A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds.

Mengyu Ji,Jun He,Sijue Li,Shaohui Liu,Gaoliang Peng,Zhao Chen

doi:10.3390/s21030675

Abstract

When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.

Highlights

With the development of science and technology, mechanical equipment is becoming more and more automatic and intelligent
Compared with the benign situation that the bearing rotates at constant speed, this paper addresses a much more challenging problem where the bearing has variable speed, which directly leads to changes in the distribution of vibration signal, and makes it more difficult to diagnose
This paper proposes a two-stage bearing fault diagnosis algorithm to realize the diagnosis of bearing fault data under different speed conditions

Summary

Introduction

With the development of science and technology, mechanical equipment is becoming more and more automatic and intelligent. When the bearing parts are damaged and fail, the precision of the equipment will decline rapidly; eventually there will be equipment failure and casualties. Bearing fault diagnosis is a hot research field of mechanical condition monitoring. The extraction of monitoring signals’ features and pattern classification are the core steps of bearing fault diagnosis. Among various kinds of monitoring signals, the vibration signal, which has the advantages of being easy to monitor and rich in information, is widely used in the field of mechanical condition monitoring. The frequency of the additional vibration has a certain relationship with the bearing speed (Equations (2)–(5)), which is called the fault characteristic frequency. When transforming the vibration signal to the frequency domain, the signal components with the fault characteristic frequency will have a large amplitude. By identifying the frequency components of the original vibration signal, we can identify where the bearing failed

Methods

Results

Discussion

Conclusion