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Period phase gradient metasurface plays a great role in promoting the innovation of acoustic application devices. However, harnessing the internal thermal viscosity of the period phase gradient metasurface to realize sound absorption and non-reciprocal transmission faces the narrow working frequency band and uncontrollable efficiency. In this paper, we propose a lossy metasurface by embedded microperforated walls to realize sound redirection and absorption with high efficiency than 90% simultaneously. The phase modulation is realized using an opening channel, which can cover the 2 π phase range in a broadband frequency range by changing the channel depth. The loss introduced by the microperforated walls can achieve efficient sound energy dissipation when negative reflection occurs. The functions can be switched between wave redirection and wave absorption by rotating the metasurface. In addition, this metasurface can redirect the incident wave below −10° and absorb the incident wave above 25° over a wide frequency range from 1500 to 6500 Hz. The simulation and experiment results of our design are in excellent agreement. This research provided a new bridge to integrate wave redirection and absorption with microperforated walls and may have potential applications in acoustic sensing, sound source identification, and mechanical fault diagnosis. [ABSTRACT FROM AUTHOR]

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environments. [ABSTRACT FROM AUTHOR]

Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic and health management. Most of existing data-driven fault diagnosis approaches are designed in the framework of supervised learning, which requires a large number of labelled samples. In this paper, a novel self-training semi-supervised deep learning (SSDL) approach is proposed to train a fault diagnosis model together with few labelled and abundant unlabelled samples. The addressed SSDL approach is realised by initialising a stacked sparse auto-encoder classifier using the labelled samples, and subsequently updating the classifier via sampling a few candidates with most reliable pseudo labels from the unlabelled samples step by step. Unlike the commonly used static sampling strategy in existing self-training semi-supervised frameworks, a gradually exploiting mechanism is proposed in SSDL to increase the number of selected pseudo-labelled candidates gradually. In addition, instead of using the prediction accuracy as the confidence estimation for pseudo-labels, a distance-based sampling criterion is designed to assign the label for each unlabelled sample by its nearest labelled sample based on their Euclidean distances in the deep feature space. The experimental results show that the proposed SSDL approach can achieve good prediction accuracy compared to other self-training semi-supervised learning algorithms. [ABSTRACT FROM AUTHOR]

This study proposes an ensemble deep learning method for diagnosing faults in a hybrid power generation system composed of high-power proton exchange membrane fuel cells (PEMFCs) and ammonia-hydrogen fueled internal combustion engines (AHICEs). The ensemble method integrates principal component analysis, a multi-scale convolutional neural network, and an optimized support vector machine to evaluate the system's reliability and accuracy under various single and multiple fault conditions. The results demonstrate that the ensemble deep learning method achieves an overall fault diagnosis accuracy of 98.15% for PEMFCs when diagnosing both single and multiple faults. Similarly, for the internal combustion engine, the method attains an accuracy of 97.85% under equivalent fault conditions. Additionally, when the hybrid system simultaneously encounters multiple faults in both components, the ensemble method achieves a fault diagnosis accuracy of 96.67%. Compared to traditional methods such as support vector machine, backpropagation neural networks, and principal component analysis-support vector machine models, the proposed approach consistently demonstrates superior diagnostic performance. By leveraging its inherent advantages such as dimensionality reduction, enhanced feature extraction, and robust classification, the proposed method provides an efficient and accurate fault diagnosis in hybrid power generation systems. • A hybrid power system model integrating PEMFC and ammonia-hydrogen fueled internal combustion engines is established. • Various fault types in the hybrid power generation system are investigated. • A PCA–CNN–SVM-based classification method is proposed for fault diagnosis. • The proposed method achieves fault diagnosis accuracy exceeding 96.67%. [ABSTRACT FROM AUTHOR]

Industrial machinery relies heavily on accurate fault diagnosis to maintain its reliability and operational efficiency. However, analyzing vibration signals for early fault detection is complex, as these signals often suffer from low signal-to-noise ratios, background noise, and random interferences. While wavelet filters are frequently employed for identifying informative frequency bands, determining the optimal filter parameters is crucial for accurately extracting repetitive transients related to faults. This research proposes a novel approach that utilises a crayfish optimization algorithm (COA) to adaptively optimise the wavelet filter. The COA employs correlated kurtosis (CK) as a fitness function to guide the optimization process. This method addresses limitations related to inaccurate CK period estimation through a continuous update mechanism, ensuring more precise parameter selection. Through its application to various industrial cases, the proposed methodology demonstrates its superiority over existing techniques in accurately extracting informative frequencies. This advancement enables more precise fault diagnosis, leading to improved maintenance strategies and minimized downtime in industrial environments. [ABSTRACT FROM AUTHOR]

