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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]

This paper provides a framework based on machine learning approach in I4.0 environment to predict the remaining useful life of an aviation engine. For illustration purpose, an industrial case study is presented which applies machine learning algorithms to analyze the data collected using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) simulation which includes run-to-degradation data for a turbofan engine. The results obtained from the study validate the proposed framework to identify prominent features and perform sequential analysis on unstructured data for predicting the remaining useful life of an aviation engine. Six machine learning models are applied to the dataset containing four subsets: FD001, FD002, FD003 and FD004 in C-MAPSS dataset each working on different degradation conditions for turbofan engine. For FD001, random forest had the lowest RMSE (11.59), and for FD002, FD003 and FD004, the lowest RMSE was given by LGBM classifier (12.78, 7.95 and 11.04), respectively. From the findings, it is observed that LGBM performs better with higher AUC 89% and lowest RMSE. The proposed framework can be applied to a wide range of failure prediction applications. Regardless of the underlying physics, ML-based data-driven methodologies can be used to analyze a wide range of systems. [ABSTRACT FROM AUTHOR]

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided. [ABSTRACT FROM AUTHOR]

Hydrogen fuel has shown promise as a clean, alternative fuel aiding in the reduction of fossil fuel dependence within the transportation sector. However, hydrogen refueling stations and infrastructure remains a barrier and are a prerequisite for consumer adoption of low-cost and low-emission fuel cell electric vehicles (FCEVs). The costs for FCEV fueling include both station capital costs and operation and maintenance (O&M) costs. Contributing to these O&M costs, unscheduled maintenance is presently more costly and more frequent than for similar gasoline fueling infrastructure and is asserted to be a limiting factor in achieving FCEV customer acceptance and cost parity. Unscheduled maintenance leads to longer station downtime, therefore, causing an increase in missed fueling opportunities, which forces customers to seek refueling at other operable stations that may be significantly farther away. This research proposes a framework for a hydrogen station prognostics health monitoring (H2S PHM) model that can minimize unexpected downtime by predicting the remaining useful life for primary hydrogen station components within the major station subsystems. The H2S PHM model is a data-driven statistical model, based on O&M data collected from 34 retail hydrogen stations located in the U.S. The primary subcomponents studied are the dispenser, compressor, and chiller. The remaining useful life calculations are used to decide whether or not maintenance should be completed based on the prediction and expected future station use. This paper presents the background, method, and results for the H2S PHM model as for a means for improving station availability and customer confidence in FCEVs and hydrogen infrastructure. • Hydrogen station reliability is a key contributor to market success. • Mean fills between failure and station availability are lower than conventional technologies. • A prognostics health monitoring system can improve hydrogen station availability. • Estimated remaining useful life of primary components assists in maintenance decisions. [ABSTRACT FROM AUTHOR]

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access , 2019, 7 , pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications. [ABSTRACT FROM AUTHOR]

Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

As most of the safety critical industrial systems remain sensitive to functional degradation and operate under closed loop, it becomes imperative to take into account the state of health within the control design process. To that end, an effective assessment and extension of the Remaining Useful Life (RUL) of complex systems is a standing challenge that seeks novel solutions at the cross-over of Prognostics and Health Management (PHM) domain as well as automatic control. This paper considers a dynamical system subjected to functional degradation presents a novel control design strategy. Wherein the assessment of state of health of the system is taken into account leading to effective prediction of the RUL as well as its extension. To that end, the degradation model is considered unknown but input-dependent. The control design is formulated as an optimization problem wherein a suitable comprise is reached between the performance and desired RUL of the system. The main contribution of the paper remains in proposal of set-point modulation based approach wherein the control input at a given present time stage is modulated in such way that futuristic health of the system over a long time horizon is extended whilst assuring acceptable performance. The effectiveness of the proposed strategy is assessed in simulation using a numerical example as well as liquid propellant rocket engine case.

