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- Title
时频能量谱与VGG16结合的车轮扁疤损伤程度 估计方法.
- Authors
李大柱; 牛 江; 梁树林; 池茂儒
- Abstract
In order to accurate monitoring of the damage degree of wheel flat scars of vehicles in operation, a method for estimating the damage degree of wheel flat scars was proposed based on the combination of time-frequency energy spectrum and VGG16 convolutional neural network. This method might quantitatively estimate the damage degree of wheel flat scars in real time by analyzing and processing the vibration acceleration signals of axle box during vehicle operation. The dynamics models of rigid flexible coupling system of vehicle tracks and the mathematical models of wheel flat scars were established to simulate the vibration response of the vehicle axle box under different flat scar damage conditions. Using morphological filter and CEEMDAN WVD time-frequency analysis method, the vibration acceleration signals of the axle box were filtered and reduced, and then expressed in the time-frequency energy spectrum. The VGG16 convolutional neural network models were constructed, and the training sets were constructed by using the time-frequency energy spectrum of a large number of wheel flat scar fault data to train the VGG16 models. Several wheel flat scar conditions were randomly simulated and the VGG16 models were tested and verified. The simulation tests show that the method combining time-frequency energy spectrum with VGG16 models may accurately estimate the damage degree of wheel flat scars of vehicles in operation, and the estimation errors are within 1.6 mm.
- Subjects
CONVOLUTIONAL neural networks; ARTIFICIAL neural networks; RIGID dynamics; TIME-frequency analysis; HILBERT-Huang transform; MATHEMATICAL models
- Publication
China Mechanical Engineering, 2023, Vol 34, Issue 16, p1907
- ISSN
1004-132X
- Publication type
Article
- DOI
10.3969/j.issn.l004-132X.2023.16.003