| Railway vehicles is the main means of transport in our country, Axle is one of the key parts in railway vehicles. The severe working environment always accompanied by hidden frictions such as corrosion, cyclic stress, strike, high and low temperature. These complex factors affecting the life of the axle, there will be wear and tear, crack, stripping even the very common damage phenomenon such as fracture. Especially high speed and overloaded under the action of alternating load will cause serious traffic accident. The fatigue problem is one of the subject matter of vehicle operational failure. In these complex cases, to ensure the reliability and safety of the vehicle, the axle fatigue failure problems need to be solved.Based on the fault mechanism of railway vehicle axles and detecting technology to explore, analysis the basis of the axle fatigue crack position and form. At the same time, according to the characteristics of fatigue signal time-frequency analysis algorithm on the summary of the contrast. Through the theoretical analysis and simulation study of fatigue crack signal and using improved LMD algorithm to obtain the early signs of fault signal feature extraction. Finally, the introduction of acoustic emission testing technology, combined with the improved LMD algorithm to deal with fatigue crack signal feature extraction problem. Simulation of acoustic emission signal model experiment using fault lead to replace the rail vehicle axles to study the acoustic emission signal of fatigue analysis, after processing, the data were compared with some of the common feature extraction parameters.Experimental studies show that the combining of the improved algorithm which proposed by this paper and acoustic emission detection technology can featurely draw the fatigue crack acoustic emission signals and also as a dealing method for early failure signs. This method can be improved and then perfectly applied in rail vehicle axle failure detection and other fields. |
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