Machinery degradation assessment can offer meaningful prognosis and health management information. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain discriminator to extract invariant features from different machine operating conditions. Case studies were carried out using the FEMTO-ST bearing dataset, and the Monte Carlo method was employed for RUL prediction during the degradation process. When compared to other established techniques such as the RNN-based RUL prediction method, convolutional LSTM network, Bi-directional LSTM network with attention mechanism, and the traditional RUL prediction method based on vibration frequency anomaly detection and survival time ratio, our proposed TSTN method demonstrates superior RUL prediction accuracy with a notable SCORE of 0.4017. These results underscore the significant advantages and potential of the TSTN approach over other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• Physics-Constrained Bayesian Neural Network. • Failure prognostics of lithium-ion batteries. • Comparative study of physics-based, data-driven, and physics-constrained models. In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity degradation model may be developed using either simplified physical laws or machine learning-based methods. It is observed that even though degradation models based on simplified physical laws are easy to implement, they may result in large error in the application of failure prognostics. While data-driven prognostics models can provide more accurate degradation forecasting, they may require a large volume of training data and may invoke predictions inconsistent with physical laws. It is also very challenging for existing methods to predict the RUL at the early stages of battery life. In this paper, we propose a Bayesian physics-constrained neural network for battery RUL prediction by overcoming limitations of the current methods. In the proposed method, a neural differential operator is learned from the first 100 cycles of data. The neural differential operator is modeled with a Bayesian neural network architecture that separates the fixed history dependence from the time dependence to isolate epistemic uncertainty quantification. Using the battery dataset presented in the paper by Severson et al. as an example, we compare our proposed method with a simplified physics-based degradation forecasting model and two data-driven prognostics models. The results show that the proposed physics-constrained neural network can provide more accurate RUL estimation than the other methods with the same group of training data. Most importantly, the proposed method allows for RUL prediction at earlier stages of the battery life cycle. [ABSTRACT FROM AUTHOR]

Estimating the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is key to predicting and increasing their durability. This paper investigates six deep learning techniques – long short-term memory (LSTM) networks, gated recurrent units (GRU), 1-D convolutional neural network (CNN)-LSTMs, 1D–CNN–GRUs, 1D–CNN–Bidirectional-LSTMs, and 1D–CNN–Bidirectional-GRUs – for RUL and long-term degradation trend prediction for a five-cell PEMFC stack operated with and without current ripples. The hyperparameters of all models are optimized for minimum root-mean-square error (RMSE) of the stack voltage using tree-structured Parzen estimators (TPE) with hyperband pruning. The obtained prediction results are benchmarked using the coefficient of determination (R2), RMSE, and mean absolute percentage error (MAPE) in stack voltage predictions and relative error (RE) in RUL estimates. The comparative analysis for models trained on 50% datasets reveals that the best R2, RMSE, MAPE, and RE of 98.87E-2, 1.258E-3, 0.285E-1, and 0.0425E-1 are offered by the LSTM model for the stack without current ripples. Further, for the stack with current ripples, the best R2, RMSE, MAPE, and RE of 99.482E-2, 1.77E-3, 0.444E-1, and 0.089E-1 are exhibited by the 1D–CNN–Bidirectional-GRU model. The LSTM model also reveals excellent transfer learning performance on the stack with current ripples with the best R2, RMSE, MAPE, and RE of 98.91E-2, 2.455E-3, 0.362E-1, and 0.044E-1, respectively. [Display omitted] • Six deep learning models are developed for PEMFC RUL and degradation prediction. • Hyperparameters of each model are optimized using TPE for static and dynamic loading. • Static loading models exhibited excellent transfer learning performance. • All tuned models performed superior to recent reported approaches in RMSE. • LSTM and 1D–CNN–Bidirectional-GRU demonstrated best results trained on 50% data. [ABSTRACT FROM AUTHOR]