Diagnosability is a fundamental consideration when designing an interconnected network. The PMC and MM ∗ fault diagnosis models are the two most commonly used models. Both the g -good-neighbour diagnosability and g -extra diagnosability of an interconnection network have been two of the hot topics in the intersectional research areas of Graph theory and Computer Science, which become increasingly attractive for new solutions to real-world problems. However, there are still some problems in the transformation from the concepts of Computer Science to that of mathematics. In this paper, we systematically study such problems and give a strict proof from concepts to mathematical conclusions. In the terms of results, we not only give the relationship between g -good-neighbour diagnosabilities of the network under PMC model and MM ∗ model, but also between g -extra diagnosabilities of the network under PMC and MM ∗ models. To apply our results, we give an application on the enhanced hypercube in the end and derive a lemma explaining whether these are 3-cycles in enhanced hypercubes and how many common neighbours for two vertices of enhanced hypercubes under different values of k in the meantime. [ABSTRACT FROM AUTHOR]

ABSTRACT This article presents a novel approach for fault diagnosis in rotating machinery using the few‐shot learning‐based unknown recognition and classification (FSLB‐UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in‐house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB‐UR&C model with data scaling exhibits markedly improved performance. [ABSTRACT FROM AUTHOR]

Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance. [ABSTRACT FROM AUTHOR]

Slope entropy (SlE) is an effective nonlinear dynamic analysis method, which has been used in mechanical fault diagnosis field. However, SlE only analyzes the time series on a single scale with much important information on other scales being ignored. Inspired by the multiscale analysis, the multiscale slope entropy (MSlE) is developed to extract the multiscale features of time series. Nevertheless, MSlE is susceptible to the loss of important information of original time series due to insufficient coarse-graining. In this paper, a novel algorithm termed refined time-shift multiscale slope entropy (RTSMSlE) is further proposed for enhancing the performance of MSlE. RTSMSlE changes the original coarse-grained computation and effectively improves the nonlinear analysis performance of MSlE, which has higher discriminating power and is less affected by mutant signals. After that, a novel fault diagnosis method for rotating machinery is proposed based on the RTSMSlE and DBO–SVM classifier. The effectiveness and superiority of the proposed fault diagnosis method is verified via the simulated signals and the measured data analysis with comparison to the MSlE, refined time-shift multiscale sample entropy (RTSMSE), refined time-shift multiscale fuzzy entropy (RTSMFE), refined composite multiscale sample entropy (RCMSE) and refined composite multiscale dispersion entropy (RCMDE). The analysis results show that the proposed method provides better diagnostic effect and more stable performance in analyzing the vibration signals of rotating machinery than the compared methods. [ABSTRACT FROM AUTHOR]

Data-driven intelligent methods have been widely used in bearing fault diagnosis. However, it is observed that previous studies on bearing fault diagnosis always assume that the label samples are sufficient and that the number of normal and fault samples is the same or similar, which is challenging to meet in practical engineering applications. This assumption reduces the accuracy and stability of the semi-supervised imbalanced bearing data fault diagnosis model in practical working conditions. The complex training and weak interpretation problems of transfer learning methods are analyzed, and a center jumping boosting machine method for bearing intelligent fault recognition with semi-supervised and imbalanced data is proposed. First, a modified density peak clustering (DPC) algorithm is used to classify unlabeled samples and select subsamples, and aiming at the DPC problem, a γ DPC algorithm based on the γ jumping phenomenon is proposed to determine the number of clusters and intercept distance automatically. Second, combined with the synthetic minority oversampling technique, some minority class samples are added to achieve a balanced bearing dataset. Then, a few known faults are used to assign pseudo-labels to unknown samples. Finally, to diagnose the new data and reduce the amount of calculation in actual production, the balanced data after processing are used to train the bottom light gradient boosting machine model to solve intelligent classification and recognition of bearing vibration data. In addition, by using three bearing datasets with different balance ratios and comparing them with other methods, the superiority of the proposed method is verified in bearing condition identification. [ABSTRACT FROM AUTHOR]

