Deep learning and its applications to machine health monitoring

Applications of machine learning to machine fault diagnosis: A review and roadmap

With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.

A transfer convolutional neural network for fault diagnosis based on ResNet-50

Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

One of the major obstacles of bearing fault diagnosis under variable shaft rotational speed is poor energy aggregation in time-frequency representation (TFR) for instantaneous frequency (IF) extraction. To address such a problem, a novel approach is developed in this paper to achieve an accurate IF estimation, which consists of three main steps: a) chop lower and resonance frequency band, b) acquire multiple pre-IF ridges via Regional Peak Search Algorithm (RPSA) from their TFRs obtained by Short Time Fourier Transform (STFT), c) integrate pre-IF ridges based on the frequency-redistribution and Probability Density Function (PDF) to obtain the final IF estimation. Then, bearing shaft rotating IF can be accurately extracted, from which bearing fault diagnosis can be performed without the involvement of tachometers. The effectiveness of the proposed method for IF estimation is validated by both numerical simulations and experimental signal analysis.

Instantaneous Frequency Estimation Via Multiple Ridge Integration Scheme for Bearing Fault Diagnosis

To perform bearing fault diagnosis under variable speeds, the optimal resonant frequency (ORF) band selection and diagnosis strategy are pivotal. Indexes, such as kurtosis, crest factor (CF) and smoothness index (SI), are extensively used for guiding ORF selection. Due to that each index has unique advantages, the hybrid of such indexes has been developed. However, applications of the current index hybrid method are impeded by problems of: 1) ineffectiveness for signal corrupted by impulsive noises and 2) equal segmentation of frequency band with human intervention. This paper, therefore, firstly proposes a dual-guidance based scheme with an embedded tunable Q-factor wavelet transform (TQWT) to address the problems. The so-called dual-guidance scheme contains two guidance procedures: 1) the SI guided pre-process for obtaining weight vectors and 2) the index hybrid output guided scheme for ORF selection. The embedded TQWT is used for frequency band segmentation and sub-band signal acquisition without subjective interventions. With the proposed scheme, the ORF band can be determined for bearing fault feature extraction. Then, an algorithm for multiple instantaneous frequency (IF) ridge identification is exploited based on the peak search algorithm for diagnosis. To tackle the difficulty that, at each time instance, the amplitudes of IF ridges of interest do not always dominate the time frequency representation (TFR), a starting point search tactic with a synchronization step is explored. A diagnosis vector can subsequently be obtained by calculating the average ratios of the identified ridges and bearing fault diagnosis can then be done by matching the elements of the diagnosis vector with fault characteristic coefficient (FCC). Comparisons are performed to illustrate the superiority of the proposed method. The experimental analyses are also conducted to validate the proposed method for bearing fault diagnosis under variable speeds.

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Dual-Guidance-Based Optimal Resonant Frequency Band Selection and Multiple Ridge Path Identification for Bearing Fault Diagnosis Under Time-Varying Speeds

Rotating machinery often runs under large-speed-fluctuation (LSF) conditions, which results in severe data distribution domain shift for intelligent fault diagnosis methods. However, this challenge is rarely discussed in current studies. Hence, in this article, motivated by the active imagination of a human being, a new tool named actively imaginative data augmentation (AIDA) is constructed to solve machinery intelligent diagnosis under LSF conditions. The two adversarial training steps, namely, knowledge learning and sample imagining, are included in AIDA. In knowledge learning, a deep model is trained to learn the classification knowledge. In sample imagining, the parameters of the deep model are fixed and samples are generated via inversely training the model. As a result, diversified samples and an intelligent deep model adapting to the LSF condition are obtained by alternately carrying out the two steps. Moreover, a detailed discussion is given to interpret the process of actively imagining samples in the proposed AIDA in which some measures are designed, and the feature visualization is conducted. Experimental results show the effectiveness and superiority of AIDA in machinery diagnosis under LSF conditions, and the good performance of AIDA is due to the diversified dataset generated by changing the degrees and directions of each sample imagining.

Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

The bearing failure diagnosis methods based upon variational mode decomposition (VMD) have been researched extensively in recent years. However, these methods are only capable of dealing with single channel data, in which the amount of information is limited and the anti-interference ability needs to be further enhanced. Recently, as one of extensions of VMD, the multivariate variational mode decomposition (MVMD) was put forward to deal with the multivariate signal. For the sake of monitoring the operation state of equipment more comprehensively and diagnose mechanical failure more accurately, we propose a new bearing failure diagnosis method based upon MVMD. In particular, to select a suitable value of decomposed modes K, a scheme which combines correlation coefficient and kurtosis criterion is innovatively proposed to enhance the performance of the MVMD. The result of experimental verification has demonstrated the enhanced performance of this new method in transient extraction and faults detection of bearings over the original VMD method.

Research on Multivariate Variational Mode Decomposition Method and Its Application to Bearing Fault Diagnosis

Tacholess order tracking techniques based on time-frequency (TF) ridge detection have been extensively used in bearing fault diagnosis under varying speed conditions for decades. However, the signal components of a fault bearing related to shaft rotational frequency (SRF) is difficult to be accurately extracted by these methods because of TF resolution limitation and strong noise interference. A new TF decomposing algorithm, that is, variational nonlinear chirp mode decomposition (VNCMD) is effective to extract the time-varying feature under limited TF resolution. However, its performance is influenced by prior knowledge of initial parameters. Besides, ridge information hidden in noise is difficult to be mined effectively, which increases the difficulty of ridge extraction. In this study, a feature isolation technology is proposed to enhance fault-related features and reduce the interference of noise and irrelevant components. Then inspired by the decomposing properties research on the convergence characteristics of VNCMD, an optimization tendency guiding mode decomposition (OTGMD) method is proposed to track the instantaneous frequency (IF) of fault-related mode, which can alleviate the personnel experience requirement and is not affected by the set of TF resolution. The proposed method mainly consists of three steps. First, SRF-related information is highlighted through low-pass filtering, and the dominant IF is achieved through ridge detection method. Subsequently, for the convenience of mode extraction, the fault characteristic is augmented through iterative envelope analysis. Then, the OTGMD optimization strategy is developed to gradually decompose the target mode on the basis of the above process. Finally, a stopping criterion based on characteristic frequency ratios (CFRs) is constructed to adaptively terminate the iteration process. Simulation and experiments demonstrate that the proposed method is effective and suitable for bearing fault diagnosis under varying speed conditions.

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Xingxing Jiang

Papers

Instantaneous Frequency Estimation Via Multiple Ridge Integration Scheme for Bearing Fault Diagnosis

Dual-Guidance-Based Optimal Resonant Frequency Band Selection and Multiple Ridge Path Identification for Bearing Fault Diagnosis Under Time-Varying Speeds

Actively Imaginative Data Augmentation for Machinery Diagnosis Under Large-Speed-Fluctuation Conditions

Research on Multivariate Variational Mode Decomposition Method and Its Application to Bearing Fault Diagnosis

An Optimization Tendency Guiding Mode Decomposition Method for Bearing Fault Detection Under Varying Speed Conditions