Simultaneous prediction of remaining-useful-life and failure-likelihood with GRU-based deep networks for predictive maintenance analysis


Predictive maintenance (PdM) has been an integral part of large industrial sites collecting data from multiple sensors to reduce the maintenance power and costs with the advent of Industry 4.0. Two of the major problems in PdM used at large industrial sites are: (i) the prediction of remaining useful life (RUL); (ii) the prediction of the likelihood of failing within a predefined time period. While data oriented maintenance predictions were heavily focused on using classical techniques for such problems, recent interest shifted towards utilizing AI based solutions due to the better generalization capabilities of deep solutions. Among the time-sequence based deep networks, RNN, GRU and LSTM based networks are the most frequently used solutions. GRUs have demonstrated their faster learning capabilities with near or better prediction performance on certain tasks already. However, predicting multiple PdM tasks including both RUL and failure detection, simultaneously within the same network in an end to end manner with GRUs has not been much studied in the literature before. In this paper, we introduce a solution to predict those two tasks simultaneously within the same network based on GRUs. In our experiments we compare the performance of GRU layers to LSTM and RNN layers and report their performance on NASA dataset.

Conference Name: 2021 44th International Conference on Telecommunications and Signal Processing (TSP)
Date of Conference: 26-28 July 2021
GRUs, Remaining useful life prediction, Failure prediction, Predictive maintenance, Time-sequence analysis