Intelligent prognostics of machining tools based on adaptive variational mode decomposition and deep learning method with attention mechanism

In the modern manufacturing industry, remaining useful life (RUL) prediction of the machining tools plays a significant role in promoting machining efficiency, ensuring product quality and reducing production costs. In recent years, many data-driven prognostic approaches have been developed for machining tools, but few of them have considered the operating conditions such as spindle load and rotating speed that may have great impact on the degradation behavior and sensor signals. It may give rise to more uncertainty and lead to an obvious decrease in prediction accuracy when operating condition changes. Besides, feature extraction from the raw signals that are nonstationary, nonlinear, and mixed with abundant noise is essential but quite challenging. To address these issues, this paper proposes a novel prognostic approach for machining tools under dynamic operating condition with varying spindle load. In the proposed approach, an adaptive variational mode decomposition (VMD) is newly developed to adaptively search the optimal parameters for processing the raw vibration data, then several components with good trendability and noise robustness are obtained for feature extraction. Furthermore, a deep learning model combining one-dimensional convolutional long short-term memory (LSTM) with attention mechanism is constructed to perform RUL prediction. Numerical experiments on a real-world case study show the effectiveness and superiority of the proposed approach in comparison with other baseline data-driven approaches.