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is critical for industrial equipment status detection, and the accurate prediction results provide decision-makers with actionable information. Preventive maintenance can be carried out to prevent the sudden failure of the equipment effectively based on the predicted results. However, as Industry 4.0 technologies develop, the amount of data collected by sensors is also rapidly increasing. The existing RUL prediction methods are gradually unable to cope with complex industrial equipment data, and deep learning methods gradually come to the fore. In this background, this paper proposes a transformer-based model with a multi-layer encoder–decoder structure to extract domain-invariant features. The decoder in the traditional transformer structure only obtains a single piece of information from the last layer of the encoder, and this paper uses an integrated layer-cross decoding strategy to compensate. Based on the encoder–decoder cross-connection, each decoder layer is provided with global view information from the final encoder layer simultaneously, improving the model's performance. The validity and superiorities of the proposed method are evaluated through several experiments on the publicly available C-MAPSS dataset provided by NASA. As can be seen from the results, the proposed method gets higher prediction accuracy than other network architectures and state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Remaining useful life is one of the key indicators for mechanical equipment health and condition-based maintenance requirements. In fact, the field of prognostics and health management is heavily reliant on remaining useful life estimation. The availability of industrial big data has enabled promising research efforts in prognostics and health management. Deep learning techniques have been widely adopted, and proven to be successful in big data prognostics applications. However, deep learning approaches are considered black box approaches with interpretation difficulties and loss of information due to high-level feature extraction resulting from layer-by-layer processing. Enriching the deep learning input with temporal features can increase the performance of deep learning based approaches. This paper aims to improve the performance of deep learning techniques by incorporating dynamic mode decomposition into the deep learning schemes for the purposes of remaining useful life estimation. The developed method is capable of accurately predicting the remaining useful life in a data driven manner without prior knowledge of system equations. The input temporal information and health state are enriched by using dynamic mode decomposition which produces dynamic modes that approximate the infinite Koopman operator modes. The modes contain coherent time dynamics of the processed system which contribute to producing a health indicator that is representative of the system degradation. These time dependent dynamics are important characteristics of the system's health state. The degradation profile is incorporated into deep learning schemes that accurately predict the remaining useful life of the system. To validate the proposed model, two different experimental data repositories are used in this paper. The first one is a spiral bevel gear vibration dataset. The second one consists of turbofan engines vibration datasets. The validation results have shown improved remaining useful life estimation performance when dynamic mode decomposition technique is incorporated into the deep learning schemes presented in this paper. [ABSTRACT FROM AUTHOR]

In recent years, fuel cells have attracted more and more attention in the energy field. However, accurate evaluation of durability is one of the key aspects of large-scale commercial application of fuel cells. The data-driven method has good fitting performance for predicting remaining useful life (RUL) at complex and nonlinear systems. In this paper, Cycle Reservoir with Jump with multi-inputs and multi-outputs (MIMO-CRJ) is proposed to further improve the fitting speed and reduce the complexity of the system which improved the structure of echo state network (ESN). Different from the previous method that only uses voltage to predict the voltage, this paper considers stack current, temperature and pressure, reactant flow rate in fuel cell operation as input variables, and extracts more degradation information to predict the voltage trend. Experimental results show that MIMO-CRJ has better performance than Cycle Reservoir with Jump with single -inputs and single-output (SISO-CRJ) and ESN networks under both static and quasi-dynamic current conditions. • The multi-input Cycle Reservoir with Jump network is proposed for prognostics. • The proposed model can be used for remaining useful life on the static current and dynamic current experiments. • The MIMO-CRJ is used for short-term prediction and long-term prediction of PEMFC. [ABSTRACT FROM AUTHOR]

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones. [ABSTRACT FROM AUTHOR]

A robust recursive zonotopic set-membership approach for remaining useful life forecasting with application to linear parameter-varying systems is proposed in this paper. The proposed approach addresses systems with degraded components formulated as a system-level prognostics problem. Thus, the critical degraded components of the system are augmented to the states resulting a nonlinear system that is reformulated as a linear parameter-varying model. Hence, joint estimation of states and parameters is adopted in a zonotopic set-membership scheme with an optimal linear matrix inequality-based tuning and assuming unknown-but-bounded noises and uncertainties. As a result, a recursive zonotopic set-membership approach is proposed for remaining useful life forecasting based on the prediction of the failure precursors of degraded systems. Finally, this approach is tested on a DC–DC converter case study with unknown degradation behaviors, and the obtained results show the estimation and the forecasting accuracy of this methodology. • Zonotopic set-membership for prognostics applications. • Joint estimation of states and parameters for degraded systems. • Bounded degradation prediction and remaining useful life forecasting. • LMI-based optimization for robust zonotopic observer tuning for LPV systems. [ABSTRACT FROM AUTHOR]

Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the degradation process using regression techniques that approximate a mapping function from input to continuous remaining useful life estimates. These models are typically of high complexity and low interpretability. Classification approaches are an alternative solution to these types of models. We propose a predictive classification model that translates the input into discrete output variables instead of mapping the input to a single remaining useful life estimate. Each discrete output variable corresponds to a range of remaining useful life values. In other words, each output class variable represents the likelihood or risk of failure within a specific time range. We apply this model to a real-world case study involving the unscheduled and scheduled removals of a set of engine bleed valves from a fleet of Boeing 737 aircraft. The model can reach an area under the (micro-average) receiver operating characteristic curve of 72%. Our results suggest that the proposed multiclass gated recurrent unit network can provide valuable information about the different fault stages (corresponding to intervals of residual lives) of the studied valves. [ABSTRACT FROM AUTHOR]

• A prognosis framework of reaction wheel motor in space satellites is proposed. • Real motor degradation data of 3 years accelerated-life tests are utilized. • System-level prognostics is employed to account the effects of component degradation. • Failure threshold is imposed on the system according to the design requirement. The reaction wheels actuated by motors are widely used for advanced attitude control of satellites. During the satellite operation, the performance of reaction wheel motor degrades and results in unexpected failures. To guarantee the reliability and safety of satellites, it is important to predict its remaining useful life while it is in operation. To address this issue, this study presents a system-level prognostics approach for the reaction wheel motor, by regarding it as a system composed of multiple components. The approach is demonstrated by using the motor operation data obtained during the accelerated-life tests on ground for 3 years. Health degradation of each components of the motor are estimated using the adaptive extended Kalman filter. Failure threshold of the motor performance is established by the design requirement on characteristic curve. The anomaly detection and failure prediction are performed using the shifting kernel particle filter. [ABSTRACT FROM AUTHOR]

Recently, the estimation of remaining useful life (RUL) for two-phase nonlinear degrading devices has shown rising momentum for ensuring their safe and reliable operation. The degradation processes of such systems are influenced by the temporal variability, unit-to-unit variability, and measurement variability jointly. However, current studies only consider these three sources of variability partially. To this end, this paper presents a two-phase nonlinear degradation model with three-source variability based on the nonlinear Wiener process. Then, the approximate analytical solution of the RUL with three-source variability is derived under the concept of the first passage time (FPT). For better implementation, the offline model parameter estimation is conducted by the maximum likelihood estimation (MLE), and the Bayesian rule in conjunction with the Kalman filtering (KF) algorithm are utilized for the online model updating. Finally, the effectiveness of the proposed approach is validated through a numerical example and a practical case study of the capacitor degradation data. The results show that it is necessary to incorporate three-source variability simultaneously into the RUL prediction of the two-phase nonlinear degrading systems.

Laboratory-scale data on a component level are frequently used for prognostics because acquiring them is time and cost efficient. However, they do not reflect actual field conditions. As prognostics is for an in-service system, the developed prognostic methods must be validated using real operational data obtained from an actual system. Because obtaining real operational data is much more expensive than obtaining test-level data, studies employing field data are scarce. In this study, a prognostic method for screws was presented by employing multi-source real operational data obtained from a micro-extrusion system. The analysis of real operational data is more challenging than that of test-level data because the mutual effect of each component in the system is chaotically reflected in the former. This paper presents a degradation feature extraction method for interpreting complex signals for a real extrusion system based on the physical and mechanical properties of the system as well as operational data. The data were analyzed based on general physical properties and the inferred interpretation was verified using the data. The extracted feature exhibits valid degradation behavior and is used to predict the remaining useful life of the screw in a real extrusion system. [ABSTRACT FROM AUTHOR]

Durability and reliability are the major bottlenecks of the proton-exchange-membrane fuel cell (PEMFC) for large-scale commercial deployment. With the help of prognostic approaches, we can reduce its maintenance cost and maximize its lifetime. This paper proposes a hybrid prognostic method for PEMFCs based on a decomposition forecasting framework. Firstly, the original voltage data is decomposed into the calendar aging part and the reversible aging part based on locally weighted regression (LOESS). Then, we apply an adaptive extended Kalman filter (AEKF) and long short-term memory (LSTM) neural network to predict those two components, respectively. Three-dimensional aging factors are introduced in the physical aging model to capture the overall aging trend better. We utilize the automatic machine-learning method based on the genetic algorithm to train the LSTM model more efficiently and improve prediction accuracy. The aging voltage is derived from the sum of the two predicted voltage components, and we can further realize the remaining useful life estimation. Experimental results show that the proposed hybrid prognostic method can realize an accurate long-term voltage-degradation prediction and outperform the single model-based method or data-based method. [ABSTRACT FROM AUTHOR]

