Fault Diagnosis of Rolling Bearing Based on ALIF-SVD
Aiming at the problems of rolling bearing fault signal containing a large number of noise signals and modal aliasing in iterative filtering algorithm,a new method of rolling bearing fault diagnosis based on adaptive local iterative filtering algorithm and singular value decomposition algorithm is proposed.Firstly,the adaptive local iterative filtering algorithm is used to process the fault signal to obtain several intrinsic mode functions,and the sample entropy is calculated and the threshold is set for signal reconstruction.Then singular value decomposition was performed to draw the difference spectrum curve;finally,the secondary reconstruction is carried out according to the mutation position in the difference spectrum to further complete the noise reduction.In this paper,this method is applied to the bearing da-ta of Case Western Reserve University for verification.The experimental results show that this method solves the modal aliasing problem existing in the iterative filtering algorithm and the redundancy problem of a large number of noise signals,which reflects the effectiveness of this method in the fault diagnosis of rolling bearings.
ALIFSVDsample entropysingular value difference spectrum
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