An improved variational mode decomposition method and its application in diesel engine fault diagnosis

Abstract

The diesel engine is a complex mechanical device, with the characteristics of multi-source, multi moving parts, complex work. For the complex multi-component signal, it is usually necessary to decompose it into a number of single-component AM-FM signals, and each component is analyzed to extract amplitude and frequency information. VMD is essentially composed of a plurality of adaptive Wiener filter and has good noise robustness. Compared with EMD, EEMD, CEEMDAN, LMD and ITD, VMD has strong mathematical theory basis. At the same time, VMD rejects the method of recursive screening stripping. So VMD can effectively alleviate or avoid a series of problems which appear in other methods. However, it is a problem how to determine the number of decomposition layers and the penalty factor, because human factors will affect the decomposition results. In order to solve the problem, an improved adaptive genetic algorithm (IAGA) is proposed to optimize the parameters of VMD. Genetic algorithms mainly include 3 genetic operators: selection, crossover and mutation. The cross probability and mutation probability will directly affect the optimization results. In the traditional genetic algorithm, the probability of cross and mutation are fixed, and the genetic algorithm is easy to fall into the local optimal. According to the regulation of hormone regulation, the cross probability and mutation probability in evolution were improved. The permutation entropy is a new method of mutation detection, which mainly aims at the spatial characteristics of the time series itself. Therefore, the entropy of the components obtained by the VMD decomposition is used as the fitness function of the IAGA. The modal number K and penalty factor α of VMD were iteratively optimized by IAGA, and the optimal combination of parameters was obtained. Based on the proposed method, the vibration signals of the crankshaft bearing fault simulation experiment were decomposed into several components. According to the value of the permutation entropy, the fault components were selected and the energy was extracted. The fault pattern is identified by the support vector machine (SVM) successfully. The simulation analysis and the simulation experiment of the crankshaft bearing fault show that the proposed method is effective. For the diagnosis of other engines, a large number of validation experiments are needed for further research.