Abstract
Taking the traction motor of CRH2 high-speed train as the research object, this paper proposes a diagnosis method based on random forest and XGBoost for the compound fault resulting from stator interturn short circuit and air gap eccentricity. First, the U-phase and V-phase currents are used as fault diagnosis signal and then the Savitzky–Golay filtering method is used for the noise deduction from the signal. Second, the wavelet packet decomposition is used to extract the composite fault features and then the high-dimensional features are optimized by the principal component analysis (PCA) method. Finally, the random forest and XGBoost are combined to detect composite faults. Using the experimental data of CRH2 semiphysical simulation platform, the diagnosis of different fault modes is completed, and the high diagnosis accuracy is achieved, which verifies the validity of this method.
Highlights
Traction motor is one of the key components of the drive system in CRH2 high-speed train [1]
Static air gap eccentricity exists more or less in the motors in engineering practice, so when the motor stator windings are shortcircuited, it is equivalent to the compound fault of stator interturn short circuit and air gap eccentricity. e traction motor of high-speed railway is installed on the bogie of the train
The experimental results of three parts in the process of compound fault diagnosis are introduced. e data used are from the semiphysical simulation experimental platform of Zhuzhou Electric Locomotive Research Institute. e first part is the signal noise deduction based on the Savitzky–Golay filtering (SG) filter. e second part is fault feature extraction based on wavelet packet and principal component analysis (PCA). e third part is fault diagnosis based on random forest and XGBoost
Summary
Traction motor is one of the key components of the drive system in CRH2 high-speed train [1]. Many genetic algorithms have been studied and proposed where the method can handle various fault types [11, 12] using multiobjective optimization methods Motivated by these observations, a multiphase diagnosis method based on random forest and XGBoost for the compound fault of stator interturn short circuit and air gap eccentricity is proposed in this paper. By changing the window width, the SG filter is applied to the noise reduction smoothing of threephase current signal to reduce the noise interference and facilitate the extraction of fault features in frequency domain [16, 17]. E SG filtering method is used to preprocess the threephase current of the motor [20], which can effectively reduce noise interference and enhance the discrimination between fault signals and normal signals in the frequency domain, so that the accuracy of feature extraction of compound fault can be improved
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