Parameter optimized VMD-CWD time-frequency representation and BSNMF identification diagnosis method of internal combustion engine
YUE Yingjuan
WANG Xu
CAI Yanping
LIU Yuan
ZHENG Yong
Abstract：Aiming at the problem of vibration response signal of internal combustion engine featuring the strong coupling and weak fault,a fault diagnosis method based on vibaration time-frequency feature of parameter optimization VMD-CWD internal combustion engine and BSNMF block coding recognition is proposed.Variational Mode Decomposition (VMD) is used to decompose the vibration signal of internal combustion engine into a set of Intrinsic Modal Function (IMF),and the Choi-Williams Distribution (CWD) of the IMF component signal is superimposed in order to obtain the vibration spectrum image with better time-frequency concentration and without cross term interference.In allusion to the parameter selection in the process of VMD decomposition,power spectral entropy is introduced as the objective function and the successive grid optimization is achieved for decomposition parameter of VMD,which improves the adaptability of VMD decomposition.In order to realize the automatic recognition and diagnosis of the vibration spectrum image of the internal combustion engine,a more easily convergent Block Sparse Nonnegative Matrix Factorization(BSNMF) is proposed based on the Sparse Nonnegative Matrix Factorization(SNMF),which is used to extract features of vibration spectrum of internal combustion engine and the support vector machine is adopted to directly conduct the pattern identification of extracted feature parameters.The method is applied to the fault diagnosis of internal combustion engine.The results show that this method can effectively extract the weak fault characteristics of the vibration signal of internal combustion engine and realize the automatic diagnosis of the valve mechanism failure of internal combustion engine.
Keywords：fault diagnosisIC engineChoi-Williams distributionvariational mode decompositionblock sparse non-negative matrix Factorization
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 10-16 )