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

A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery

Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals

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

A comprehensive review on convolutional neural network in machine fault diagnosis

A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

Owing to the character of diversity and complexity, the compound fault diagnosis of rotating machinery under non-stationary operation has turned into a challenging task. In this paper, a novel method based on the optimal variational mode decomposition (OVMD) and 1.5-dimension envelope spectrum is proposed for detecting the compound faults of rotating machinery. In this method, compound fault signals are first decomposed by using OVMD containing optimal decomposition parameters, and several intrinsic mode components are obtained. Then, an adaptive selection method based on the weight factor (WF) is presented to choose two intrinsic mode components that contain the principal fault characteristic information. Finally, the 1.5-dimension envelope spectrum of the selected intrinsic mode components is utilized to extract the compound fault characteristic information of vibration signals. The performance of the proposed method is demonstrated by using the simulation signal and the experimental vibration signals collected from a rolling bearing and a gearbox with compound faults. The analysis results suggest that the proposed method is not only capable of detecting compound faults of a bearing and a gearbox, but can separate the characteristic signatures of compound faults. The research offers a new means for the compound fault diagnosis of rotating machinery.

https://iopscience.iop.org/article/10.1088/0957-0233/27/7/075002/pdf

Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this article. Specifically, the labeled and unlabeled samples are first employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss. Finally, the local and discriminative objective functions are reconstructed under the semisupervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66 $\%$ accuracy with only 10 $\%$ of training labeled data, which indicates that it is a promising semisupervised intelligent fault diagnosis method.

Minping Jia

Papers

A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing

Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery