LSTM and Transformers based methods for Remaining Useful Life Prediction considering Censored Data

Predictive maintenance deals with the timely replacement of industrial components relatively to their failure. It allows to prevent shutdowns as in reactive maintenance and reduces the costs compared to preventive maintenance. As a consequence, Remaining Useful Life (RUL) prediction of industrial components has become a key challenge for condition-based monitoring. In many applications, in particular those for which preventive maintenance is the general rule, the prediction problem is made harder by the rarity of failing instances. Indeed, the interruption of data acquisition before the occurrence of the event of interest leads to right censored data. Recent deep-learning architectures, that show the best results of the literature for complete-life data, most often do not consider censoring, even though its rate in the industrial environment may be high.The present article introduces a method which considers censored data for the Dual Aspect Self-Attention based on a Transformer proposed by (Z. Zhang, Song, & Li, 2022), and puts it into competition a modified version of the ordinal regression-based LSTMof (Vishnu, Malhotra, Vig, & Shroff, 2019). The evaluation of the resulting method on the CMAPSS and N-CMAPSS benchmark dataset shows that it is competitive compared to the state-of-the-art RUL prediction methods for a low censoring rate and more efficient for a high rate of censoring in large enough data sets. Finally, conformal prediction is used to estimate confidence intervals for the predictions.