off parameters of the loss function. The effectiveness of the TECGC model is evaluated using the PHM2010 dataset. The results demonstrate its capability to accurately predict tool RUL and tool wear stages. [ABSTRACT FROM AUTHOR]