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This paper presents a fault diagnosis system for a sophisticated automotive embedded system. The latter is a Smart Distance Keeping system (SDK) which is an advanced adaptative cruise control implemented in a truck. The proposed diagnosis process is applied in a decentralized manner. To provide local diagnosis to one of the SDK electronic control unit, the approach based on the vertices principal...
Dynamic principal component analysis (DPCA) is required for the modeling and monitoring of dynamic processes. However, the root cause identification of faulty variables is quite desired after a fault is detected. As DPCA based methods construct detection indices in augmented variable space, it is difficult to use contribution analysis for diagnosis in a common way. In recent literature, reconstruction...
A fault identification algorithm of principal component space (PCS) information reconstruction based on T2 statistic have been proposed aiming at the defect of difficulty to effectively identify the fault, that qualitative diagnosis can only be implemented using traditional variable contribution rate and the data reconstruction method based on Q statistic ignores the fault information of PCS. The...
Process monitoring is critical for efficient operations of industrial processes. When a fault occurs, relevant measured data are affected by the fault, which leads to poor quality of products consequently. This paper proposes a new output-relevant index for detecting faults that affect the output or quality, and studies the fault detectability based on total projection to latent structures (T-PLS)...
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