Comprehensive Dynamic Prognosis of Rolling Element Bearing Health Through Adaptively Demodulated Nonlinear Dispersive Spectral Entropy

Spectral entropy (SE) is a promising nonlinear measure for detecting dynamic variations in vibration signals acquired from rolling element bearings (REB). However, in real world scenarios, characteristic spectral features relating to REB fault gets concealed by unwanted frequency components due to the association of heavy environmental noise. Consequently, original SE not only fails to detect incipient REB fault but also fails to monitor the progression of the fault along with predicting the remaining useful life of the faulty REB. Aiming to address aforementioned problems, in this paper, firstly, characteristic spectral features of REB fault is revealed by calculating the spectrum of the adaptively demodulated weighted squared envelope of the corresponding vibration signal. Subsequently, instead of using classical Shannon entropy theory corresponding to original SE, comprehensive prognosis of the analyzed REB health is achieved through the information quantification of the calculated spectrum by incorporating dispersion entropy (DE) theory. In this context, the proposed measure is named as adaptively demodulated dispersive spectral entropy (ADDSE). Two different run to failure REB data have been utilized to verify the effectiveness of the proposed ADDSE. Results show that the proposed ADDSE not only can overcome the limitations of the original SE in comprehensive dynamic prognosis of REB health but also demonstrate superior performance in compare to other conventional measures such as original DE and root mean square (RMS); advanced version of spectral entropy namely cumulative spectrum distribution entropy (CSDE) and three dimensional holo hilbert spectral entropy (MHHSE3D); alternative sparsity based measure namely Gini index (GI). Note to Practitioners - Motivation of this research paper is derived from the practical need of detecting REB fault at its earliest point of inception, monitoring the growth of the fault in reliable consistent manner and facilitating the efficient remaining useful life prediction of the incepted fault during the prognosis of REB health. Different from conventional research on time-series based entropy theory for prognosis of REB health, this research calculates the entropy value within spectral domain of the vibration signal collected from REB for its better suitability of representing characteristic fault frequency. In this context, considering the limitations of original Shannon entropy theory, dispersion entropy (DE) theory has been utilized for calculating the SE value of the analyzed vibration signal. Two different sets of experimental accelerated REB degradation data have demonstrated the superiority of the proposed measure over the state of the art time series based and frequency domain based entropy measures for comprehensive prognosis of REB health.