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

Abstract

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. INDEX TERMS Machine health monitoring, remaining useful life (RUL), long-short term memory, recurrent neural network, data compression.

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

“…For instance, a two-phase gammas process with Bayesian approach was used to predict the remaining useful life of corroded reinforced concrete beams. 260 For more information about RUL estimation, interested readers are recommended to read the bottom-top review paper by Lei et al, 261 and the deep NN methods for RUL estimation by Zhang et al 262 However, the biggest challenge in achieving complete 5stage damage identification is creating a pipeline that can run each stage with an acceptable level of performance both quantitatively and also qualitatively. Even with advances in technology, the realization of damage prognosis into civil structure monitoring systems currently does not necessitates spending efforts.…”

Remaining Useful Life Estimation of Bearings

“…Some researchers forego feature engineering altogether and use the raw signal directly (Khelif et al, 2017;C. Liu, Zhang, & Wu, 2019;Verstraete, Droguett, & Modarres, 2019;Jiang, Lee, & Zeng, 2019;Zhang, Hutchinson, Lieven, & Nunez-Yanez, 2020;B. Wang, Lei, Yan, Li, & Guo, 2020).…”

Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network

“…For example, Angela et al used a hybrid VARMA-LSTM method to estimate the state-ofcharge of electric vehicles [23]. Zhang et al used LSTMs with deep fusion for machinery RUL prediction [24]. Wu et al used vanilla LSTM for RUL prediction [25].…”