《机电工程》杂志社科技期刊核心期刊论文投稿机电一体化机械、仪表电工自动化、计算机浙江大学主办

Introduction

International Standard Serial Number:

ISSN 1001-4551

Sponsor:

Zhejiang University;

Zhejiang Machinery and Electrical Group

Edited by:

Editorial of Journal of Mechanical & Electrical Engineering

Chief Editor:

ZHAO Qun

Vice Chief Editor:

TANG ren-zhong,

LUO Xiang-yang

Tel:

86-571-87041360,87239525

Fax:

86-571-87239571

Add:

No.9 Gaoguannong,Daxue Road,Hangzhou,China

P.C:

310009

E-mail:

meem_contribute@163.com

IMRPE and AO-SVM in reciprocating compressor fault identification

Published:2023-12-26 author:LI Zhanfeng, ZHANG Junchang. Browse: 1026 Check PDF documents

IMRPE and AOSVM in reciprocating compressor fault identification

LI Zhanfeng1, ZHANG Junchang2

(1.Department of Mechanical Engineering, Yantai Vocational College, Yantai 264670, China; 2.College of Mechanical

and Electronic Engineering, Northwest A&F University, Yangling 712100, China)

Abstract: Aiming at the problem that the conventional fault diagnosis method is not suitable for extracting the fault characteristics of the sound signal of the reciprocating compressor, and the fault identification accuracy of the reciprocating compressor is not high. A reciprocating compressor fault diagnosis method based on improved multiscale reverse permutation entropy (IMRPE), t-distribution stochastic neighborhood embedding (t-SNE) and aquila optimizer (AO) optimized support vector machine (SVM) was proposed. Firstly, IMRPE method with excellent feature representation performance was used to extract fault information of reciprocating compressor sound signal, and fault feature vector reflecting sample fault feature attributes was constructed. Then, t-SNE method was used to reduce the dimension of fault features dimension to reduce the dimension of fault features and remove redundant features, so as to obtain low-dimensional sensitive features. Finally, AO method was used to adaptively search the penalty coefficients and kernel parameters of SVM classifier, so as to establish a classifier with optimal structural parameters, and input the low dimensional sensitive fault features into the AO-SVM classifier for training and classification, and the fault identification of the test samples was completed according to the output label of the test samples. Taking the acoustic signal fault data of reciprocating compressor as the object of study, and the effectiveness of the IMRPE-t-SNE-AO-SVM method were evaluated. The research results show that the proposed fault diagnosis method can not only accurately and stably identify the fault types of reciprocating compressors, improve the accuracy of fault identification, but also outperform the comparison methods in terms of accuracy and stability.

Key words: compressor; fault diagnosis; improved multiscale reverse permutation entropy(IMRPE); tdistribution stochastic neighborhood embedding(t-SNE); aquila optimizer optimized support vector machine(AOSVM)

Advanced Search

Online Office System

Chinese Core Periodicals
Chinese Sci-tech Core Periodicals
SA, INSPEC Indexed
CSA: T Indexed
UPD:Indexed