基于离散粒子群优化算法的多值属性系统故障诊断策略  

Fault Diagnosis Strategy for Multi-valued Attribute System Based on a Discrete Particle Swarm Optimization Algorithm

作　　者：田恒 许荣滨 姜艳红 张文虎 邓四二[1] TIAN Heng;XU Rongbin;JIANG Yanhong;ZHANG Wenhu;DENG Si er(School of Mechatronics Engineering,Henan University of Science and Technology,Luoyang 471003,Henan,China;Zhejiang XCC Group Co.,Ltd.,Shaoxing 312500,Zhejiang,China;Zhongzhe High-speed Railway Bearing Co.,Ltd.,Quzhou 324407,Zhejiang,China)

机构地区：[1]河南科技大学机电工程学院,河南洛阳471003 [2]浙江五洲新春集团股份有限公司,浙江绍兴312500 [3]中浙高铁轴承有限公司,浙江衢州324407

出　　处：《兵工学报》2022年第12期3240-3246,共7页Acta Armamentarii

基　　金：国家自然科学基金项目(52105182、51905152);河南省高等学校重点科研项目(21A460011);河南省科技攻关项目(222102240050)。

摘　　要：针对传统离散粒子群优化(PSO)算法仅能搜索多值属性系统(MVAS)最小完备测试集的问题,通过重塑离散PSO算法,提出一种测试序列寻优算法—PSO-测试(TS)算法。在多值D矩阵和五元组的基础上,公式化处理MVAS的诊断策略。重塑离散粒子群的过程,将离散PSO算法与MVAS的故障诊断策略融合。设置PSO-TS算法的自身认知和社会知识阶段的计算规则,并通过引入交换序提升PSO-TS算法中粒子的多样性。采用实例和随机仿真实验验证PSO-TS算法。研究结果表明:与MV-Rollout和MV-IG算法相比,PSO-TS算法的期望测试费用少,能够获得较优的诊断策略,但是运行时间较长。To solve the problem that the traditional discrete particle swarm optimization(DPSO)algorithm can only find the minimum complete test set for a multivalued attribute system(MVAS),particle swarm optimization for test sequencing(PSO-TS)algorithm is proposed.The diagnosis strategy for MVAS is formulated based on multi-valued D matrix and five-tuple.The implementation process of DPSO is remodeled,and DPSO algorithm is combined with the fault diagnosis strategy of MVAS.Subsequently,a set of calculation rules for self-cognition and the social knowledge are set,and the exchange order is introduced to increase particle diversity.The PSO-TS algorithm is verified using experiments and stochastic simulations.Compared with the MV-Rollout algorithm and MV-IG algorithm,the PSO-TS algorithm can obtain an optimal fault diagnosis strategy with a relatively lower expected test cost,but with a longer running time.

关 键 词：多值属性系统 离散粒子群优化算法 诊断策略 序贯诊断 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]