A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

Abstract

The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing map (SOM) networks, this paper constructs the dynamic entropy weighted minimum quantization error (DEWMQE) as the initial health indicator (HI). Subsequently, employing windowed inertia smoothing and scale correction mechanisms, this paper filters anomalies in HI, ensuring trend smoothing and scale consistency. Considering the degradation process and characteristics of rotating machinery systems, this study utilizes linear multi-fractional Lévy stable motion (LMLSM) to build a degradation model. Building upon this, to effectively utilize historical data and overcome the shortcomings of random process theory requiring predefined degradation paths, this study further integrates bidirectional gated recurrent units (BGRU) and similarity transfer learning methods to devise the BGTLLM hybrid model, enabling adaptive fitting of the degradation trend. Finally, the Monte Carlo (MC) method is employed to evaluate the uncertainty in RUL prediction. The effectiveness and accuracy of the proposed hybrid prediction model in RUL forecasting are validated using heavy-duty truck axle data and the PHM2012 dataset.