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The model is mainly based on an improved butterfly optimizer algorithm\u2010 (BOA\u2010) optimized kernel extreme learning machine (KELM) model. Firstly, the roller bearing\u2019s vibration signals in the four states that contain normal state, outer race failure, inner race failure, and rolling ball failure are decomposed into several intrinsic mode functions (IMFs) using the complete ensemble empirical mode decomposition based on adaptive noise (CEEMDAN). Then, the amplitude energy entropies of IMFs are designated as the features of the rolling bearing. In order to eliminate redundant features, a random forest was used to receive the contributions of features to the accuracy of results, and subsets of features were set up by removing one feature in the descending order, using the classification accuracy of the SBOA\u2010KELM model as the criterion to obtain the optimal feature subset. The salp swarm algorithm (SSA) was introduced to BOA to improve optimization ability, obtain optimal KELM parameters, and avoid the BOA deteriorating into local optimization. Finally, an optimal SBOA\u2010KELM model was constructed for the identification of rolling bearings. In the experiment, SBOA was validated against ten other competitive optimization algorithms on 30 IEEE CEC2017 benchmark functions. The experimental results validated that the SBOA was evident over existing algorithms for most function problems. SBOA\u2010KELM employed for diagnosing the fault diagnosis of rolling bearings obtained improved classification performance and higher stability. Therefore, the proposed SBOA\u2010KELM model can be effectively used to diagnose faults of rolling bearings.<\/jats:p>","DOI":"10.1155\/2021\/6315010","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T01:51:32Z","timestamp":1612835492000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Improved Butterfly Optimizer\u2010Configured Extreme Learning Machine for Fault 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