Rolling bearing fault diagnosis based on probabilistic mixture model and semi-supervised ladder network

Xu Ding,Dong Wang,Xuesong Lu,Hua Zhai,Qingzhou Lv

doi:10.1177/1687814020977748

Abstract

Fault diagnosis of rolling bearings is of great significance to ensure the production efficiency of rotating machinery as well as personal safety. In recent years, machine learning has shown great potential in signal feature extraction and pattern recognition, and it is superior to traditional fault diagnosis methods in dealing with big data. However, most of the current intelligent diagnostic methods are based on the ideal conditions that bearing data set and label information are sufficient, which are often not always available in engineering practice. In response to this problem, this paper proposes to use probabilistic mixture model (PMM) to approximate the data distribution of the bearing signal, and then use Markov Chain Monte Carlo (MCMC) algorithm to sample the probabilistic model to expand the fault data set. In addition, Semi-supervised Ladder Network (SSLN) can achieve the effect of supervised learning classifier with only a few labeled samples. Based on Case Western Reserve University (CWRU) Bearing Database, the recognition accuracy of the proposed PMM-SSLN model can reach 99.5%, and the experimental results show that this model is applicable to the case where both bearing data set and label information are insufficient.

Highlights

Rolling bearings are the most important and most vulnerable component of the rotating machinery and widely used in machinery industry.[1]
To overcome the above weaknesses, this paper proposes probabilistic mixture model (PMM)-Semisupervised Ladder Network (SSLN) framework based on Probability Mixture Model and Semi-supervised Ladder Network
The rolling bearing fault diagnosis method based on PMM-SSLN Model consists of three steps: (1) Data acquisition and augmentation of the bearing signal model: First collect bearing signals from the experimental device, and use PMM to fit the distribution of bearing data, in order to establish the probability models for bearing data under different working conditions

Summary

Introduction

Rolling bearings are the most important and most vulnerable component of the rotating machinery and widely used in machinery industry.[1]. Keywords Rolling bearing, fault diagnosis, probabilistic mixture model, MCMC, semi-supervised ladder network To overcome the above weaknesses, this paper proposes PMM-SSLN framework based on Probability Mixture Model and Semi-supervised Ladder Network.

Results

Conclusion