Enhanced Mamba model with multi-head attention mechanism and learnable scaling parameters for remaining useful life prediction

Prognostics and health management (PHM) technology aims to analyze and diagnose the state of equipment using a large amount of data, predict potential failures, and adopt corresponding maintenance and repair strategies to enhance equipment reliability, reduce repair costs, and prevent production interruptions. In this paper, we propose a remaining useful life (RUL) prediction model based on Mamba, which incorporates learnable parameters and a multi-head attention mechanism; to address the issues faced by traditional algorithms, which struggle to efficiently capture dependencies in long sequences and parallelize the processing of these sequences. Firstly, min-max scaling and exponential smoothing techniques are used to preprocess the feature data in order to prevent gradient explosion while speeding up the convergence of the model. Secondly, a learnable scaling parameter is introduced into the Residual block to adjust the output, and a multi-head attention mechanism is innovatively integrated into the Mamba block to operate on the data processed by the convolutional layer, thereby enhancing the expressiveness and accuracy of the model. Lastly, the model is compared with the current state-of-the-art research findings on aero-engine and lithium-ion batteries datasets, and the experimental results demonstrate that the model outperforms the current state-of-the-art methods in RUL prediction tasks, exhibiting better generalization, and can be applied as a general RUL prediction method to other fields.