Multi-scale deep neural network approach with attention mechanism for remaining useful life estimation

• An attention-based deep learning framework is developed for machinery prognostics. • PSO algorithm is employed to optimize the hyperparameters of the proposed network. • The multi-head self-attention mechanism is applied to increase prognostic accuracy. • Experiments on the C-MAPSS data validate the effectiveness of the proposed method. Prognostics and Health Management (PHM) is the core task in modern industries to provide the reliability and availability of mechanical systems. In recent years, the degradation behaviors have been extensively employed to estimate the remaining useful life in PMH technologies. In this research, a novel data-driven framework based on the multi-scale network structure, called MCA-BGRU, is proposed to provide the remaining useful life prediction, which combines multi-scale convolution neural network (CNN), bidirectional gated recurrent unit (BGRU), multi-head self-attention (MHSA) mechanism, and fully-connected layers. In this proposed structure, multi-scale CNN blocks and the MHSA mechanism are constructed to capture high-level representations from the multivariate input data automatically. Then, a BGRU layer is leveraged to learn various temporal tendencies between extracted features. Additionally, particle swarm optimization is adopted to simultaneously tune the hyperparameters of this framework. The superiority of the MCA-BGRU is validated by the well-known C-MAPSS dataset of NASA. The experimental results revealed that the presented approach achieves an improvement of 0.32% and 5.6% in terms of RMSE and Score values compared with the various existing studies.