{"title":"基于PSO-SVM的航空发动机气路故障诊断","authors":"Jin Chi, Yuanfang Liu, D. Luo, Langcai Cao","doi":"10.1109/SAFEPROCESS45799.2019.9213257","DOIUrl":null,"url":null,"abstract":"Aiming at the high incidence of faults and high maintenance cost of aero-engine gas path components, this paper adopts the condition-based maintenance mode and introduces the fault diagnosis method combining particle swarm optimization (PSO)with support vector machine(SVM). Firstly, the fault diagnosis method of aero-engine gas path based on SVM is proposed. Then, kernel function parameters and penalty coefficients of SVM are optimized by PSO. Finally, the aero-engine gas path fault diagnosis model based on PSO-SVM is established. The simulation results show that the design method of fault diagnosis of aero-engine Gas path based on PSO-SVM has advantages of short prediction time, high prediction efficiency and higher accuracy than the traditional diagnosis method based on SVM, reached 100%. Moreover, the effect of PSO on kernel function parameters and penalty coefficients is better than that of other optimization algorithms.","PeriodicalId":353946,"journal":{"name":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fault Diagnosis of Aero-engine Gas Path Base on PSO-SVM\",\"authors\":\"Jin Chi, Yuanfang Liu, D. Luo, Langcai Cao\",\"doi\":\"10.1109/SAFEPROCESS45799.2019.9213257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the high incidence of faults and high maintenance cost of aero-engine gas path components, this paper adopts the condition-based maintenance mode and introduces the fault diagnosis method combining particle swarm optimization (PSO)with support vector machine(SVM). Firstly, the fault diagnosis method of aero-engine gas path based on SVM is proposed. Then, kernel function parameters and penalty coefficients of SVM are optimized by PSO. Finally, the aero-engine gas path fault diagnosis model based on PSO-SVM is established. The simulation results show that the design method of fault diagnosis of aero-engine Gas path based on PSO-SVM has advantages of short prediction time, high prediction efficiency and higher accuracy than the traditional diagnosis method based on SVM, reached 100%. Moreover, the effect of PSO on kernel function parameters and penalty coefficients is better than that of other optimization algorithms.\",\"PeriodicalId\":353946,\"journal\":{\"name\":\"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213257\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAFEPROCESS45799.2019.9213257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fault Diagnosis of Aero-engine Gas Path Base on PSO-SVM
Aiming at the high incidence of faults and high maintenance cost of aero-engine gas path components, this paper adopts the condition-based maintenance mode and introduces the fault diagnosis method combining particle swarm optimization (PSO)with support vector machine(SVM). Firstly, the fault diagnosis method of aero-engine gas path based on SVM is proposed. Then, kernel function parameters and penalty coefficients of SVM are optimized by PSO. Finally, the aero-engine gas path fault diagnosis model based on PSO-SVM is established. The simulation results show that the design method of fault diagnosis of aero-engine Gas path based on PSO-SVM has advantages of short prediction time, high prediction efficiency and higher accuracy than the traditional diagnosis method based on SVM, reached 100%. Moreover, the effect of PSO on kernel function parameters and penalty coefficients is better than that of other optimization algorithms.