A Blind Deconvolution Approach Based on Spectral Harmonics-to-Noise Ratio for Rotating Machinery Condition Monitoring

Abstract

Harmonics-to-noise ratio (HNR) is an important health index of rotating machine, which has been applied in blind deconvolution (BD) method to realize periodic impulse detection. However, most fault impulses are not strictly periodic, but pseudo-cyclostationary, which will affect the performance of HNR in fault characterization to some extent. This limits its applications. Therefore, in this paper, a novel BD method, maximum squared envelope spectrum harmonic-to-interference ratio deconvolution (MSESHIRD), is proposed to more effectively achieve fault identification. The proposed method seeks a target filter by maximizing squared envelope spectrum harmonic-to-interference ratio (SESHIR). Since harmonic components corresponding to repetitive fault impulses in SES are less sensitive to random fluctuations, SESHIR can more accurately distinguish repetitive fault impulses from irrelevant interference in vibration signals. Therefore, BD based on SESHIR has better performance than BD based on HNR in measuring fault features in signals. Through simulation and experimental case analysis, the proposed method is compared with several public methods Results show that the proposed method has better performance in fault characteristic extraction. In addition, it is implemented on bearing run-to-failure data for condition monitoring to show that the proposed method has excellent ability of early fault detection. Note to Practitioners—This paper is motivated by the problems of automatic operating condition monitoring and early defect diagnosis of rotating machines. These problems can be effectively solved by designing a BD method based on reliable and efficient health indices. HNR defined on autocorrelation function (AF) is an excellent health index to characterize the signal-to-noise ratio (SNR) of repetitive fault impulse in signals. However, this paper uses mathematical models of HNR to show that it has very strict requirements on the period and SNR of fault impulse signals. A fluctuating fault period or a low SNR might make HNR unable to accurately estimate the energy of fault components in signals, thus weakening its performance in fault characterization. Compared with HNR, SESHIR has better fault characterization ability due to that SES can more accurately obtain the periodicity (frequency) and energy of fault components in signals. Therefore, this paper proposes a novel BD method based on SESHIR maximization for repetitive impulse monitoring. Its effectiveness and robustness are verified by both theoretical justification and experimental results.