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

Accurate 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.