Since their inception, industries have experienced the negative effects of downtime, lost productivity, lost revenue, as well as layoffs. The prediction of an item's remaining useful life (RUL) enables maintenance techniques to avoid costly and serious damage. As a result, a prognostic is now acknowledged as a crucial activity. Thanks to the Internet of Things (IoT) and IT solutions like Computer Aided Maintenance Management (CMMS) software packages, industries today have a vast amount of data gathered from on-site sensors. This offers real-time data on the equipment's state as well as each piece of equipment's history of interventions from the CMMS software database. By utilizing the vast amount of data that has accumulated over the years, we will be able to extract even more crucial information. The use of artificial intelligence (AI) methods can open up new possibilities for CMMS software packages. In this study, we try to predict RUL (Remaining Useful Time) using an artificial intelligence technique called association rules. This strategy is applied to enhance existing CMMS software programs. Experiment is carried out with a well-known dataset provided by the NASA Ames Research Center and the CoE "Center of Excellence". Experiment results indicate that our suggested approach performs well in forecasting the RUL of turbojet engines and that it also significantly improves the outcomes of predictive maintenance. [ABSTRACT FROM AUTHOR]

• A Wiener-process-inspired semi-stochastic filtering approach is developed. • The proposed method is applied to prognostics of stochastic degrading systems. • No resort to negatively correlated assumption is achieved by the developed method. • The parameters in the established model are estimated from measurements. • Advantages of the developed method are validated by two case studies. Semi-stochastic filtering method has been termed as one of the typical methods for predicting remaining useful life (RUL). Despite of its simple and direct framework, selecting or determining the conditional distribution of the condition monitoring (CM) measurements given the RUL is a challenging task remaining to be solved. To address this issue, this paper presents a novel Wiener-process-inspired semi-stochastic filtering approach for prognostics. First, the relationship between the degradation monitoring data modeled by Wiener process and the RUL of the system is explored. Inspired this relation, the conditional distribution of the CM measurements give the RUL is directly established and used as the observation equation in semi-stochastic filtering based prognosis method, and the parameters in Wiener process and semi-stochastic filtering model are estimated by the maximum likelihood estimation method. Then, the RUL of the concerned in-service system can be predicted with a probabilistic distribution based on the constructed state equation and the estimated model parameters using the CM data to date. To do so, the proposed prognosis method has an ability to integrate the historical data and the real-time CM data of the in-service system. Finally, the developed method is validated by the wear data of milling cutters and Lithium-ion button cells. [ABSTRACT FROM AUTHOR]

Electromagnetic Relays (Electromagnetic Relay (EMR)s) are omnipresent in electrical systems, ranging from mass-produced consumer products to highly specialised, safety-critical industrial systems. Our detailed literature review focused on EMR reliability highlighting the methods used to estimate the State of Health or the Remaining Useful Life emphasises the limited analysis and understanding of expressive EMR degradation indicators, as well as accessibility and use of EMR life cycle data sets. Prioritising these open challenges, a deep learning pipeline is presented in a prognostic context termed Electromagnetic Relay Useful Actuation Pipeline (EMRUA). Leveraging the attributes of causal convolution, a Temporal Convolutional Network (TCN) based architecture integrates an arbitrary long sequence of multiple features to produce a remaining useful switching actuations forecast. These features are extracted from raw, high volume life cycle data sets, namely EMR switching data (Contact-Voltage, Contact-Current). Monte-Carlo Dropout is utilised to estimate uncertainty during inference. The TCN hyperparameter space, as well as various methods to select and analyse long sequences of multivariate time series data are investigated. Subsequently, our results demonstrate improvements using the developed statistical feature-set over traditional, time-based features, commonly found in literature. EMRUA achieves an average forecasting mean absolute percentage error of ±12 % over the course of the entire EMR life.

