A novel approach for remaining useful life prediction of high‐reliability equipment based on long short‐term memory and multi‐head self‐attention mechanism

Author:

Al‐Dahidi Sameer1,Rashed Mohammad23,Abu‐Shams Mohammad4,Mellal Mohamed Arezki5,Alrbai Mohammad6,Ramadan Saleem7,Zio Enrico89

Affiliation:

1. Department of Mechanical and Maintenance Engineering School of Applied Technical Sciences German Jordanian University Amman Jordan

2. Department of Informatics Technical University of Munich Garching Germany

3. ANavS Gmbh, Computer Vision Group Munich Germany

4. Department of Industrial Engineering School of Applied Technical Sciences German Jordanian University Amman Jordan

5. Faculty of Technology M'Hamed Bougara University Boumerdes Algeria

6. Department of Mechanical Engineering School of Engineering University of Jordan Amman Jordan

7. Industrial Engineering Department School of Engineering Technology Al Hussein Technical University Amman Jordan

8. Department of Energy Politecnico di Milano Milan Italy

9. MINES‐Paris PSL Research University CRC Sophia Antipolis France

Abstract

AbstractAccurate prediction of the Remaining Useful Life (RUL) of components and systems is crucial for avoiding an unscheduled shutdown of production by planning maintenance interventions effectively in advance. For high‐reliability equipment, few complete‐run‐to‐failure trajectories may be available in practice. This constitutes a technical challenge for data‐driven techniques for estimating the RUL. This paper proposes a novel data‐driven approach for fault prognostics using the Long‐Short Term Memory (LSTM) model combined with the Multi‐Head Self‐Attention (MHSA) mechanism. The former is applied to the input signals, whereas the latter is used to extract features from the LSTM hidden states, benefiting from the information from all hidden states rather than utilizing that of the final hidden state only. The proposed approach is characterized by its capability to recognize long‐term dependencies while extracting features in both global and local contexts. This enables the approach to provide accurate RUL estimates in various stages of the equipment's life. The proposed approach is applied to an artificial case study simulated to mimic the realistic degradation behaviour of a heterogeneous fleet of aluminium electrolytic capacitors used in the automotive industry (under variable operating and environmental conditions). Results indicate that the proposed approach can provide accurate RUL estimates for high‐reliability equipment compared to four benchmark models from the literature.

Publisher

Wiley

Subject

Management Science and Operations Research,Safety, Risk, Reliability and Quality

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