《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

Centrifugal compressor fault warning method based on attention mechanism and XBOA-Bi-LSTM

Published:2024-03-26 author:YUAN Zhenhua, MAO Dajun, LI Yuzhen. Browse: 118 Check PDF documents

Centrifugal compressor fault warning method based on

attention mechanism and XBOA-Bi-LSTM

YUAN Zhenhua1, MAO Dajun1, LI Yuzhen2

(1.College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;

2.Shanghai Chang Geng Information Technology Co.,Ltd., Shanghai 201209, China)

Abstract: Aiming at the complex operating conditions, high maintenance costs, and harsh working environment of long-distance pipelines faced by centrifugal compressors, a fault warning method for centrifugal compressors based on attention mechanism (AM) and butterfly algorithm optimized bidirectional short-term and short-term memory neural network (XBOA-Bi-LSTM) was proposed. Firstly, in response to the problems of slow convergence speed, single conversion probability, and easy falling into local optima in traditional butterfly algorithms, infinite folding iterative aliasing mapping was introduced to enrich the initial population of butterfly algorithms. At the same time, an adaptive inertia conversion probability based on population dispersion and iteration number was proposed to improve the optimization ability of the butterfly algorithm. Then, the grey correlation analysis method was used to extract features from the measurement point data, and the grey correlation coefficient was assigned to the input sequence using attention mechanism. Finally, a bidirectional long-term and short-term memory neural network fault warning model was established, and the fault warning of centrifugal compressors was completed through simulation experiments. The feasibility of the fault warning method for a centrifugal compressor in a longdistance natural gas pipeline unit was verified using the centrifugal compressor as the simulation object.The experiment results prove that when using the attention mechanism and XBOA-Bi-LSTM based centrifugal compressor fault warning method, a warning signal is issued within 2h~3h before the centrifugal compressor fault occurred, achieving fault warning for abnormal pressure difference of the inlet filter and abnormal operation of the support bearing of the centrifugal compressor.

Key words: centrifugal compressors; butterfly optimization algorithm(BOA); grey relation analysis(GRA); attention mechanism(AM); bidirectional long short-term memory neural network(Bi-LSTM); fault feature extraction

Advanced Search

Online Office System

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