Theoretical and Experimental Investigations on Spectral Lp/Lq Norm Ratio and Spectral Gini Index for Rotating Machine Health Monitoring

Prognostics and health management of the rotating machine aim to use monitoring data to infer the health conditions of the rotating machine in order to avoid unexpected accidents and minimize economic losses. Since health indices can detect an abnormality and provide observations for prognostic modeling, they are the basis of prognostics and health management. The spectral Lp/Lq norm ratio and the spectral Gini index have been recognized as popular health indices to characterize the impulsiveness of repetitive transients caused by machine faults for rotating machine health monitoring. Here, some special forms of the spectral Lp/Lq norm ratio include spectral kurtosis, the spectral L2/L1 norm ratio, the reciprocal of the spectral smoothness index, and so on. In this article, theoretical and experimental investigations on the spectral Lp/Lq norm ratio and the spectral Gini index for machine health monitoring are conducted to prove how they characterize the impulsiveness of repetitive transients. Results showed that an increase in the total length of the nonimpulsive regions of repetitive transients makes the spectral Lp/Lq norm ratio and the spectral Gini index become large, which, in turn, can be used to explain changes of health indices during machine degradation at varying operating conditions and in the case of impulsive noises. To solve the problem of the sensitiveness of popular health indices to impulsive noises, a fused health index for characterizing cyclostationarity of repetitive transients is proposed. Analyses of bearing run-to-failure showed that the proposed fused index has better monitoring performance than the aforementioned popular health indices. Note to Practitioners-This article was motivated by the problem of characterizing nonprocessed signals for automatic machine health monitoring. Practically, nonprocessed signals, such as vibration signals, acoustic signals, and so on, cannot directly be applied to reflect machine health conditions. The transformation of nonprocessed signals by using health indices into process signals is great of a concern. Most existing methods directly use intuition and experience to choose health indices in order to realize machine health monitoring. Thus, these methods do not provide theoretical investigations and support to illustrate how health indices characterize nonprocessed signals generated from a faulty machine. This article uses mathematical models and inferences to explain how popular health indices characterize impulsive signals generated from a faulty machine. Then, it is shown that direct applications of popular health indices in a time domain are sensitive to impulsive noises, which causes failures of health indices for machine health monitoring in the occurrence of impulsive noises. To solve this problem, it is suggested to use indices to characterize frequency information of faulty signals. Finally, an efficient and reliable fusion of health indices in a frequency domain is proposed for machine health monitoring.

Automated summary

Prognostics and health management of rotating machines aim to use monitoring data to infer health conditions, with health indices being the basis. Spectral Lp/Lq norm ratio and spectral Gini index are popular health indices characterizing impulsiveness caused by faults. Special forms include spectral kurtosis and spectral L2/L1 norm ratio. Experimental investigations showed that these indices characterize impulsiveness effectively, with a fused health index proposed to address issues with impulsive noises.