Remaining Useful Life Estimation Using Long Short-Term Memory Neural Networks and Deep Fusion

• Published in: IEEE access Vol. 8; pp. 19033 - 19045
• Main Authors: Zhang, Yang, Hutchinson, Paul, Lieven, Nicholas A. J., Nunez-Yanez, Jose
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Archiving; Artificial neural networks; Autoregressive processes; Data compression; Data integration; Degradation; Energy storage; Estimation; Hidden Markov models; long-short term memory; Machine health monitoring; Machinery; Neural networks; Physics; Prognostics and health management; recurrent neural network; Regression analysis; remaining useful life (RUL); Statistical analysis; Statistical models; Useful life
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2966827

Estimation of Remaining Useful Life (RUL) is a crucial task in Prognostics and Health Management (PHM) for condition-based maintenance of machinery. In order to transmit and store the sensor data for archiving and long term analysis, data compression techniques are regularly used to reduce the requirements of bandwidth, energy and storage in modern remote PHM systems. In these systems the challenge arises of how the compressed sensor data affects the RUL estimation algorithms. A main drawback of conventional statistical modeling approaches is that they require expert prior knowledge and a significant number of assumptions. Alternative regression based approaches and deep neural networks are known to have issues when modeling long-term dependencies in the sequential data. Recently Long Short-Term Memory (LSTM) neural networks have been proposed to overcome these issues and in this paper we create a LSTM network and data fusion approach that can estimate the RUL with compressed (distorted) data. The experimental results indicate that the proposed method is able to estimate RUL reliably with narrower error bands compared to other state-of-the-art approaches. Moreover, the proposed method is able to predict RUL from both the raw and compressed datasets with comparable accuracy.