RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network

Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. In addition, introducing FOA enables the model to dynamically adjust the hidden layers and thresholds while optimizing the optimal path, thereby finding the best solution. This article conducts ablation experiments on the model using the acceleration life dataset of IEEE PHM 2012 rolling bearings. The experimental results show that the FOA-CNN-LSTM model proposed in this paper significantly outperforms other comparative methods in RUL prediction accuracy and stability, verifying its effectiveness and innovation in dealing with complex degradation processes. This method helps to take preventive measures before faults occur, thereby reducing economic losses and having important practical significance for predicting the remaining life of rolling bearings.