Fault diagnosis of rolling element bearing using Naïve Bayes classifier

The development of machine learning brings a new way for diagnosing the fault of rolling element bearings. However, the method in machine learning with high accuracy often has the poor ability of generalization due to the overuse of feature engineering. To address this challenge, Naive Bayes classifier is applied in this paper. As the one of the cluster of Bayes classifiers, its ability of classification is very outstanding. In this paper, the method is provided with a detailed description for why and how to diagnose the fault of bearing. Finally, an evaluation of the performance of Naive Bayes classifier is presented with real world data. The evaluation indicates that Naive Bayes classifier can achieve a high level of accuracy without any feature engineering.

[1]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[2]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[3]  Jing Lin,et al.  Feature Extraction Based on Morlet Wavelet and its Application for Mechanical Fault Diagnosis , 2000 .

[4]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[5]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[6]  Jian Pei,et al.  A brief survey on sequence classification , 2010, SKDD.

[7]  Li Weihua,et al.  A Firefly Neural Network and Its Application in Bearing Fault Diagnosis , 2015 .

[8]  Yixiang Huang,et al.  A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion , 2010, Expert Syst. Appl..

[9]  Junyan Yang,et al.  Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension , 2007 .

[10]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[11]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[12]  Jian-Jiun Ding,et al.  Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine , 2012, Entropy.

[13]  Keith C. C. Chan,et al.  A Feature Extraction Method for Multivariate Time Series Classification Using Temporal Patterns , 2015, PAKDD.

[14]  Christian P. Robert,et al.  Machine Learning, a Probabilistic Perspective , 2014 .

[15]  Tao Liu,et al.  Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis , 2015 .

[16]  H. Muro,et al.  Failure analysis of rolling bearings by X-ray measurement of residual stress , 1973 .

[17]  Yuan Li,et al.  Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study , 2017 .

[18]  S. Mallat.  VI – Wavelet zoom , 1999 .