Study On The Key Technologies Of Prognostics And Health Management For Track Circuits

The faults from track circuits are key factors that can affect the efficiency of the railway transportation and cause safety accidents. Therefore, the accurate diagnosis, timely prognostics, scientific management of its faults has a significant meaning to the reliability and safety of train control system.According to the working principle and practical applications of track circuits, the fault formation mechanisms of track circuits are analysed in detail, and its fault types and corresponding symptoms are summarized. On this basis, using the open layered architecture of the prognostics and health management (PHM), an architecture of track circuits is built for PHM, the overall solution is proposed, and then a track circuit parameters acquisition and processing equipment is developed.The main innovation achievements of this dissertation are showed below:(1) An algorithm using the wavelet transformation is proposed for extracting fault symptoms of track circuits. As the traditional FFT algorithm cannot analyse non-stationary signal on time-frequency domain locally, an algorithm based on the multi-scale resolution of wavelet transformation is selected. The fault characteristic parameters of track circuits are got from signal with lot of impulse noises using the multilayer decomposition technique. As a result of comparing the simulations and the experiments, the db5wavelet base function is selected from three typical wavelet functions, the validity and accuracy of the algorithm is verified.(2)A model based on fuzzy neural network(FNN) is established for fault diagnosis and prognostics on track circuits. As the track circuit systems are non-linear without any accurate analytical model, with the combination of the advantages of fuzzy inference systems easily expressing knowledge and self-learning capabilities of neural networks, an algorithm using FFN model is proposed for fault diagnosis and prognostics. Two fuzzy operators without compensation parameters, Sum and Prod., are used for calculating the fault reliability and the rule activation degree respectively. Using the Sum_Prod. operators, the validity of this model is verified by computer simulation.(3) An improved algorithm based on fuzzy operators with compensation parameters is presented. Through the analysis of the aggregation performance of typical fuzzy operators, the Generalized Probability Sum-Generalized Probability Product (GPS-GPP) and the Generalized Weighted Average (GWA) operators are applied in FNN models to overcome the shortcomings. These shortcomings which come from fuzzy operators without parameters may cause the omission of input information and larger deviation of diagnosis and prognostics. The learning algorithms of these two models are derived according to the error Back Propagation(BP) and the gradient optimization methods. The comparison between the two algorithms with compensation parameters and the Sum-Prod.algorithm without parameters shows that the FNN fault diagnosis and prognostics model based on the fuzzy operators with compensation parameters has higher prediction accuracy, where more compensation parameters bring higher accuracy.