Condition monitoring and prognosis is a key issue in ensuring stable and reliable operation of mechanical transmissions. Wear
in a mechanical transmission, which leads to the production of wear particles followed by severe wear, is a slow degradation
process that can be monitored by spectral analysis of oil, but the actual degree of degradation is often difficult to evaluate in
practical applications due to the complexity of multiple oil spectra. To solve this problem, a health index extraction methodology
is proposed to better characterize the degree of degradation compared to relying solely on spectral oil data, which leads to an
accurate estimation of the failure time when the transmission no longer fulfils its function. The health index is extracted using a
weighted average method with selection of degradation data with allocation steps for weight coefficients that lead to a reasonable
mechanical transmission degradation model. First, the degradation data used as input are selected based on source entropy which
can describe the information volume contained in each set of spectral oil data. Then, the weight coefficient of each set of degradation data is modelled by measuring the relative scale of the permutation entropy from the selected degradation data. Finally, the
selected degradation data are fused, and the health index is extracted. The proposed methodology was verified using a case study
involving a degradation dataset of multispectral oil data sampled from several power-shift steering transmissions.
Remaining useful life prediction is a critical issue to fault diagnosis and health management of power-shift steering transmission. Power-shift steering transmission wear, which leads to the increase of wear particles and severe wear afterwards, is a slow degradation process, which can be monitored by oil spectral analysis, but the actual degree of the power-shift steering transmission degradation is often difficult to evaluate. The main purpose of this article is to provide a more accurate remaining useful life prediction methodology for power-shift steering transmission compared to relying solely on an individual spectral oil data. Our methodology includes multiple degradation data fusion, degradation index construction, degradation modelling and remaining useful life estimation procedures. First, the robust kernel principal component analysis is used to reduce the data dimension, and the state space model is utilized to construct the wear degradation index. Then, the Wiener process-based degradation model is established based on the constructed degradation index, and the explicit formulas for several important quantities for remaining useful life estimation such as the probability density function and cumulative distribution function are derived. Finally, a case study is presented to demonstrate the applicability of the proposed methodology. The results show that the proposed remaining useful life prediction methodology can objectively describe the power-shift steering transmission degradation law, and the predicted remaining useful life has been extended as 65 Mh (38.2%) compared with specified maintenance interval. This will reduce the maintenance times of power-shift steering transmission life cycle and finally save the maintenance costs.
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