Deep LSTM Enhancement for RUL Prediction Using Gaussian Mixture Models

• Published in: Automatic control and computer sciences Vol. 55; no. 1; pp. 15 - 25
• Main Authors: Sayah, M., Guebli, D., Noureddine, Z., Al Masry, Z.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 2021; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Clustering; Computer Science; Control Structures and Microprogramming; Life prediction; Machine learning; Modular systems; Probabilistic models; Propulsion systems; Useful life; RUL; deep learning; Gaussian mixture; expectation-maximization; LSTM network; prognostics and health management

This paper introduces a new deep learning model for Remaining Useful Life (RUL) prediction of complex industrial system components using Gaussian Mixture Models (GMMs). The used model is an enhanced deep LSTM approach, for which Gaussian mixture clustering is performed for all collected sensors data...