Lithium battery has been widely applied as new energy to cope with pressures in both form environment and energy. The remaining useful life (RUL) prognostics of lithium-ion batteries have become more critical. Convenient battery life prediction allows early detection of performance deficiencies to help maintain the battery system promptly. This paper proposes a RUL prognostics model of lithium-ion batteries based on a coordinate reconfiguration of degradation trajectory and multiple linear regression. First, a new sampling rule is used to reconfigure the coordinates of degradation data of new batteries and truncated similar batteries. Then, the relationship between similar and new lithium-ion batteries is established by using the reconfiguration data. Moreover, a new RUL prognostics model based on a coordinate reconfiguration of degradation trajectory and multiple linear regression is established by considering the influence of time-varying factors, which can improve prediction accuracy with small sample data and significantly reduce product development time and cost.

Lithium-ion is a progressive battery technology that has been used in vastly different electrical systems. Failure of the battery can lead to failure in the entire system where the battery is embedded and cause irreversible damage. To avoid probable damages, research is actively conducted, and data-driven methods are proposed, based on prognostics and health management (PHM) systems. PHM can use multiple time-scale data and stored information from battery capacities over several cycles to determine the battery state of health (SOH) and its remaining useful life (RUL). This results in battery safety, stability, reliability, and longer lifetime. In this paper, we propose different data-driven approaches to battery prognostics that rely on: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Reinforcement Learning (RL) based on the permutation entropy of battery voltage sequences at each cycle, since they take into account vital information from past data and result in high accuracy. [ABSTRACT FROM AUTHOR]

In safety-critical systems such as industrial plants or aircraft, failure occurs inevitably during operation, and it is important to prevent it in order to maintain high availability. To reduce this risk, a lot of efforts are directed from developing sensing technologies to failure prognosis algorithms to enable predictive maintenance. The success of effective and reliable predictive maintenance not only relies on robust prognosis algorithms but also on the selection of sensors or data acquisition strategy. However, there are not many in-depth studies on a trade-off between sensor quality and data storage in the view of prognosis performance. The information about (1) how often data should be measured and (2) how good sensor quality should be for reliable failure prediction can be highly impactful for practitioners. In this paper, the authors evaluate the efficacy of the two factors in terms of remaining useful life (RUL) prediction accuracy and its uncertainty. In addition, since knowing true degradation information is almost impossible in practice, the authors validated the use of the prognosis metric without requiring the true degradation information. A numerical case study is conducted to identify the relationship between sensor quality and data storage. Then, real bearing run-to-failure (RTF) datasets acquired from accelerometer (contact type) and microphone (non-contact type) sensors are evaluated based on the prognosis performance metric and compared in terms of the sensors' cost-effectiveness for predictive maintenance. [ABSTRACT FROM AUTHOR]

In the absence of a high-fidelity physics-based prognostics model, data-driven prognostics methods are widely adopted. In practice, however, data-driven approaches often suffer from insufficient training data, which causes large training uncertainty that hinders the Digital twin (DT)-based decision-making. In such a case, the integration of low-fidelity physics with a data-driven method is highly demanded. This paper introduces physics-informed neural network (PINN)-based prognostics that can utilize low-fidelity physics information, such as monotonicity or the sign of curvature. Low-fidelity physics information is included as a constraint during the optimization process to reduce the training uncertainty in the neural network model by preventing unrealistic predictions. The proposed method is applied to two case studies to demonstrate the effect of reducing the prediction uncertainty and the robustness to the variability in test data. The two case studies show that PINN-based prognostics can successfully reduce the prediction uncertainty and yield more robust prognostics performance than the ordinary neural network. [ABSTRACT FROM AUTHOR]

Bearing is one of the critical components in rotating equipment. Therefore, accurate estimation of the remaining useful life (RUL) of bearings plays a vital role in reducing the costly unplanned maintenance and increasing the reliability of machines. This paper proposes a method for bearing prognostics that uses iteratively updated degradation regression models to capture the degradation trend in bearing's health indicator (HI), and the models are utilized to predict the degradation trajectory of HI and to estimate the RUL of bearings. The importance of determining the time to start prediction by elbow point is explained, which is often overlooked in prognostics. To improve the prognostic performance, an adaptive approach for elbow point detection is designed based on the gradient change of HIs, and a new smooth approach is applied to reduce spurious fluctuations in degradation trajectory. The effectiveness of the proposed method is validated on two publicly available data sets, i.e., IMS and FEMTO bearing prognostics data set, and its prognostic performance is compared with that of three state-of-the-art methods. The obtained results demonstrate that the proposed method can effectively detect elbow point and determine the time to start prediction, and can calibrate the degradation regression model dynamically according to the evolving degradation trend in the HI, which validates its superior prognostic performance. • A remaining useful life prediction method using the dynamic degradation regression models is proposed. • An adaptive approach for elbow point detection based gradient change of health indicator is proposed. • The proposed method is tested and validated on IMS and FEMTO bearing data sets, respectively. • The proposed algorithm shows superiority comparing to other art-of-the-state methods. [ABSTRACT FROM AUTHOR]