Piezoelectric needle selectors, as key weaving components in the jacquard knitting process of knitting machinery, are widely used in textile equipment. Accurate diagnostic procedures for needle selector drive faults are crucial to ensure the normal operation of the equipment. However, traditional vibration diagnosis methods cannot detect weak periodic signals, resulting in low accuracy of monitoring results. To address this issue, this paper proposes an adaptive stochastic resonance (SR) method based on an improved generative adversarial network (IGAN). Firstly, in order to solve the problem of difficulty in obtaining stochastic resonance parameters, SR is combined with IGAN, and IGAN provides the optimal SR parameters. Secondly, a soft threshold residual attention mechanism and residual network were introduced in the GAN network, and multiple generators were utilized to alleviate the problem of model collapse, in order to adapt to the actual working environment. In addition, due to the large amount of data, it is recommended to use feature parameters for training to improve the efficiency of model training. Finally, through a typical vibration data, the influence of different parameter quantities on the training accuracy of the model was studied, and the superiority of the proposed model compared to other models and the stability of the model under different environmental influences were explored. The results show that this method can effectively detect weak periodic signals of the needle selection driver, effectively alleviate the problem of model collapse in different environments, and is superior to existing methods in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

ABSTRACT In practical industrial applications, equipment usually operates normally and failures are relatively rare, resulting in serious imbalances in the collected data. This imbalance leads to issues such as overfitting, instability, and poor robustness, significantly reducing the accuracy and stability of fault diagnosis system. To address these challenges, this research proposes a method for imbalanced data augmentation and industrial process fault diagnosis based on improved Generative Adversarial Network (GAN). The method adopts Wasserstein distance with gradient penalty and integrates residual connections into the architecture of the generator. This innovation not only helps improve gradient transfer in the generator, but also significantly enhances the data generation capabilities of the generative model through improving the stability of training. Limited industrial process data is used by a generative model to produce synthetic samples with high similarity and diversity. These high‐quality samples improve fault diagnosis by enriching the imbalanced dataset. Experimental results on two industrial datasets confirm the method's effectiveness in enhancing fault diagnosis performance with limited data. [ABSTRACT FROM AUTHOR]

Design optimization of multilayer piezoelectric transducers is intended for efficient and practical usage of wideband transducers for fault diagnosis, biomedical, and underwater applications through adjusting layer thicknesses and volume fraction of piezoelectric material in each layer. In this context, we propose a parallel differential evolution (PDE) algorithm to mitigate the complexities of multivariate optimization as well as the computation time to achieve an optimized wideband transducer for the particular application. For lead magnesium niobate-lead titanate (PMN PT)- and PZT5h-based piezoelectric materials, the fitness function is formulated based on uniformity of mechanical pressure at the first three harmonics to achieve wide bandwidth in the required functional frequency range. It is carried out using a one-dimensional model (ODM), while input layer thicknesses and volume fractions of active material are evaluated using PDE. The simulation is performed on a parallel computing platform utilizing three different host machines to reduce computational time. Results of the proposed methodology for PDE are statistically represented in the form of minimum, maximum, mean, and standard deviation of fitness value, while graphically represented in terms of speedup and time. It can be observed that the execution time for parallel DE decreases with the increasing number of cores. [ABSTRACT FROM AUTHOR]

To address the problem of fault branch recognition in mine ventilation systems, a one-class classification algorithm is introduced to construct the MC-OCSVM ventilation system fault diagnosis model, which is integrated with multiple OCSVMs. This model adopts uniform hyperparameters and transforms the ventilation system fault diagnosis problem into a maximum decision distance problem, to realize the effective use of mine monitoring wind speed data. The experiments on public KEEL datasets verify that the one-class classification integration model can solve the multiclassification problem and that the MC-OCSVM model has better generalizability than other one-class classification integration models do. The experiment is carried out in the Buertai coal mine, and the results show that the proposed algorithm can identify fault branches quickly and accurately, with an accuracy of 93.2% and a single fault diagnosis time is 1.2 s, highlighting its strong robustness. [ABSTRACT FROM AUTHOR]

