Multi-strategy hippopotamus algorithm for rolling bearing life prediction by BiGRU

Abstract:

To predict the remaining life of rolling bearings more accurately, this paper proposed a bearing life prediction method based on the fusion of the multi-strategy hippopotamus algorithm (TOBCHO: adaptive t-distribution, optimal-worst opposite learning, and chaos mapping) and bi-directional gated recurrent unit (BiGRU). Firstly, feature extraction was performed for the whole life cycle signal, and comprehensive evaluation indexes were established based on correlation, monotonicity, and robustness. Sensitive feature vectors were screened as the sensitive feature set, and principal component analysis (PCA) technology was used to construct the health index curve. Then, for the problem that it was difficult for BiGRU to determine the hyperparameters in rolling bearing life prediction research, a life prediction model of TOBCHO-optimized BiGRU (TOBCHO-BiGRU) was proposed, which introduced the optimal worst opposition-based learning mechanism in the population initialization stage of the hippopotamus algorithm and generated the opposing solution to expand the search space. The chaos mapping sequence was adopted to replace the generation of random numbers to solve the problem of unstable convergence of the algorithm. The adaptive distribution perturbation strategy for optimal individual was introduced in the late iteration stage of the hippopotamus optimization (HO) algorithm, and the perturbation strength was dynamically adjusted to balance the local development and global search capability. Finally, the experimental validation was conducted on the internationally used IEEE PHM 2012 bearing dataset, and the proposed model was compared with a variety of other prediction models. The results adequately show that the proposed TOBCHO-BiGRU method has a significant advantage in terms of prediction accuracy. Ablation experiment results demonstrate that there are positive synergistic effects among improvement strategies, promoting the enhancement of the HO algorithm, which provides a high-precision solution for the rolling bearing life prediction under complex working conditions.