Robustness Assessment and Adaptive FDI for Car Engine

作     者：Mahavir Singh Sangha J.Barry Gomm 

作者机构：Control Systems Research GroupSchool of EngineeringLiverpool John Moores University 

出 版 物：《International Journal of Automation and computing》 (国际自动化与计算杂志（英文版）)

年 卷 期：2008年第5卷第2期

页      面：109-118页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 081002[工学-信号与信息处理] 

基　　金：This work was supported by Universities UK Faculty of Technology and Environment and School of Engineering Liverpool John Moores University UK 

主　　题：On-board fault diagnosis automotive engines adaptive neural networks (ANNs) fault classification robustness assessment 

摘      要：A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.