Fault Diagnosis About Asynchronous Traction Motor Of HXD1Electric Locomotive

As trains become speedier and more information-based, the safety of the railway is also more prominent. HXD1AC drive electric locomotive is a heavy freight one developed by China’s CSR Zhuzhou Electric Locomotive Co., Ltd. and Siemens. As the core equipment of the AC electric locomotives, AC traction motors’safe operation is essential to the operation of the entire train, so fault diagnosis of AC traction motor is of great practical significance.In this paper, the state of motor fault diagnosis research, the signal analysis methods and traction motor fault diagnosis mechanism are researched and analyzed to provide the theory basis. For the special structure and operating environment of traction motor, several new-effective traction motor fault diagnosis methods are proposed.Considering that it is difficult to establish an accurate fault status mathematical model as the traction motor is nonlinear multivariable system with strong coupling, a shuffled frog leaping algorithm ridgelet neural network fault diagnosis method for traction motor is proposed. Firstly, the ridgelet neural network is used to approximate the nonlinear part of traction motor, and establish the neural network observer, and then the shuffled frog leaping algorithm is used to optimize the ridgelet neural network parameters, and the optimal design method is used to select the feedback gain matrix of observer, finally the residual of observer is used for traction motor fault diagnosis. With good learning ability, high fault diagnosis precision and fast convergence speed, this method not only combines the advantages of shuffled frog leaping algorithm and ridgelet neural network, but can fuse well with speed sensorless traction motor vector control, and detect the traction motor faults and identify the motor states at the same time.In variable frequency speed regulation system of HXD1electric locomotive, the fault characteristic quantity generated in the stator current because of traction motor broken bar fault and stator inter-turn short circuit of asynchronous will pass through the converter to converter primary side (network side) and influence the current of primary side, therefore the easily measured current signal of the network side in traction converter can be used for motor fault diagnosis. A novel fault diagnosis based on the rectifier side instantaneous power of traction motor is proposed in this paper. At first, we analyze the stator current and the primary-side current, and then build the instantaneous power. By analyzing instantaneous power spectrum, we select the characteristic frequency2Sf0as broken rotor bars fault diagnostic criterion. The method overcomes the problems of the fault characteristic frequency covered easily by fundamental frequency in the traditional method of current analysis, and simplifies the hardware structure of fault diagnosis system by selecting the network side signal.A variety fault classification of traction motor fault diagnosis is proposed based on Kernel primary component analysis (KPCA) and Relevance vector machine (RVM).This method uses the sampling current signal with wavelet analysis to construct the learning sample vectors, and then uses the KPCA to reduce dimension, and the new fault characteristic vectors are inputed RVM to carry out training and fault classification, finally,traction motor faults diagnosis achieve more satisfactory results by the use of RVM.In HXD1electric locomotive traction motor vector control system, rotor field-oriented inaccuracy is one of the key problems affecting electric locomotive performance. We comprehensively analyze the fault diagnosis, rotor field orientation and speed identification, and find that instantaneous reactive power can be used not only for traction motor rotor fault diagnosis, but also for field oriented control and speed identification. Firstly, we constructe the instantaneous reactive power through vector cross product with the rotor EMF and current, and select the characteristic frequency of2Sf1as the rotor broken bar fault diagnosis criterion by analyzing the instantaneous reactive power spectrum. And then we use the instantaneous reactive power to identify speed, eliminating the effect of traditional MRAS speed identification of stator flux linkage equations of the stator resistance and the integral effect. Finally, we adjust rotor magnetic field oriention using PI with the difference of two reactive power models. The method, as it selects the instantaneous reactive power to detect traction motor fault, identifies the speed and corrects the rotor flux orientation, not only can effectively diagnose traction motor broken rotor bar fault, but also can integrate the speed sensorless vector control technology to improve the control performance of the system.Hardware in loop(HIL) simulation platform of AC drive of traction motor is developed.The software of speed sensorless traction motor vector control system and online fault diagonosis system are established based on the platform.