Fault diagnosis of rolling bearing based on permutation entropy and Extreme Learning Machine

A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

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Multifault Diagnosis for Rolling Element Bearings Based on Intrinsic Mode Permutation Entropy and Ensemble Optimal Extreme Learning Machine

Advances in Mechanical Engineering ◽

10.1155/2014/803919 ◽

2014 ◽

Vol 6 ◽

pp. 803919 ◽

Cited By ~ 9

Author(s):

Jianzhong Zhou ◽

Jian Xiao ◽

Han Xiao ◽

Weibo Zhang ◽

Wenlong Zhu ◽

...

Keyword(s):

Extreme Learning Machine ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Rolling Element Bearings ◽

Bearing Fault Diagnosis ◽

Bee Colony ◽

Mode Decomposition ◽

Rolling Element ◽

Learning Machine

This paper presented a novel procedure based on the ensemble empirical mode decomposition and extreme learning machine. Firstly, EEMD was utilized to decompose the vibration signals into a number of IMFs adaptively and the permutation entropy of each IMF was calculated to generate the fault feature matrix. Secondly, a new extreme learning machine was proposed by combining ensemble extreme learning machine and the evolutionary extreme learning machine which used an artificial bee colony algorithm to optimize the input weights and hidden bias. The proposed diagnosis algorithm was applied on the three rolling bearing fault diagnosis experiments. The numerical experimental results demonstrated that the proposed method had an improved generalization performance than traditional extreme and other variants.

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Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine

Mechanism and Machine Theory ◽

10.1016/j.mechmachtheory.2015.03.014 ◽

2015 ◽

Vol 90 ◽

pp. 175-186 ◽

Cited By ~ 124

Author(s):

Ye Tian ◽

Jian Ma ◽

Chen Lu ◽

Zili Wang

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Rolling Bearing ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Learning Machine

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Fault Diagnosis of Rolling Bearing Based on Wavelet Packet and Extreme Learning Machine

Lecture Notes in Electrical Engineering - Proceedings of 2017 Chinese Intelligent Systems Conference ◽

10.1007/978-981-10-6496-8_13 ◽

2017 ◽

pp. 131-140

Author(s):

Xiaoquan Tang ◽

Yi Chai ◽

Yongfang Mao ◽

Junjie Ji

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Wavelet Packet ◽

Rolling Bearing ◽

Learning Machine

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A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

Mathematical Problems in Engineering ◽

10.1155/2016/5432648 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13 ◽

Cited By ~ 9

Author(s):

Guiji Tang ◽

Xiaolong Wang ◽

Yuling He

Keyword(s):

Fault Diagnosis ◽

Wavelet Packet ◽

Rolling Bearing ◽

Wavelet Packet Transform ◽

Permutation Entropy ◽

Feature Vectors ◽

Novel Method ◽

Complex Wavelet ◽

Learning Machine ◽

Low Dimensional

A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT), the improved multiscale permutation entropy (IMPE), and the linear local tangent space alignment (LLTSA) with the extreme learning machine (ELM), is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

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