A rolling bearing fault diagnosis method based on improved Aquila optimization algorithm to optimize LSTM

Here, aiming at problems of Aquila optimization (AO) algorithm being easy to fall into local optimization and long short-term memory (LSTM) neural network being easily affected by parameters, a model based on improved Aquila optimizer (IAO) algorithm for optimizing LSTM neural network was proposed and applied in fault diagnosis of rolling bearings. Firstly, the hypercube strategy was introduced to optimize the initial quality of a population, and the adaptive spiral strategy was designed to balance the global and local search abilities of AO algorithm. Gaussian mutation strategy was used to enhance the ability of AO algorithm for jumping out from local optimization. Then, the proposed IAO algorithm was used to optimize weights and thresholds of LSTM neural network for constructing a rolling bearing fault diagnosis model based on IAO algorithm-optimized LSTM neural network. Finally, Simulation test results of Case Western Reserve University (CWRU) bearing dataset and Paderborn University (PU) bearing dataset showed that compared with other fault diagnosis models, IAO algorithm-optimized LSTM neural network model has higher Classification accuracy and can effectively identify various fault types of rolling bearings. © 2024 Chinese Vibration Engineering Society. All rights reserved.