EMD generalized energy-based wheeltrack vibration signal fault feature extraction method

Abstract

The invention discloses an EMD generalized energy-based wheeltrack vibration signal fault feature extraction method which comprises the following steps: collecting a vibration acceleration signal of a real-time running train, integrating the train speed to determine the starting and stopping moments corresponding to one revolution of the wheel, and intercepting the acceleration signal of corresponding time history by using the starting and stopping moments; carrying out wavelet decomposition, threshold processing of each layer of wavelet coefficient and wavelet reconstruction on the collected vibration acceleration signal to realize wavelet denoising; carrying out empirical mode decomposition on the obtained axle box vibration acceleration signal to obtain a series of intrinsic mode functions; finally determining the energy weight coefficient by combining the vibration acceleration signal under fault excitation simulated by a vehicle track coupling kinetic model, calculating the empirical mode decomposition generalized energy and determining the fault feature according to the value. The EMD generalized energy-based wheeltrack vibration signal fault feature extraction method has the advantages of being low in cost, and high in feature extraction resolution ratio and real-time performance.

Description

technical field [0001] The invention relates to the technical field of in-transit monitoring and safety early warning of urban rail trains, in particular to a method for extracting fault features of wheel-rail vibration signals based on empirical mode decomposition (EMD) generalized energy. Background technique [0002] According to the statistics of the China Urban Rail Transit Association, during the "Twelfth Five-Year Plan" period, urban rail transit will be built and put into operation about 2,500 kilometers, with an average annual distance of about 500 kilometers. By the end of 2020, the total mileage of urban rail transit in the country will reach about 7,000 kilometers. Urban rail transit is developing towards high speed, high density, complex technical system composition, and high linkage of business systems, which puts forward higher and higher requirements for vehicle driving control, fault diagnosis and detection. [0003] The running system of an urban rail trans...

AI Technical Summary

Problems solved by technology

The flat wheel failure is one of the major driving accident hazards of urban rail transit vehicles. The flat wheel failure of the vehicle will produce periodic noise and wheel-rail impact when the vehicle is running. This noise not only seriously affects the comfort of passengers, but also gives The surrounding environment of the line brings interference; and the impact energy of the wheel and rail is transmitted from the ballast bed to the inner layer of the roadbed, which is one of the important reasons for the fracture of concrete sleepers and rails, and this type of impact is also an important reason for the fatigue and cold shear of the vehicle axle and bearing damage , with high vehicle speeds, heavy loads, and deep bruises, this high-intensity impact can cause more damage

[0004] At present, although the urban rail transit operation department has formulated various maintenance plans for train wheelsets in different periods, this kind of static inspection not only takes up the service time of the vehicles but also brings a lot of labor costs, and cannot monitor the flat wheel faults of the vehicle wheelsets in real time. It is increasingly unable to meet the requirements of urban rail transit operation safety factor and operation service quality

Images