摘要
Morphological component analysis( MCA) is a signal separation method based on signal morphological diversity and sparse representation. MCA can extract the signal components of different morphologies by different dictionary combinations. Firstly,the theory of MCA was analyzed with sparse representation principle and relaxation criterion. Then detailed steps of block coordinate relaxation( BCR) were given. Finally,algorithm performance was verified by simulation signals analysis, MCA was applied to decomposing and denoising gearbox signals, and the fault parameters were extracted by energy operator demodulation envelop of morphological component.
Morphological component analysis（ MCA） is a signal separation method based on signal morphological diversity and sparse representation. MCA can extract the signal components of different morphologies by different dictionary combinations. Firstly,the theory of MCA was analyzed with sparse representation principle and relaxation criterion. Then detailed steps of block coordinate relaxation（ BCR） were given. Finally,algorithm performance was verified by simulation signals analysis, MCA was applied to decomposing and denoising gearbox signals, and the fault parameters were extracted by energy operator demodulation envelop of morphological component.
基金
National Natural Science Foundation of China(No.51575523)
