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The bearing fault impact impulses induced by the contact between components with drawback, is difficult to be detected at sprouting stage due to the interference of background noise, harmonics, random shocks, etc. In this paper, an impulse feature enhanced weighted sparse representation (IFEWSR) algorithm is proposed to accurately detect the weak bearing fault impact feature from incipient stage condition monitoring (CM) signal. Firstly, a modified fault period estimation method is presented to improve the robustness and reduce the computational complexity of recently proposed algorithms. Secondly, a novel weighting strategy on wavelet coefficients, indicated by the period-assisted corelated kurtosis of envelope spectrum (CKSES), is presented to denote the contribution of subband signals for sparse representation calculation framework. In addition, the mean normalized energy-weight deviation (MNEWD) rule is proposed to evaluate the performance of the weighting algorithm on subband signals which is blank at present. Thirdly, a novel fault feature enhancement technique is developed to better capture the bearing fault feature information. The effectiveness and superiority of the proposed method are proved by simulation and experiments. Results show that the proposed IFEWSR method provides higher accuracy for incipient fault feature extraction and outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• An evaluation scheme is developed to detect the health status of rolling bearing. • Mixed-scale model combining with CEEMDAN and SVDE is constructed to extract fault features adaptively. • Pt-SNE algorithm is provided for high-dimensional features reduction. Accurate and efficient fault diagnosis for rolling bearing is essential to ensure the reliable and safe operation of mechanical equipment. In this paper, a hybrid hierarchical fault diagnosis method based on scale-variable dispersion entropy (SVDE) and parametric t-distributed stochastic neighbor embedding (Pt-SNE) algorithm, systematically combining the statistics analysis principle and machine learning technique, is proposed. First, an initial evaluation scheme with regard to the health state of bearing is implemented by the SVDE theory blended with the statistical analysis technology. Second, if fault appears, a novel mixed-scale model, integrating series mode with multi-scale factor, is established based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and SVDE for further improvement of feature extraction performance. Subsequently, a Pt-SNE algorithm is provided to reduce the features for higher accuracy and speed of diagnosis. Finally, the obtained low-dimensional representations after reduction can be imported in Softmax classifier for fault recognition. Two vibration datasets are used for bearing fault diagnosis. The experimental results prove the superior performances of the proposed method, and it has better practicability in engineering problems compared with other existing approaches. [ABSTRACT FROM AUTHOR]

The advances of intelligent fault diagnosis in recent years show that deep learning has strong capability of automatic feature extraction and accurate identification for fault signals. Nevertheless, data scarcity and varying working conditions can degrade the performance of the model. More recently, a tool has been proposed to address the above challenges simultaneously. Meta-learning, also known as learning to learn, uses a small sample to quickly adapt to a new task. It has great application potential in few-shot and cross-domain fault diagnosis, and thus has become a promising tool. However, there is a lack of a survey to conclude existing work and look into the future. This paper comprehensively investigates deep meta-learning in fault diagnosis from three views: (i) what to use, (ii) how to use, and (iii) how to develop, i.e. algorithms, applications, and prospects. Algorithms are illustrated by optimization-, metric-, and model-based methods, the applications are concluded in few-shot cross-domain fault diagnosis, and open challenges, as well as prospects, are given to motivate the future work. Additionally, we demonstrate the performance of three approaches on two few-shot cross-domain tasks. Typical meta-learning methods are implemented and available at https://github.com/fyancy/MetaFD. • Review the advances of meta-learning in fault diagnosis for the first time. • Demonstrate deep meta-learning in fault diagnosis via algorithms and applications. • Illuminate meta-learning algorithms by mathematical optimization. • Stimulate future work with open challenges and prospects. [ABSTRACT FROM AUTHOR]

• Proposed cycle envelope spectral segmentation method based to optimize EWT. • Calculate the similarity of the envelope spectra of adjacent signals to optimize the components. • A new indicator called sensitive IMFs assessing index (SIAI) was designed to identify fault information. The empirical wavelet transform (EWT) has made outstanding contributions in the field of fault diagnosis. However, when EWT processes complex signals, there may be modal aliasing or meaningless components. In order to solve the spectral segmentation defect of the EWT method and improve its ability to extract bearing fault features, this paper proposes a new algorithm to improve EWT. In order to weaken the influence of extreme points in the complex Fourier spectrum on modal differentiation, the cycle envelope spectral segmentation method is proposed. The maximum envelope fitting method not only reduces the number of useless extreme points, but also highlights each mode. The number of filters is reduced while suppressing the interference of noise on the modal. The components that contain similar information in the result will be merged based on their correlation. Then sensitive IMFs assessing index (SIAI) is proposed to combine correlation and symmetry of the signal, and obtains the characteristics of time domain, frequency domain, and amplitude domain to extract fault information. Locomotive bearing and wind turbine gearbox bearing fault data verified the effectiveness of the method. [ABSTRACT FROM AUTHOR]

• A JDSDL framework is proposed for bearing intelligent fault classification. • A dictionary extension strategy enables the learned dictionary shift-invariant and significantly improves the classification accuracy. • Applications show that the proposed method is accurate and robust. • We compare the proposed method with competing methods and the proposed method presents respective application scope. Sparse representation is one of the effective approaches for bearing fault diagnosis. Conventional sparse representation only focuses on fault feature extraction from bearing vibration signals while requiring experts to empirically make a diagnosis. This paper investigates bearing intelligent fault classification based on sparse representation based classification (SRC) method. A novel SRC algorithm, named joint discriminative and shared dictionary learning (JDSDL), is proposed for simultaneously characterize the class-specific and common features in vibration signals of bearings with different faults. The JDSDL seamlessly integrates discriminative dictionary learning, shared dictionary learning, and classifier training into a joint model to automatically recognize bearing faults. Moreover, a dictionary extension strategy is embedded within the proposed JDSDL model to make learned discriminative dictionary shift-invariant to improve its classification performance. The main advantages of the proposed method are that the JDSDL not only works well even if the training set is small, but also is robust to corruption of environment noises. Experimental results on three bearing datasets and comparison with competing methods are presented to demonstrate the effectiveness and superiority of the proposed JDSDL in bearing intelligent fault classification. [ABSTRACT FROM AUTHOR]

[Display omitted] With the development of smart sensors, large amount of operating data collected from a complex system as a high-speed train providing opportunities in efficient and effective fault detection and diagnosis (FDD). The data brings also challenges in the FDD modelling process, since the various signals may be redundant, useless and noisy for the FDD modelling of a specific sub-system. The data-driven methods suffer also from the curse of dimensionality. Feature dimension reduction can reduce the dimension of the monitoring dataset and eliminate the useless information. Different from the classical methods based on the correlation among variables, recent studies have shown that causality-based methods can make the FDD model more explanatory and robust. From the adjacency matrix of the causal network diagram, three unsupervised causality-based feature extraction methods for FDD in the braking system of a high-speed train are proposed in this paper. By constructing the causal network diagram among the raw monitoring feature variables through the causal discovery algorithm, the proposed methods extract informative features based on the causal adjacency matrix or the full causal adjacency matrix proposed in this work. These methods are adopted for fault detection with real dataset collected from the braking system in a high-speed train to verify their effectiveness. The experimental results show that the proposed causality-based feature extraction methods are effective and have certain advantages in comparison with the classical correlation-based methods. Especially, the feature extraction method based on the correlation matrix constructed from full causal adjacency matrix achieves better and stable results than the benchmark methods in the experiment. [ABSTRACT FROM AUTHOR]