期刊
SENSORS
卷 10, 期 1, 页码 241-253出版社
MDPI
DOI: 10.3390/s100100241
关键词
multi-fault diagnosis; principal component analysis; signal reconstruction; fault detection; fault isolation
资金
- National Natural Science Foundation of China [50775136]
- Shanghai Municipal Education Commission [10ZZ97, 09YZ248]
A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time.
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