Multi-stage Fault Diagnosis Framework for Rolling Bearing Based on OHF Elman AdaBoost-Bagging Algorithm
Multi-stage Fault Diagnosis Framework for Rolling Bearing Based on OHF Elman AdaBoost-Bagging Algorithm
复制标题
基于OHF Elman AdaBoost-Bagging算法的滚动轴承多级故障诊断框架
DOI：
10.1016/j.neucom.2020.10.003
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发表时间：
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期刊：
影响因子：
6
通讯作者：
Lifeng Xi
中科院分区：
文献类型：
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作者：
Tangbin Xia;Pengcheng Zhuo;Lei Xiao;Shichang Du;Dong Wang;Lifeng Xi
随着工业设备的日益复杂，迫切需要提供及时的诊断和准确的评估，以避免故障。对于滚动轴承，实现随机噪声环境下的早期、中期、后期故障诊断是一个重要的研究课题。与传统方法不同，在综合诊断框架下，提出了输出隐反馈Elman自适应Boosting-Bootstrap聚集算法。该方法首先对原始信号进行包络经验模式分解（Energy-Empirical Mode Decomposition，EMD）分解、去噪和重构。然后，在Elman神经网络的基础上，通过增加一个输出层到隐层的反馈，设计了OHF Elman神经网络。这改善了滚动轴承动态数据的记忆功能。此外，为了保证诊断的准确性和算法的稳定性，通过AdaBoost算法和Bagging算法的双重融合，将OHF Elman AdaBoost-Bagging算法发展为强学习器。实验结果表明，该算法不仅对滚动轴承故障的不同阶段具有良好的诊断性能，而且具有较高的泛化能力和稳定性。这种多阶段故障诊断框架为滚动轴承故障诊断提供了一种新的工具和有效的解决方案。
With the increasing complexity of industrial equipment, it is urgent to provide timely diagnosis and accurate evaluation to avoid failure. For rolling bearings, it is important to achieve the multi-stage (incipient, intermediate, late) fault diagnosis under random noise. Different from traditional methods, an Output Hidden Feedback Elman Adaptive Boosting-Bootstrap Aggregating algorithm is proposed under a comprehensive diagnosis framework. First, the original signal is decomposed, denoised and reconstructed by Ensemble Empirical Mode Decomposition. Then, OHF Elman neural network is designed by increasing a feedback from the output layer to the hidden layer based on Elman neural network. This improves the memory function for dynamic data of rolling bearings. Furthermore, for achieving diagnostic accuracy and algorithm stability, OHF Elman AdaBoost-Bagging algorithm is developed as a strong learner through the dual integration of AdaBoost algorithm and Bagging algorithm. Experimental results show that the proposed algorithm not only has a good diagnostic performance on different stages of rolling bearing faults, but also achieves higher generalization ability and stability. This multi-stage fault diagnosis framework provides a novel tool and an effective solution for rolling bearing fault diagnosis.