基于多级DSN的船舶柴油机故障在线诊断  

On-Line Fault Diagnosis of a Marine Diesel Engine Based on Multi-Level DSN

作　　者：陈智君[1,2] 吴萌萌[1] 王忠俊 袁强[1] 余永华[1,2] Chen Zhijun;Wu Mengmeng;Wang Zhongjun;Yuan Qiang;Yu Yonghua(School of Naval Architecture,Ocean and Energy Power Engineering,Wuhan University of Technology,Wuhan 430063,China;Key Laboratory of High Performance Ship Technology(Wuhan University of Technology),Ministry of Education,Wuhan 430063,China)

机构地区：[1]武汉理工大学船海与能源动力工程学院,湖北武汉430063 [2]高性能船舶技术教育部重点实验室(武汉理工大学),湖北武汉430063

出　　处：《内燃机学报》2022年第4期371-377,共7页Transactions of Csice

基　　金：工信部高技术专项资助项目(工信部联装函[2019]360号).

摘　　要：针对柴油机多工况下燃烧室部件故障难以辨别的问题,基于Z6170ZICZ-1型柴油机的缸盖振动信号,提出了一种多级深度堆栈网络(DSN)故障诊断算法,并进行了在线验证.首先,分析了DSN的构建流程,结合多级诊断的思想,构建了多级DSN故障诊断模型来对多种工况下不同的故障和故障程度进行识别,并且基于准确率完成了DSN算法与经典的极限学习机(ELM)和支持向量机(SVM)算法对比,结果表明:构建多级DSN诊断模型具有更好的分类效果.最后,基于dSPACE平台设计了船舶柴油机燃烧室部件在线故障诊断系统,验证故障诊断算法的可行性和有效性.Aiming at the difficulty to identify the faults of combustion chamber components in diesel engines under multiple working conditions,a multi-level deep stack network(DSN)fault diagnosis algorithm was proposed and verified on-line on Z6170ZICZ-1 diesel engine based on the vibration signal of the cylinder head.Firstly,the construction process of the DSN was analyzed.Combined with the idea of multi-level diagnosis,a multi-level DSN fault diagnosis model was constructed to identify different faults and the degree of faults under various working conditions.Then,a comparison between DSN algorithm and classical extreme learning machine(ELM)and support vector machine(SVM)algorithm was completed based on the accuracy.The experimental results show that the multi-level DSN diagnostic model has better classification effect.Finally,an on-line fault diagnosis system for the marine diesel combustion chamber components was designed based on dSPACE platform and the feasibility and effectiveness of the fault diagnosis algorithm were verified.

关 键 词：船舶柴油机 振动信号 多级深度堆栈网络 在线故障诊断 

分 类 号：TK428[动力工程及工程热物理—动力机械及工程]