Research On Fault Diagnosis Using Neural Network In Tilting Control System Of Titling Train

The tilting train is a way of high-speed railways suiting for the situation ofour railways and country, which has great economic potential. The tilting colltrolsystem is the key technique of tilting train, whose safety is very important fortilting train to transport normally. Fault diagnosis technology has provided a newmeans to enhance the reliability of system. Fault diagnosis of tilting controlsystem in tilting train is discussed in this thesis, which is the main substance ofdeveloping the tilting control system supported by the R&D Program of Ministryof Railways of China.Considering the fact that the tilting control system of tilting train is workingin quite an uncertain environment, a conventional fault diagnosis method is hardto perfOrm effectively. Neural network has been widely used in fault diagnosis inmany fields as a novel method or measure. However, there are very fewpublished studies dealing in neural network used in fault diagnosis of thelocomotive and rolling stock. Neural network has been systematically introducedinto fault diagnosis of tilting contro1 system in tilting train for the first time inour country by this thesis. Fault diagnosis theory based on neural network isstudied in detail. Some practical issues of the tilting control system design areaddressed by applying neural network.The main contributions are as follows.(1) In view of practice that the data obtained from accelerometer has seriouszero deviation, it is very difficult to diagnosis fault by hardware redundant. Afault diagnosis scheme for multi-accelerometers employing predictor based onradial basis function (RBF) neural network is proposed. The strategy eliminatesthe disturbance with zero deviation and improves the reliability of faultdiagnosis.(2) In the light of reality adopting double redundant gyroscope, the faultdiagnosis model for double redundant sensors taking the basic principle ofadaptive noise cleaner is established. The diagnosis method, recurrence algorithmand implement are given.(3) On the basis of the fuzzy property and correlation characteristics existingin the fault of tilting driving subsystem, several typical faults in tilting drivingsystem are analyzed and identified with the fiizzy neural network (FNN). The structure of FNN and the membership function are given. Meanwhile a fault discriminate method for threshold vector is addressed.(4) Since there is no perfect theoretical guide, the characteristics of orthogonal wavelet, semi-orthogonal wavelet and biorthogonal wavelet in signal decomposition and reconstruction are compared, and the conclusion of the study demonstrates that semi-orthogonal wavelet is a good wavelet for fault diagnosis in tilting control system of tilting train. A new approach is presented for deciding the fault threshold.(5) A generalized wavelet neural network is put forward by integrating wavelet analysis with neural network. A fault diagnosis method using wavelet packet as its pretreatment is proposed. The method using wavelet packet analysis is proposed to extract fault information from vibration signal obtained from testing jig of tilting train. The vector comprised of the energy of signal in all spectrum bands is input to a feed forward neural network. The trained neural network can explore the typical faults of tilting control system.Trial and research show that the fault diagnosis method based on neural network is practicable in terms of theory. A direction to set up the fault diagnosis of the locomotive and rolling stock, especially, high-speed train by means of neural network is pointed out. The research results in this thesis lay the theoretical foundation of fault diagnosis in tilting control system of the first tilting train of our country.