Remaining useful life and health status estimation of turbofan engines using unsupervised deep-learning framework

Predictive maintenance can prevent machine failure by estimating the remaining useful life (RUL) in advance. In the literature, various data-driven methods that require labelled data cannot be useful when the failure data is scarce. In this paper, an unsupervised deep-learning framework is proposed that consists of (i) an autoencoder for reducing the dimensionality of features into a virtual health index (VHI), (ii) a Bi-LSTM-based encoder-decoder with an attention mechanism and a sliding window method for predicting RUL using VHI dataset, and (iii) K-means clustering and a neural network for estimating the health status using VHI dataset. The proposed framework is tested on one of the C-MAPSS datasets, and the results are compared with the literature. Results demonstrate satisfactory performance by the proposed framework in estimating accurate RUL and health states for many turbofan engines.