Minimum noise amplitude deconvolution and its application in repetitive impact detection

Abstract

Blind deconvolution (BD) is an effective technology for rotating machinery fault detection because it significantly weakens noise and reduces the interference of the system transmission path. This paper analyses the possible shortcomings of several popular BDs in detecting repetitive impacts hidden in rotating machinery signals. Then a new index named periodic noise amplitude ratio (PNAR) is defined to measure the noise level of fault signals. Minimum noise amplitude deconvolution (MNAD) is a new BD algorithm based on PNAR minimization. Unlike the existing BDs, MNAD does not focus on the impacts with certain randomness in their moments of occurrence but enhances the repetitive impact characteristics by weakening the defined periodic noise. MNAD is particularly suitable for detecting repetitive impacts hidden in cyclostationary signals. Simulation analysis and experimental results show that MNAD adaptively determines one or more resonance bands generated by repetitive impacts and has a good suppression effect on the noise in the resonance bands. The implementation of MNAD is available in MATLAB community.