We propose an instance-based learning approach with data augmentation and similarity evaluation to estimate the remaining useful life (RUL) of a mechanical component for health management. The publicly available PRONOSTIA datasets, which provide accelerated degradation test data for bearings, are used in our study. The challenges with the datasets include a very limited number of run-to-failure examples, no failure mode information, and a wide range of bearing life spans. Without a large number of training samples, feature engineering is necessary. Principal component analysis is applied to the spectrogram of vibration signals to obtain prognostic feature sequences. A data augmentation strategy is developed to generate synthetic prognostic feature sequences using learning instances. Subsequently, similarities between the test and learning instances can be assessed using a root mean squared (RMS) difference measure. Finally, an ensemble method is developed to aggregate the RUL estimates based on multiple similar prognostic feature sequences. The proposed approach demonstrates comparable performance with published solutions in the literature. It serves as an alternative method for solving the RUL estimation problem.

Micro-direct-methanol fuel cells (μ DMFCs) use micro-electro mechanical system (MEMS) technology, which offers high energy density, portable use, quick replenishment, and free fuel reforming and purification. However, the μ DMFC is limited by a short effective service life due to the membrane electrode's deterioration in electrochemical reactions. This paper presents a health status assessment and remaining useful life (RUL) prediction approach for μ DMFC under dynamic operating conditions. Rather than making external observations, an internal characterization is used to describe the degradation indicator and to overcome intrusive influences in operation. Then, a Markov-process-based usage behavior prediction mechanism is proposed to account for the randomness of real-world operation. The experimental results show that the proposed degradation indicator alleviates the reduction in μ DMFC output power degradation behavior caused by the user loading profile. Compared with the predictions of RUL using traditional external observation, the proposed approach achieved superior prognostic performance in both accuracy and precision. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a data-driven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models.

Current approaches for monitoring machine health (SOH) and effective prognostics depend on the extensive use of complete degradation data trajectories, implying the reliance on data generation techniques that involve functional degradation of the real system until the failure state is reached. These commonly adopted approaches that depend on labeled target data remain operationally and economically nonviable for most industries and safety critical systems. This paper presents novel approaches that alleviate the existing dependence of most prognostics procedures on Remaining Useful Life (RUL) labeled data for training. To this end, firstly, a hybrid data augmentation procedure is proposed that enables the integration of system knowledge available a priori as well as physics of failure, within the training data. Secondly, an unsupervised Health Index (HI) extraction approach is developed, followed by a long-term prediction of this same HI, that leads to an efficient prediction of RUL without labeled data. Finally, a reliability-based assessment is performed to validate the proposed approach. This comprehensive approach (i.e. integrating all the various stages involved in achieving a RUL prediction based on unlabeled data) is tested on a real industrial aircraft system demonstrating the effectiveness of the proposed approach in real industrial context. [Display omitted] • Comprehensive, unsupervised approach adapted to real-life industrial cases (i.e. where no degradation data are available). • Use of physics-based data augmentation to overcome the lack of real degradation data. • Use of the reconstruction error of an autoencoder to extract the Health Index. • Validation of AI-based predictions using established laws of reliability. [ABSTRACT FROM AUTHOR]

Estimation of the remaining useful life (RUL) of industrial machinery is essential for condition-based maintenance (CBM). While numerous papers have explored this issues, challenges arise as machinery often works in non-stationary conditions, particularly in harsh environments (like mining machines, wind turbines, helicopters, etc.). The data collected from such environments are affected by non-Gaussian noise, posing difficulties for traditional approaches to non-linear state estimation or prediction. The widely used extended Kalman filter (EKF) suffers from the non-Gaussian noise effect due to its recursive minimum L2-norm filtering. To address these issues, we propose a robust EKF based on the maximum correntropy criterion. This method effectively estimates the RUL of the time-varying degradation process in the presence of non-Gaussian noise, also enabling confidence interval computation for uncertainty management. The efficiency of our approach was confirmed through application to simulated and benchmark data sets, outperforming Kalman filter-based methods for both simulated and real-world scenarios. • Maximum correntropy criterion extended Kalman filter (MCEKF) is proposed. • MCEKF is used to estimate remaining useful life. • The method assumes the non-Gaussian distribution and time-varying characteristics of the data. • Efficiency is verified for simulated data sets. • Three benchmark real data sets have been used to validate the procedure. [ABSTRACT FROM AUTHOR]