Faults in the various electrical and mechanical components of a fuel cell system can affect system reliability and durability. In this study, machine learning was used to accurately diagnose 18 faults in a proton exchange membrane fuel cell system. These faults included those in the thermal management system, first cooling line, second cooling line, air supply system, and water management system. Among the random forest, support vector machine, extreme gradient boosting, light gradient boosting machine, and deep neural network algorithms, the deep neural network model exhibited the highest accuracy in model training. Before diagnosing the 18 faults, a pipeline scenario was introduced to address the data imbalance between normal and fault data and to distinguish between normal and fault conditions. A state-based data distribution method proposed to mitigate data imbalance among fault states achieved an F1-score of 0.987 (accuracy of 98.4%) and 0.942 (accuracy of 94.2%) for fault detection and diagnosis, respectively. Misdiagnosed cases were analyzed by considering the physical characteristics of the system. Additionally, a study on training strategies, prediction of data for operating conditions not included in the training process, for designing datasets for machine learning models revealed an F1-score greater than 0.9. This result showed the generality of the model and provided a reference for designing efficient training datasets based on operating conditions. • Machine learning-based diagnosis used for 18 fault types in PEMFC systems. • Pipeline scenario proposed to address data imbalance between normal and fault data. • State-based data distribution devised to mitigate data imbalance among fault states. • Model generality evaluated by predicting data for conditions excluded in training. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty in obtaining the eigenfrequency of the vibration component of rolling bearing faults in a strong background noise environment and the problem of extraction efficiency, the adaptive chirp mode decomposition (ACMD) combined with Improved maximum second-order cyclostationary blind deconvolution (ICYCBD) fault feature extraction algorithm is proposed. Firstly, to improve the signal-to-noise ratio, the original signal is adaptively decomposed using the ACMD method, and the optimal components are selected based on the principle of maximizing the correlation gini coefficient index. Secondly, to improve the accuracy of parameter setting and extraction efficiency, an improved CYCBD method is proposed to estimate the cyclic frequency set of CYCBD using the proposed enhanced energy harmonic product spectrum (EEHPS) method for the optimal components, the envelope spectrum peak factor index is improved by proposing the envelope spectral period pulse factor (EPPF) index, and the filtering length of the CYCBD is selected adaptively using the step search to obtain the optimized filtered signal. Finally, the envelope spectrum analysis is carried out to extract the fault information accurately. The simulation signals and experimental data show that the method can quickly and accurately extract the fault characteristics of rolling bearings under strong background noise, and the comparison with other methods shows the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Rolling bearings are the critical components of rotating mechanical equipment, and it is more important to fault diagnosis and recognition of the rolling bearings. Multivariate variational mode decomposition (MVMD) with one dimensional residual network (1D ResNet) is proposed to fault diagnosis and subtle feature recognition of the rolling bearings. Intrinsic modal components are extracted to further signal process under different operational conditions to the segmentation of signal components and the feature reconstruction. The average success accuracy rate for the ten types of rolling bearing faults (normal, ball fault, inner race fault, outer race fault with different damage degree) exceeds 99.32 %. MVMD-1D ResNet with the advantage of fault recognition of rolling bearings is validated by comparing to other algorithms (1D ResNet, 1D CNN and KNN). MVMD-1D ResNet model has great potential to condition monitoring and subtle feature recognition with limited sample sizes of the rolling bearings. [ABSTRACT FROM AUTHOR]

As rolling bearings frequently operate at varying speeds, the time interval of the induced pulse in the measured vibration signal varies with the speed. This variability makes it difficult to capture the inherent time and frequency information, leading to poor diagnostic performance. In response, this study presents a model for time-varying speed fault diagnosis based on dual-channel parallel multi-scale information. This model incorporates multi-scale information learning into traditional convolutional neural networks (CNNs), employing multiple pairs of convolutional and pooling layers to establish a hierarchical learning structure. Additionally, the model integrates a gated recurrent unit (GRU) to account for time information and enhance the interdependence among time series data. Furthermore, a dual-channel parallel structure is devised to autonomously fuse source data collected by sensors. To validate its effectiveness, the proposed model is assessed using the time-varying speed-bearing dataset. This evaluation includes the introduction of Gaussian white noise and the simulation of experiments in noisy environments. Comparative analysis against advanced methods demonstrates the proposed model's strong discrimination and robustness under conditions of variable rotational speed and noise. [ABSTRACT FROM AUTHOR]

