Failure Prediction of Power-Shift Steering Transmission Based on Oil Spectral Analysis with Wiener Process

The most common methodology used in element concentration measurement and analyzing of wear particles is Atomic emission (AE) spectroscopy. As an indirect measuring method, the oil spectral data is introduced to indicate the performance degradation and the residual life prediction in the reliability evaluation of Power shift steering transmission (PSST). Stochastic methods especially the Wiener process is convenient in solving and analyzing the unitary degradation failure indicated by the oil spectral data. The oil data have been sampled in the real operating condition, and the data set has more than 50 samples taken from PSST. The mean values and time-dependent characteristics of three indicating elements are statistically obtained by the linear regression analysis. The model of the degradation and failure prediction has been proposed based on the Wiener process with the positive drift. For modeling and simulation the software R was used. Therefore, the trend curves of diffusion process with their First Hitting Time have been predicted. Through comparison, the time intervals of condition-based maintenance have been extended as 27 Mh (15.9%). This will save the cost of maintenances by eliminate the preventive maintained cycles. The advantage and novelty of the Outcomes presented in the article are that the stochastic process might be applied for predicting the degradation failure occurrence and also for optimizing the maintenance intervals and the cost-benefit. As might be expected, the method can be extended to other cases of wear prediction and evaluation in complex mechanical system.