Real-time Fault Diagnosis and Fault-tolerant Estimation of Temperature Sensor in Train Traction Drive System
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Graphical Abstract
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Abstract
The temperature of traction drive system is an important index for the system operation status monitoring and protection decision-making. To solve the problems of missing diagnosis and false alarm in the existing fault diagnosis methods, an intelligent fault diagnosis method based on information fusion is proposed for multi-temperature sensors in traction drive system. Firstly, according to the temperature sampling mechanism of the traction drive system, a method for estimating the sampling values is proposed for the water temperature sensors and the oil temperature sensors based on the dynamic principal component analysis(PCA) algorithm. Then based on the variation law of residual between the sampled value and estimated value of each temperature sensor, a method of fault detection and fault isolation is proposed. Finally, the actual train operation data is used to verify the proposed algorithm. The experimental results show that compared with the existing methods, the proposed method can not only improve the diagnosis accuracy, but also estimate the actual temperature when the temperature sensor fails, which effectively improves the availability of the train and has good practical value.
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