To address the oversight of data feature properties and interactions in the fault diagnosis of multiple different sensors, we introduce a novel fault diagnosis method leveraging a multi-sensor feature selection mechanism. This method employs multiple parallel multi-view feature extraction modules to distill essential fault features from the data collected by different types of sensors and subsequently aligns these features within a unified information metric space. Within this space, a sensor source selector scrutinizes the features, pinpointing those with significant fault-relevant distinctions and calculating a weight matrix for the multi-view features based on these insights. The refined feature set is then forwarded to the fault pattern recognizer, which leverages these optimized features for the precise diagnosis of faults in industrial equipment. Experimental evidence from applying this method to a flash furnace system showcases a fault detection rate exceeding 99 %, markedly improving fault classification accuracy. [ABSTRACT FROM AUTHOR]

Dependency between the conventional imbalance diagnostic feature and the shaft rotational speed makes imbalance diagnosis challenging for variable-speed machines. This paper focuses on an investigation of this dependency and on a proposal for a novel imbalance diagnostic feature and a novel simplified version for this feature, which are independent of shaft rotational speed. An equivalent mass–spring–damper system is investigated to find a closed-form expression describing this dependency. By normalizing the conventional imbalance diagnostic feature by the obtained dependency, a diagnostic feature is proposed. By conducting comprehensive experimental trials with a wind turbine with a permissible imbalance, it is justified that the proposed simplified version of imbalance diagnostic feature is speed-invariant. [ABSTRACT FROM AUTHOR]

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a crucial role in maintaining the operational safety of mechanical systems. As intelligent data-driven approaches evolve, deep learning (DL) has emerged as a pivotal technique in fault diagnosis research. However, the collected vibrational signals from mechanical systems are usually corrupted by unrelated noises due to complicated transfer path modulations and component coupling. To solve the above problems, this paper proposed the dynamic temporal denoise neural network with multi-head attention (DTDNet). Firstly, this model transforms one-dimensional signals into two-dimensional tensors based on the periodic self-similarity of signals, employing multi-scale two-dimensional convolution kernels to extract signal features both within and across periods. Secondly, for the problem of lacking denoising structure in traditional convolutional neural networks, a temporal variable denoise (TVD) module with dynamic nonlinear processing is proposed to filter the noises. Lastly, a multi-head attention fusion (MAF) module is used to weight the denoted features of signals with different periods. Evaluation on two datasets, Case Western Reserve University bearing dataset (single sensor) and Real aircraft sensor dataset (multiple sensors), demonstrates that the DTDNet can reduce the useless noises in signals and achieve a remarkable improvement in classification performance compared with the state-of-the-art method. DTDNet provides a high-performance solution for potential noise that may occur in actual fault diagnosis tasks, which has important application value. [ABSTRACT FROM AUTHOR]

To address the challenge of extracting effective fault features at low speeds, where fault information is weak and heavily influenced by environmental noise, a parameter-adaptive variational mode decomposition (VMD) method is proposed. This method aims to overcome the limitations of traditional VMD, which relies on manually set parameters. The sparrow search algorithm is used to calculate the fitness function based on mean envelope entropy, enabling the adaptive determination of the number of mode decompositions and the penalty factor in VMD. Afterward, the optimised parameters are used to enhance traditional VMD, enabling the decomposition of the raw signal to obtain intrinsic mode function components. The kurtosis criterion is then used to select relevant intrinsic mode functions for signal reconstruction. Finally, envelope analysis is applied to the reconstructed signal, and the results reveal the relationship between fault characteristic frequencies and their harmonics. The experimental results demonstrate that compared with other advanced methods, the proposed approach effectively reduces noise interference and extracts fault features for diagnosing low-speed bearing faults. [ABSTRACT FROM AUTHOR]

The widespread diffusion of electric vehicles poses new challenges in the field of fault diagnostics. Past studies have been focused mainly on machines designed for industrial applications, where the operating conditions and requirements are significantly different. This work presents a review of the most recent studies about fault diagnosis techniques in electrical machines feasible for traction applications, with a focus on the most adopted approaches of the last years and on the latest trends. Considerations about their applicability for electric vehicle purposes, along with some areas that require further research, are also provided. [ABSTRACT FROM AUTHOR]

