A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field.

Bin Han,Fengtong Wu,Ming Sun,Hui Zhang

doi:10.3390/s21227762

Abstract

Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.

Highlights

IntroductionMachines are a key part of the production process
Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers
The bearing failures were set to single-point damage from EDM. This dataset was designed with three damage types: Outer ring failure (OF), rolling element failure (RF), and inner ring failure (IF)

Summary

Introduction

Machines are a key part of the production process. Failures of these machines can cause significant economic losses and sometimes pose a threat to the people who use them. More than 50% of mechanical defects are related to bearing failures [1,2]. Better and smarter bearing health-monitoring techniques are becoming an increasingly critical part of guaranteeing the proper and reliable operation of machines [3]. In order to obtain information about possible internal failures of a machine while it is working, one can only determine the internal state by analyzing the relevant external information. Through the literature on measurement science, the most useful and primary tool in the diagnosis of rolling bearing faults is the raw vibration signal [4,5]

Methods

Results

Conclusion