A Novel Composite Envelope Negentropy Deconvolution Reconstruction Method for Fault Diagnosis

Traditional deconvolution methods struggle to separate fault information under interference from contaminative signals. Although some improved efforts tried, their application is strictly limited to parameter requirements. To overcome this, a composite envelope negentropy deconvolution and reconstruction (CENDR) method is proposed. First, considering the inherent sensitivity to repetitious transient features in the energy domain, composite envelope negentropy (CEN) is utilized as the objective function for deconvolution to enhance the fault features. Then, an adaptive reconstruction method based on CEN and singular value decomposition (SVD) is proposed to reconstruct the filtered signal. The superiority of CENDR lies in its ability to simultaneously consider the periodicity and impulsiveness of fault information. It achieves fault feature extraction under strong interference without relying on strict parameters. Specifically, by utilizing fault characteristic ratio (FCR), the quantitative evaluation reveals that CENDR performs better than the existing optimal deconvolution algorithm, ranging from 28% to 404% under different scenarios.