D1244 in operation – first data and results on the way to controlled adaptation Global construction is responsible for about 50 % of the world's CO2‐emissions and resource consumption. For a sustainable future, a drastic reduction is required, which, however, is in contradiction to the worldwide demographics characterized by population increase and urbanization. One possible approach are adaptive load‐bearing structures equipped with distributed actuators and sensors that can manipulate deformations and stress distributions under loads through geometric adaptation. For high‐rise buildings, this technology enables a reduction of resource consumption by about 50 %, for example. The world's first prototype was inaugurated at the end of 2021 on the campus of the University of Stuttgart. With twelve floors on a base area of 5 m × 5 m, the D1244 is 36 m high and has 24 hydraulic actuators, 128 strain gauges, and an optical measurement system that uses two cameras to measure deformations at 16 points on the structure in a plane parallel to the camera sensor with submillimeter precision. This paper illustrates the steps within the model‐based development chain for automation and control of D1244 that have been accomplished and the results that have been achieved on this high‐rise building. The experimental model identification and the subsequent model‐based controller and observer design are discussed. Finally, the algorithms used for fault detection and diagnosis are presented and their basic functionality is demonstrated on the basis of initial validation measurements. [ABSTRACT FROM AUTHOR]

The performance of a diesel engine is closely related to the injector, and the fuel pressure shock signal for monitoring the state of the injector always contains multiple natural oscillation modes at different levels. Therefore, it is very necessary to detect the dynamic changes of the injector signal from multiple levels. Multi-scale Diversity Entropy (MDE) is a recent commonly used method of information entropy. However, due to its inherent defects, it limits the wide application of MDE in fault feature extraction. Therefore, this paper proposes a new nonlinear dynamics method, called the Refined Composite Hierarchical Bidirectional Diversity Entropy (RCHBDE). The ability of RCHBDE to extract signal complexity changes was verified through simulation signals, which is superior to MDE and multi-scale bidirectional diversity entropy (MBDE). On this basis, this article proposes a diagnostic framework for fuel injector fault type and degree based on RCHBDE and Seagull optimized Support Vector Machine. We apply the proposed method to the injector failure test data. The results show that RCHBDE can better reflect the dynamic change process of the injector fault signal than MDE and MBDE. Moreover, compared with existing methods, the proposed method in this paper improves the accuracy and efficiency of fault diagnosis. [ABSTRACT FROM AUTHOR]

Due to the mutual coupling between sub-faults in the case of compound faults in rolling bearings, coupled with external noise, energy attenuation during information transmission, etc., the acquired fault information is usually very weak and complex. This greatly increases the difficulty of extracting features from bearing compound faults. To further enhance the accuracy of fault feature extraction and achieve precise identification of rolling bearing compound faults, this paper presents an innovative compound fault diagnosis method that integrates variable step-size multiscale weighted Lempel-Ziv complexity (VMW-LZC) and intrinsic time-scale decomposition (ITD). First, considering that the traditional Lempel-Ziv complexity (LZC) method can only extract single-dimensional fault information without thoroughly exploiting fault characteristics, we optimize the coarse-graining process of proper rotation components (PRCs) after ITD using a variable step-size multiscale strategy. Additionally, LZCs obtained from variable step-sizes in each scale are fused and reconstructed using a weighted method. Meanwhile, multiple variable step-size multiscale LZCs are combined into a new signal evaluation index, VMW-LZC. In addition, with the new signal evaluation index VMW-LZC, the optimal PRC which is best representative of fault characteristic information is chosen. Furthermore, frequency spectrum of autocorrelation function of optimal PRC is used to identify multiple faults of bearings. To exemplify the efficiency of presented method, a comparison has been made among the optimal PRC chosen by the methods with VMW-LZC, traditional LZC (T-LZC), multiscale LZC(M-LZC), and classical kurtosis as signal evaluation indexes. The result has indicated that fused signal evaluation index VMW-LZC can actualize more precise option of sensitive fault component signals and the proposed ITD-VMW-LZC method can do a more precise job in identifying a compound fault of bearings. [ABSTRACT FROM AUTHOR]

