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Principal Components Analysis based Fault Detection and Isolation for Electronic Throttle Control system

机译:基于主成分分析的电子节气门控制系统故障检测与隔离

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In this paper, a Principal Component Analysis (PCA) based Fault Detection and Isolation (FDI) method for nonlinear Electronic Throttle Control (ETC) system is presented. The proposed method introduces a novel configuration of PCA bases by computing the absolute value of weights. The fault can be detected if the Sum Square Error (SSE) distance exceeds its pre-defined threshold and the isolation of the detected fault is done under the minimum of the SSE distance. The PCA model is used to detect (offline and/or online) failure in the ETC from the old Normal Operation Condition (NOC) as well as to diagnose the cause of the failure. A set of faults with armature resistance, armature inductance are evaluated to demonstrate the performance and effectiveness of the proposed method.
机译:本文提出了一种基于主成分分析(PCA)的非线性电子节气门控制(ETC)系统故障检测和隔离(FDI)方法。该方法通过计算权重的绝对值,引入了一种新颖的PCA基配置。如果总平方误差(SSE)距离超过其预定义的阈值,并且在SSE距离的最小值下完成检测到的故障的隔离,则可以检测到该故障。 PCA模型用于根据旧的正常运行条件(NOC)检测(离线和/或在线)ETC中的故障,并诊断故障原因。对一组具有电枢电阻,电枢电感的故障进行了评估,以证明所提出方法的性能和有效性。

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