Prediction Interval Estimation of Aeroengine Remaining Useful Life Based on Bidirectional Long Short-Term Memory Network

Reliable and accurate aeroengine remaining useful life (RUL) prediction plays a key role in the aeroengine prognostics and health management (PHM) system. However, due to the epistemic uncertainties associated with aeroengine systems, prediction errors are unavoidable and sometimes significant in traditional deterministic point prediction methods. To improve the accuracy and credibility of RUL prediction, a novel prediction interval (PI) estimation method is proposed to quantify the uncertainties in RUL prediction. The proposed method involves data clustering, mathematical-statistical analysis, and deep learning techniques and is achieved through off-line and online phases. In the off-line phase, an enhanced fuzzy c-means (FCM) algorithm is proposed to divide the aeroengine health status into several discrete states. After labeling the health state of each sampling point, PIs are computed for them. This step is achieved by the empirical distributions of errors associated with all instances belonging to the health state under consideration. In the online phase, a bidirectional long short-term memory (Bi-LSTM) network is employed to estimate the boundaries of point prediction, and thus, the PI of aeroengine RUL is generated. The aeroengine degradation dataset from NASA is used to validate the proposed RUL PI estimation method. The results obtained indicate that the proposed method is a promising tool for providing reliable aeroengine RUL interval estimates, which can inform maintenance-related decisions.

Automated summary

A novel prediction interval (PI) estimation method is proposed to quantify uncertainties in RUL prediction, combining data clustering, mathematical-statistical analysis, and deep learning techniques in offline and online phases. Experimental results show that the proposed method is a promising tool for providing reliable aeroengine RUL interval estimates.