When it comes to practical engineering, there is a high likelihood of shafts developing slant cracks due to torque transmission. These cracks exhibit breathing characteristics, repeatedly opening and closing under gravity and radial forces. To diagnose rotor systems with breathing slant cracks, the nonlinear output frequency response functions (NOFRFs) method is introduced. Firstly, a dynamic model of rotor systems with breathing slant cracks is established, and the system's frequency spectra are obtained by solving the model with different crack angles, depths, and positions. Secondly, the NOFRFs values at different orders are derived based on the NOFRFs theory. The simulation results display increased nonlinear characteristics with crack angle and depth. The system's nonlinear characteristics are stronger when cracks are closer to the disk. The G 3 (j ω F) ¯ value for the first-order is sensitive to crack angles, the G 4 (j 2 ω F) ¯ value for the second-order is sensitive to crack depths, and the G 4 (j 4 ω F) ¯ value for the fourth-order is sensitive to crack positions. As a result, the NOFRFs response at different orders can detect variations in angles, depths, and positions of the breathing slant crack. Finally, experiments are conducted on a rotor system with breathing slant cracks, and the results match those of the simulation, indicating that the NOFRFs method is feasible for identifying breathing slant cracks. [ABSTRACT FROM AUTHOR]

Due to the variable working environment of bearings, the collected data often follow different probability distributions. It is hard to directly use the trained models to identify the bearing fault with different operating conditions. In addition, it is a high cost to label the samples for every work condition. To solve these problems, a multi-scale adversarial subdomain adaptation bearing fault diagnosis method is proposed, which is based on Continuous Wavelet Transform (CWT) and our constructed Multi-scale Adversarial SubDomain Adaptation Network (MASDAN). Firstly, to extract the features of non-stationary signals with different frequency bands, CWT is used to convert continuous vibration signals into two-dimensional time-frequency images. Secondly, to enhance the correlation of the features across frequency bands, a multi-scale ConvNeXt is proposed, which adds a multi-scale module based on ConvNeXt to obtain features with different scales. Finally, to reduce the distribution discrepancy between the source domain and the target domain and to avoid feature confusion in different domains, a domain adaptive alignment network introducing domain information is constructed. Two modules are included: the Domain Alignment Classification Network Module (DACNM) based on Multi-kernel Local Maximum Mean Discrepancy (MK-LMMD) and the Domain Adversarial Network Module (DANM) based on domain discrimination. Thus, the MASDAN consists of the multi-scale ConvNeXt module, DACNM, and DANM, which can realize the multi-scale feature extraction and adaptively align the fault features at different domains. The experimental results on the Qingdao University of Technology (QUT) bearing dataset and the Case Western Reserve University (CWRU) bearing dataset demonstrate the proposed method can effectively diagnose bearing faults under different operating conditions. [ABSTRACT FROM AUTHOR]

The health status of bearings seriously affects the operational efficiency of equipment, and it is important to carry out bearing health status detection. A bearing fault diagnosis method based on stepwise variational modal decomposition (SVMD) with adaptive initialization center frequency and Gramian angular difference field is proposed. Firstly, a method of center frequency initialization base on frequency energy distribution characteristics is proposed to improve the decomposition speed and stability. Secondly, SVMD with single component decomposition and local decomposition is proposed to improve decomposition efficiency. It can effectively avoid inconsistency in different signal parameter settings and ensures consistency in the number of signal components, which is very suitable for batch processing of signals. Finally, Gramian angular field (GAF) and convolutional neural networks (CNNs) are combined to extract features of the reconstructed signal spectrum and enhance the differential characteristics between different signal spectrum. The experiment shows that the center frequency initialization method can shorten the single decomposition time from 11.13 to 6.71 s. The overall recognition rate can reach 95.2%, which is at least 1.9% higher than other decomposition methods. [ABSTRACT FROM AUTHOR]

