The Status Recognition Method Of High Speed Train Based On Morphology

The security state of high-speed train in the process of long-term service will be time-varying. A large number of train vibration data collected by the sensors monitoring which contain a mass of train status information can reflect the running state of the train. The approach of extracting the characteristic parameters of high-speed train running state possesses great significance for the inversion and identification of high-speed train running state. Therefore, the status recognition methods based on the fractal and singular spectrum analysis of high speed train state have been proposed, and the characteristic parameters reflecting the running state information for the status recognition of the high-speed train have been extracted. The main work of the thesis are as follows:1. Combined with influence caused by the relationship between axle and wheel vibration of train and the track excitation, explored the feasibility of using periodic shock response to gain wheel tread flat of a railway vehicle; Through the analysis of High-speed rail vibration model of axle box for the work, a new lifting scheme named improved adaptive morphological gradient lifting wavelet (IAMGLW) is presented, which is Suitable for the vertical vibration signal of axle-box; The results showed that the proposed method can effectively deal with high speed railway axle box vibration signals. Compared to adaptive morphological gradient lifting wavelet (AMGLW), IAMGLW is more suitable for analyzing high-speed rail vibration data of axle box.2. High-speed train vibration propagation path, the working principle of the coil resistance shock absorber and the lateral damper are analyzed, to find suitable sensor channel for monitoring the performance change of those shock absorbers. come up with a approach which use morphology fractal to identify the gradient trends of those two absorbers, using fractal dimension as the characteristic to recognize operating state of the absorbers.3. Constructive Morphology Neural Network (CMNN) is a kind of new nonlinear neural network which is the combination of mathematical morphology and the traditional neural network model and has a strong practicability.Its training algorithm is derived according to the morphological associative memory, morphological operator is used to rank the test sample to the Box in the process of testing.But it is unable to classify this sample which is out of the Box into correct group, someone presents a morphology neural network based on fuzzy lattice (FL-CMNN), this algorithm can improve effect of Classification by calculating the membership grade sample of sample and BOX, But the complexity of the algorithm is increased and the result is unstable. in this paper a kind of lifting algorithm based on morphological neural network (LCMNN) has been presented, This algorithm inherits the quick operation speed of original morphological operator and is able to classify this sample which is out of the Box into correct group.Numerical experiments show that LCMNN has the best effect of Classification and simple, less computation time which compared with several other algorithms.This work is supported by National Natural Science key Foundation of China. (No.61134002)...