A prognosis method for multi-sensor-monitored transportation devices under truncated data conditions

Abstract With the advancement of artificial intelligence, transportation devices driven by autonomous driving technology can efficiently process large-scale data and predict the lifetime through deep learning, thereby enhancing the intelligent operation and maintenance capabilities of such devices. Considering both safety and economic factors associated with multi-sensor-monitored transportation devices, the truncated lifetime test is typically conducted to acquire degradation data for evaluating the device’s lifetime characteristics. In this context, historical monitoring data are typically truncated, posing practical challenges for existing methods, including the absence of lifetime labels and uncertainty in remaining useful life (RUL) prediction. To address this issue, for transportation devices under multi-sensor monitoring, this paper proposes a novel interactive prognosis method that accounts for the influence of truncated data. In the offline training phase, a stacked denoising attention autoencoder is employed to extract health indicators (HIs) from multi-sensor-monitored data in an unsupervised manner, enabling nonlinear degradation modeling based on these indicators. Furthermore, the optimal solution of the deep learning network is obtained by constructing a feedback optimization function linking truncated HIs with a stochastic degradation model. In the online testing phase, the interactive prognosis model trained offline is employed to extract HIs from on-site testing data, and the probability distribution of the RUL is derived based on the nonlinear Wiener process model characterizing the HIs. Meanwhile, Bayesian posterior distribution can be utilized to enable online updating of parameter estimates. Finally, the case studies on turbofan engines are provided to demonstrate the proposed method’s effectiveness.