Faulty compressors must be timely detected to prevent excessive energy consumption, maintenance, and energy costs. Existing diagnostics models lack addressing the energy performance indicators and do not provide effective hybrid machine learning (ML) model for advanced fault diagnosis to prevent compressors from becoming energy hogs. Therefore, this study proposed a novel approach in the form of an energy-based diagnostic model for integrating energy performance indicators to detect the healthy and faulty behavior of the compressor using a hybrid ML model. The time series analysis and Isolation Forest techniques have been used to detect faulty and healthy behavior of centrifugal gas compressor. To obtain more insight and prevent false alarms the hybrid ML model was introduced. The Ridge regression was used as the meta-classifier in the suggested hybrid model, which receives the input from the base classifiers Decision Tree (DT), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) to optimize the performance and accuracy of hybrid model. This study was conducted on a two-stage centrifugal compressor that compresses production gas for export powered by a gas turbine at Malaysia's PETRONAS Angsi oil and gas field. According to the findings, the energy efficiency predicted for the first stage was 84.2% for healthy behavior and 69.7% for faulty behavior, while for the second stage, it was 83.2% for healthy behavior and 68.1% for faulty behavior, indicating high energy efficiency during the healthy operation of a centrifugal compressor in comparison with faulty behavior. The slight difference between the proposed diagnostic model training, testing, and prediction performance accuracy 0.98, 0.97 and 0.99 proposes a model is efficient neither overfitting nor underfitting according to the value of co-efficient of determination (R2). The R2 values for training, testing, and prediction performance accuracy for the GB model were 0.95, 0.93, and 0.94; for kNN, 0.89, 0.87, and 0.86, and for Tree, 0.95, 0.94, and 0.93 respectively. According to the results, the proposed hybrid model performs more eloquently and efficiently than other single models DT, kNN, and GB. This study empowers operators to take critical measures to increase energy efficiency, reduce downtime, and schedule maintenance to improve the reliability of centrifugal gas compressor. [ABSTRACT FROM AUTHOR]

The aerodynamic stability of a compressor has a crucial impact on the performance of modern aircraft power system. It is necessary to design an accurate and reliable rotating stall warning system to take active control measures to avoid compressor instability as much as possible. This paper proposes a compressor rotating stall warning system that combines continuous wavelet transform (CWT) and a vision transformer (ViT), called a CWT-ViT system. Specifically, the system transforms one-dimensional time-series dynamic pressure signal data into two-dimensional color time–frequency images using CWT, which serves as the input to train the ViT classifier. In response to sensor failure, a model ranking execution strategy was adopted to improve the reliability of the whole system. The feasibility and performance of the proposed system were evaluated in different operating modes and sensor failure conditions using compressor stall experiments. The results showed that the average classification accuracy of the proposed system in stall warning tasks was 97.66%, which was the highest among all methods. In addition, the proposed system can maintain an early warning time of over 160 m seven in the case of sensor faults, which was the best warning performance among all methods. [ABSTRACT FROM AUTHOR]

In order to meet the needs of digital development of power system and ensure the safety of power communication equipment, an intelligent patrol inspection method for power communication equipment with low code enabling based on digital transformation is proposed. Build an intelligent patrol platform for low code enabled power communication equipment based on digital transformation. The data acquisition module of the infrastructure layer scans the RFID tags on the communication equipment through the patrol personnel's handheld devices to obtain the specific information of the current equipment. The UDT data transmission protocol of the data transmission module transmits the equipment information collected by the patrol personnel to the patrol application layer; The fault diagnosis module of patrol application layer uses convolutional neural network to analyze patrol data and complete fault detection of power communication equipment; The integrated component layer uses the application components developed by visual tools to enable the platform with low code through the low threshold platform development technology; The front-end service layer, supported by the technology of the integrated component layer, implements the simplification and configurable processing of front-end display results such as patrol results display, and views and manages the patrol results of the platform through the user terminal layer. The experiment shows that the method is accurate in data acquisition, fast in data transmission and high in fault diagnosis accuracy. It is a highly practical intelligent patrol method for power communication equipment. [ABSTRACT FROM AUTHOR]

In order to realize high-precision diagnosis of bearings faults in a multi-sensor detection environment, a fault diagnosis method based on two-stage signal fusion and deep multi-scale multi-sensor networks is proposed. Firstly, the signals are decomposed and fused using weighted empirical wavelet transform to enhance weak features and reduce noise. Secondly, an improved random weighting algorithm is proposed to perform a second weighted fusion of the signals to reduce the total mean square error. The fused signals are input into the deep multi-scale residual network, the feature information of different convolutional layers is extracted through dilated convolution, and the features are fused using pyramid theory. Finally, the bearings states are classified according to the fusion features. Experiment results show the effectiveness and superiority of this method. • Application range of the traditional random weighted fusion algorithm is extended. • A new signal fusion method based on two-stage weighting is constructed. • An improved deep residual network is proposed to realize multi-scale features extraction and fusion enhancement. [ABSTRACT FROM AUTHOR]