自适应乌鸦搜索算法改进深度置信网络的发动机故障诊断  

ENGINE FAULT DIAGNOSIS BASED ON DEEP BELIEF NETWORK IMPROVED BY ADAPTIVE CROW SEARCH ALGORITHM

作　　者：王亮[1] 唐明伟 WANG Liang;TANG MingWei(Department of Computer,Sichuan Top IT Vocational Institute,Chengdu 611743,China;School of Computer and Software Engineering,Xihua University,Chengdu 610039,China)

机构地区：[1]四川托普信息技术职业学院计算机系,成都611743 [2]西华大学计算机与软件工程学院,成都610039

出　　处：《机械强度》2023年第2期278-283,共6页Journal of Mechanical Strength

基　　金：四川省青年科学技术项目(sjzz2016-73)资助。

摘　　要：在乌鸦搜索算法(Crow Search Algorithm,CSA)的基础上,自适应乌鸦搜索算法(Adaptive CSA,ACSA)设计了感知概率和飞行距离这两个参数的自适应取值策略,有效增强了算法的性能。针对深度置信网络(Deep Belief Network,DBN)模型参数的选取对发动机故障诊断结果影响较大的实际,利用ACSA对其模型参数进行优化选取,并提出了ACSA改进DBN的发动机故障诊断方法。发动机故障诊断实例结果表明,ACSA算法能够获得更优的DBN模型参数,相比于其他几种方法,在耗时更少的情况下,获得了更高的发动机故障诊断精度。Based on the crow search algorithm(CSA),by the adaptive crow search algorithm(ACSA) the adaptive value strategy of sensing probability and flight distance,which effectively enhanced the performance of the algorithm is designed.In view of the fact that the selection of deep belief network(DBN) model parameters has great influence on the engine fault diagnosis results,ACSA is used to optimize the selection of its model parameters,and an engine fault diagnosis method based on DBN improved by ACSA is proposed.The Engine fault diagnosis example results show that the ACSA algorithm can obtain better DBN model parameters,and obtain higher engine fault diagnosis accuracy in less time than other methods.

关 键 词：深度置信网络 乌鸦搜索算法 自适应参数 发动机 故障诊断 

分 类 号：TK42[动力工程及工程热物理—动力机械及工程]