With smart electronic devices delving deeper into our everyday lives, predictive maintenance solutions are gaining more traction in the electronic manufacturing industry. It is imperative for the manufacturers to identify potential failures and predict the system/device's remaining useful life (RUL). Although data-driven models are commonly used for prognostic applications, they are limited by the necessity of large training datasets and also the optimization algorithms used in such methods run into local minima problems. In order to overcome these drawbacks, we train a Neural Network with Bayesian inference. In this work, we use Neural Networks (NN) as the prediction model and an adaptive Bayesian learning approach to estimate the RUL of electronic devices. The proposed prognostic approach functions in two stages—weight regularization using adaptive Bayesian learning and prognosis using NN. A Bayesian framework (particle filter algorithm) is adopted in the first stage to estimate the network parameters (weights and bias) using the NN prediction model as the state transition function. However, using a higher number of hidden neurons in the NN prediction model leads to particle weight decay in the Bayesian framework. To overcome the weight decay issues, we propose particle roughening as a weight regularization method in the Bayesian framework wherein a small Gaussian jitter is added to the decaying particles. Additionally, weight regularization was also performed by adopting conventional resampling strategies to evaluate the efficiency and robustness of the proposed approach and to reduce optimization problems commonly encountered in NN models. In the second stage, the estimated distributions of network parameters were fed into the NN prediction model to predict the RUL of the device. The lithium-ion battery capacity degradation data (CALCE/NASA) were used to test the proposed method, and RMSE values and execution time were used as metrics to evaluate the performance. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0 technology including the Internet of Things (IoT) and deep learning techniques, it is important to reduce maintenance costs and ensure the safety of manufacturing process. The cutting tool degradation can cause significant economic losses and risks for machine users. Accurate prediction of cutting tool is important for making full use of cutter life. Deep learning plays an important role for tool condition monitoring. To overcome these difficulties, this paper aims to propose a new approach in the application of deep learning to estimate the tool wear during the milling process. The proposed methodology is based on the data-driven approach using Variational Mode Decomposition (VMD) and deep learning. Two deep learning machines used in this study, Convolutional Neural Networks (CNN) and Bidirectional long short-term memory (BiLSTM) to achieve collaborative data mining on (VMD) and to enhance the accuracy of modeling. VMD is a new decomposition technique used to decompose signal into sub-time series called intrinsic mode functions (IMFs). However, the VMD performances specifically depend on the constraints parameters that need to be pre-determined for VMD method especially the number of modes. The model development based on 1D-CNN and BiLSTM are selected by using the IMFs as inputs. The performance of the proposed approach is further improved by using the combined CNN and BiLSTM and has shown higher performances in prediction, compared to traditional learning techniques and adopted in previous works highlight the proposed prognostics method's superiority. Among all models, the VMD-CNN-BiLSTM achieve the best performance of modeling with respect to efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Prognostics is an AI-based technique for predicting the degrading/damaging behavior and remaining useful life (RUL) of a system, which facilitates a cost-effective and smart maintenance process. Many prognostics methods have been developed for various applications, such as bearings, aircraft engines, batteries, and fuel cell stacks. Once a new prognostics method is developed, it is evaluated using several metrics based on the true value of the RUL. However, these typical evaluation metrics are not applicable in real-world applications, as the true RUL cannot be known before the actual failure of a system. There are no ways to determine the reliability of prognostics results in practice. Therefore, this article presents the credibility criteria of prognostics results based on prediction intervals (PI), which are known values, unlike the true RUL. The PI-based credibility criteria for prognostics results are explained with two simple examples under different levels of noise to help with the decision making on prognostics results in the industrial field. [ABSTRACT FROM AUTHOR]