Abstract
In this paper, the direct current (DC) offset cancellation and S transform-based diagnosis method is verified using three case studies. For DC offset cancellation, correlated kurtosis (CK) is used instead of the cross-correlation coefficient in order to determine the optimal iteration number. Compared to the cross-correlation coefficient, CK enhances the DC offset cancellation ability enormously because of its excellent periodic impulse signal detection ability. Here, it has been proven experimentally that it can effectively diagnose the implanted bearing fault. However, the proposed method is less effective in the case of simultaneously present bearing and gear faults, especially for extremely weak bearing faults. In this circumstance, the iteration number of DC offset cancellation is determined directly by the high-speed shaft gear mesh frequency order. For the planetary gearbox, the application of the proposed method differs from the fixed-axis gearbox, because of its complex structure. For those small fault frequency parts, such as planet gear and ring gear, the DC offset cancellation’s ability is less effective than for the fixed-axis gearbox. In these studies, the S transform is used to display the time-frequency characteristics of the DC offset cancellation processed results; the performances are evaluated, and the discussions are given. The fault information can be more easily observed in the time-frequency contour than the frequency domain.
Highlights
To stay competitive, many companies all over the world try to go beyond the costly industry standard of preventative maintenance by implementing predictive maintenance procedures
In order to detect the periodic impulse signals (PIS) signal produced by the gearbox fault, direct current (DC) offset cancellation was used to process the fault signal
Through a comparative study based on an implanted bearing fault case, the correlated kurtosis (CK) was demonstrated to be superior to the cross-correlation coefficient to determine the iteration number of DC offset cancellation
Summary
Many companies all over the world try to go beyond the costly industry standard of preventative maintenance by implementing predictive maintenance procedures. Based on the above-mentioned literature and analysis, the main contributions of this paper can be summarized as follows: (1) instead of the cross-correlation coefficient, correlated kurtosis was used as a criterion to estimate the optimal iteration number of DC offset cancellation, since CK has the superior ability to detect the PIS signal and is more robust to the noise interference; (2) when PIS is very weak compared with the deterministic signal or existing bearing fault and gear fault simultaneously, even CK cannot detect weak PIS produced by the bearing fault effectively; in this circumstance, the maximum order of high-speed shaft mesh frequency was used as the iteration number of DC offset cancellation; (3) because of several existing merits, the S transform was used to combine with DC offset cancellation to display the time-frequency characteristic of various gearbox faults; it is very efficient and intuitive to find faults through time-frequency contours; in addition, it could be used for deep learning-based fault classification; (4) through the planetary gearbox case study, it was found that DC offset cancellation is less effective for those small fault frequency signals, such as the planet gear, ring gear, carrier, etc.
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