Investigation Techniques for Rolling Bearing Fault Diagnosis Using Machine Learning Algorithms

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

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Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

Energies ◽

10.3390/en14165150 ◽

2021 ◽

Vol 14 (16) ◽

pp. 5150

Author(s):

Shiza Mushtaq ◽

M. M. Manjurul Islam ◽

Muhammad Sohaib

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Fault Diagnosis ◽

Learning Algorithms ◽

Data Driven ◽

Support Vector ◽

Comprehensive Review ◽

Bearing Fault ◽

Bearing Fault Diagnosis

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented.

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Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network

Applied Sciences ◽

10.3390/app10030770 ◽

2020 ◽

Vol 10 (3) ◽

pp. 770 ◽

Cited By ~ 1

Author(s):

Guoqiang Li ◽

Chao Deng ◽

Jun Wu ◽

Zuoyi Chen ◽

Xuebing Xu

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Wavelet Packet ◽

Rolling Bearing ◽

Wavelet Packet Transform ◽

Fault Classification ◽

Bearing Fault ◽

Bearing Fault Diagnosis

Timely sensing the abnormal condition of the bearings plays a crucial role in ensuring the normal and safe operation of the rotating machine. Most traditional bearing fault diagnosis methods are developed from machine learning, which might rely on the manual design features and prior knowledge of the faults. In this paper, based on the advantages of CNN model, a two-step fault diagnosis method developed from wavelet packet transform (WPT) and convolutional neural network (CNN) is proposed for fault diagnosis of bearings without any manual work. In the first step, the WPT is designed to obtain the wavelet packet coefficients from raw signals, which then are converted into the gray scale images by a designed data-to-image conversion method. In the second step, a CNN model is built to automatically extract the representative features from gray images and implement the fault classification. The performance of the proposed method is evaluated by a real rolling-bearing dataset. From the experimental study, it can be seen the proposed method presents a more superior fault diagnosis capability than other machine-learning-based methods.

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