基于优化A-BiLSTM的滚动轴承故障诊断  

Rolling bearing fault diagnosis based on optimized A-BiLSTM

作　　者：余萍[1,2,3] 赵康 曹洁[1] YU Ping;ZHAO Kang;CAO Jie(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou University of Technology,Lanzhou 730050,China;National Demonstration Center for Experimental Electrical and Control Engineering Education,Lanzhou University of Technology,Lanzhou 730050,China)

机构地区：[1]兰州理工大学电气工程与信息工程学院,兰州730050 [2]甘肃省工业过程控制重点实验室,兰州730050 [3]兰州理工大学电气与控制工程国家级实验教学示范中心,兰州730050

出　　处：《吉林大学学报（工学版）》2024年第8期2156-2166,共11页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家重点研发项目(2020YFB1713600);国家自然科学基金项目(62241307);甘肃省科技计划项目(22YF7FA166);兰州市科技计划项目(2022-RC-60);甘肃省教育厅高等学校创新基金项目(2021A-027).

摘　　要：为提高超参数设置的效率及其与模型的适配性,改善人工设置模型参数的高成本和低效率问题,提出一种基于蜜獾算法(Honey badger algorithm,HBA)优化注意力双向长短时记忆网络(HBA-A-BiLSTM)的滚动轴承故障诊断方法。首先,通过HBA对A-BiLSTM模型进行最优超参数组合搜寻,然后基于最优超参数下的A-BiLSTM模型进行故障诊断性能测试。最后,基于不同工况的数据集进行模型泛化能力测试。采用CWRU数据集对所提方法的故障诊断效果进行验证,利用诊断精度以及混淆矩阵进行评价。实验结果表明,与其他群智能优化算法相比,蜜獾算法搜索全局性能好,收敛速度快,优化后的最终模型的故障诊断准确率达到了99.5%,具有良好的效果,且在不同工况下能够实现稳定、准确的故障诊断性能,泛化能力强。In order to improve the efficiency of hyperparameter setting and its adaptability to the model,and break the high cost and low efficiency of manual parameter setting,a fault diagnosis method for rolling bearings based on the honey badger algorithm(HBA)optimizing bi-directional long short-term memory(BiLSTM)with attention mechanism(HBA-A-BiLSTM)is proposed.Firstly,search the optimal hyperparameter combination of the A-BiLSTM model through HBA.Secondly,the fault diagnosis performance is tested based on the A-BiLSTM model under the optimal hyperparameters.Finally,the generalization ability of the model is tested based on the datasets under different working conditions.The CWRU dataset is used to verify the fault diagnosis effect of the proposed method,which is used the diagnostic accuracy and the confusion matrix to evaluate.It is shown that,compared with other swarm intelligence optimization algorithms,the HBA has better global searching performance and faster convergence speed.The fault diagnosis accuracy of the optimized model has reached 99.5%,which has a good effect,also under different working conditions,it can achieve stable and accurate fault diagnosis performance,and has strong generalization ability.

关 键 词：故障诊断 蜜獾算法 参数优化 双向长短时记忆网络 注意力机制 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]