Long short-term memory for machine remaining life prediction

By Jianjing Zhang, Peng Wang, Ruqiang Yan, Robert X. Gao. Journal of manufacturing systems, 2018

411 citations recorded by the source corpus.

Abstract Reliable tracking of performance degradation in dynamical systems such as manufacturing machines or aircraft engines and consequently, prediction of the remaining useful life (RUL) are one of the major challenges in realizing smart manufacturing. Traditional machine learning algorithms are often constrained in adapting to the complex and non-linear characteristics of manufacturing systems and processes. With the rapid development of modern computational hardware, Deep Learning has emerged as a promising computational technique for dynamical system prediction due to its enhanced capability to characterize the system complexity, overcoming the shortcomings of those traditional methods. In this paper, a new approach based on the Long Short-Term Memory (LSTM) network, an architecture that is specialized in discovering the underlying patterns embedded in time series, is proposed to track the system degradation and consequently, to predict the RUL. The objectives of this paper are: 1) translating the raw sensor data to an interpretable health index with the aim of better describing the system health condition; and 2) tracking the historical system degradation for accurate prediction of its future health condition. Evaluation using NASA’s C-MAPSS dataset verifies the effectiveness of the proposed method. Compared with other machine learning techniques, LSTM turns out to be more powerful and accurate in revealing degradation patterns, enabled by its time-dependent structure in nature.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/6bbcc36fb5c2c0cbfb91338b77492fcfe827d3b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Ruqiang Yan
2. Rui Zhao
3. Robert X. Gao
4. Min Wu
5. Feri Guretno
6. Xiaoli Li
7. Zhenghua Chen
8. Noureddine Zerhouni

In Industrial and Manufacturing Engineering

1. Ruqiang Yan
2. Rui Zhao
3. Robert X. Gao
4. Zhenghua Chen
5. Jianbo Yu

Citations in the corpus

• Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook (2020, positive): “[122], a bi-directional LSTM for aircraft engine RUL estimation has been developed.”
• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021, methodology): “Zhang et al. [22] proposed a bidirectional long short-term memory (BD-LSTM) approach to predict the RUL [22].”