《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

Detection strategy optimization of large equipment group based on improved MOPSO algorithm

Published:2024-03-26 author:ZHANG Jiwang, LIU Suo, GONG Shu, et al. Browse: 137 Check PDF documents

Detection strategy optimization of large equipment group based on

improved MOPSO algorithm

ZHANG Jiwang1, LIU Suo2, GONG Shu2, LIU Yue2, DING Keqin1

(1.China Special Equipment Inspection and Research Institute, Beijing 100029, China; 2.Tianjin fabrication

company, China Offshore Oil Engineering Co.,Ltd., Tianjin 300461, China)

Abstract: Aiming at the problem that the optimal detection strategy under multi-objective constraints such as tight testing schedules, high requirements for testing quality and personnel safety, and maximization of economic benefits in the inspection and testing of large crawler crane group(large equipment groups),an optimization method based on improved multi-objective particle swarm optimization (MOPSO)was proposed. Firstly, the traditional multiobjective particle swarm optimization algorithm was improved using mutation operators. Then, a multi-objective optimization model for testing duration, testing cost, testing quality, and safety was constructed based on the actual needs of large-scale equipment group testing projects, and the constraints of each sub objective were determined. Finally, the optimization algorithm and constructed model were applied to the detection project of a large crawler crane group to verify the effectiveness of the method. The research results show that the optimal detection strategy obtained using the method, comparing with traditional detection strategies, only requires 3/4 of the detection time while ensuring quality and safety. Meanwhile, the testing eunitcoste and the inspected eunitcoste can respectively save 14% and 32.8%. It greatly improved the detection efficiency, reduces the human, economic, and time costs of enterprises and inspection units, and the method has good practicality and promotion application value.

Key words: equipment group; large crawler crane; multi-objective particle swarm optimization(MOPSO); detection strategy optimization; particle swarm; optimization algorithm

Advanced Search

Online Office System

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