Abstract: Aiming at the issue of difficulty in online detection of sensitivity drift and other faults in axle-load dynamic truck scales in service state, a fault diagnosis method of dynamic truck scale based on Levy flight improved particle swarm optimization support vector machine (IPSO-SVM) model combined with signal feature extraction and dimension reduction under feature dimension reduction was proposed. Firstly, the time-domain and frequencydomain features of the output signal were extracted, and kernel principal component analysis (KPCA) was used to transform the input space into the high-dimensional space through the nonlinear mapping function to realize the dimensionality reduction and screening of the feature vectors. Then, the optimization ability of Levy's improved particle swarm optimization algorithm (PSO) was adopted, and the improved algorithm was used to optimize the support vector machine (SVM) to obtain the optimal parameter combination, so as to construct the global optimal IPSO-SVM diagnosis model. Finally, the established diagnostic model was used to diagnose and verify the fault diagnosis of the dynamic truck scale under different vehicle weights, different speeds, and different axle load conditions. The experimental results show that the diagnostic accuracy of the prototype can reach 98% using the dynamic truck scale fault diagnosis method, it confirms that the improved particle swarm optimization algorithm after the introduction of Levy flight significantly improves the optimization efficiency and effect. Comparing with the existing diagnostic methods, the IPSO-SVM diagnostic model can effectively solve the problem of PSO algorithm easily getting stuck in local optimal solutions, thus greatly improving its accuracy, and can realize the highprecision diagnosis of all types under the dynamic fault conditions of the truck scale system.
Key words: quality measuring instrument; fault diagnosis model; Levy flight; signal feature extraction; signal feature dimensionality reduction; support vector machine (SVM); improved particle swarm optimization support vector machine (IPSOSVM); kernel principal component analysis (KPCA)