论文研究-改进的深层自动编码器网络在滚动轴承故障诊断中的应用

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Journal of Computer and Communications, 2018, 6, 41-53

http://www.scirp.org/journal/jcc

ISSN Online: 2327-5227

ISSN Print: 2327-5219

DOI:

10.4236/jcc.2018.67005 Jul. 30, 2018 41 Journal of Computer and Communications

Application of Improved Deep Auto-Encoder

Network in Rolling Bearing Fault Diagnosis

Jian Di, Leilei Wang

School of Control and Computer Engineering, North China Electric Power University, Baoding, China

Abstract

Since the effectiveness of extracting fault features is not high

under traditional

bearing fault diagnosis method, a bearing fault diagnosis method based on

Deep Auto-encoder Network

(DAEN) optimized by Cloud Adaptive Particle

Swarm Optimization (CAPSO) wa

s proposed. On the basis of analyzing

CAPSO and DAEN, the CAPSO-D

AEN fault diagnosis model is built. The

model uses the randomness and stability of CAPSO algorithm to optimize the

connection weight of DAEN,

to reduce the constraints on the weights and

extract fault features adaptively. Finally, efficient and accurate fa

ult diagnosis

can be implemented with the Softmax classifier.

The results of test show that

the proposed method has higher diagnostic accuracy and more stable diagno-

sis results than those based on the DAEN, Support Vector Machine (SVM)

and the Back Propagation algorithm (BP) under appropriate parameters.

Keywords

Fault Diagnosis, Rolling Bearing, Deep Auto-Encoder Network, CAPSO

Algorithm, Feature Extraction

1. Introduction

With the wide application of rolling bearings in various mechanical equipment,

the problem of mechanical equipment damage caused by faults has become in-

creasingly prominent, which affects the safety of equipment and personnel se-

riously [1]. Therefore, research on the fault diagnosis of rolling bearings is cru-

cial for the stable operation of mechanical equipment. Vibration analysis is the

most widely used method for detecting the health of mechanical equipment [2].

At present, there have been many studies on the diagnosis methods of bearing

faults [3] [4] [5]. The analysis methods based on vibration signals mainly include

noise method, oil sample analysis method, and vibration method. Hao

et al.

[6]

How to cite this paper:

Di, J. and Wang,

.L. (2018)

Application of Improved Deep

Auto

Encoder Network in Rolling Bearing

Fault Diagnosis

Journal of Computer and

Communications

, 41-53.

https://doi.org/10.4236/jcc.2018.67005

Received:

June 30, 2018

Accepted:

July 27, 2018

Published:

July 30, 2018

8 by authors and

Scientific

Research Publishing Inc.

This work is licensed under

the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

J. Di, L. L. Wang

DOI:

10.4236/jcc.2018.67005 42 Journal of Computer and Communications

proposed a multi-layer over-limit learning machine method to learn the fault vi-

bration time domain signal and diagnose the fault of the rolling bearing. Liu

al.

[7] proposed a method for extracting fault characteristics of rolling bearings

based on fault characteristic trend line template. According to the fault characte-

ristic trend line, the method finds the bearing fault characteristics and avoids the

shortcomings in the order tracking process. A rolling bearing fault feature ex-

traction method based on adaptive noise-based complete empirical mode de-

composition (CEEMDAN) combined with IMF sample entropy is used to adap-

tively decompose the vibration signal [8]. In order to extract the fault characte-

ristics of rolling bearings accurately and stably, Liu

et al.

[9] proposed a feature

extraction method based on variational mode decomposition and singular value

decomposition. However, the K value in this method needs to be given in ad-

vance, and the determination or range of other parameters is still lack of theo-

retical basis. The most critical part of the data-driven fault diagnosis method is

the extraction of bearing fault characteristics [10]. Due to the increasing number

of bearing equipment, the frequency of collecting samples is getting higher and

higher, which makes bearing faults fall into massive data problems. The method

adopted above requires a large amount of prior knowledge, rich theoretical

knowledge of signal processing and practical experience as a support in the

process of feature extraction. Moreover, the number of samples selected by the

fault feature is small, which cannot adequately reflect the potential information

of the bearing fault data, and reduces the accuracy of fault diagnosis. Therefore,

it is especially important to choose a suitable method for fault diagnosis of roll-

ing bearings.

In 2006, Professor Hinton proposed the idea of deep learning, which opened

up a wave of deep learning in academia and industry [11]. At present, deep

learning has also been applied in the field of mechanical failure. The advantage

of this method lies in the ability to mine representative information and sensitive

features from raw data. The convolutional neural network (CNN) is used to

learn the characteristics from the frequency data of the vibration signal [12], and

test the different performances of the feature learning from the original data, the

spectrum and the time-frequency domain combined data. The method can

adaptively learn features from frequency data and has higher diagnostic accura-

cy, but it is easy to fall into the local optimal solution. In order to combine the

power transformer online monitoring of dissolved gas analysis (DGA) data in

oil, Shi

et al.

[13] proposed a transformer fault diagnosis method based on

CDAEN, and compared with the traditional method, the deep self-encoding al-

gorithm can more vividly describe the potential of input data. Information and

effective extraction of fault characteristics, but this method is poorly stable. Deep

Auto-Encoder Network (DAEN) is a deep learning model and is widely used in

various fields [14] [15]. In this paper, cloud adaptive particle swarm optimiza-

tion (CAPSO) algorithm is used to optimize the depth self-encoding network,

and CAPSO-DAEN based fault diagnosis method for rolling bearings is pro-

posed. This method is based on the powerful computing of DAEN and combines

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