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Intelligent Diagnostics for Bearing Faults Based on Integrated Interaction of Nonlinear Features

机译：基于非线性功能综合交互的轴承故障智能诊断

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摘要

An unforeseen fault of the key bearing of production system due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. To improve the reliability of industry production, this article presents an intelligent diagnosis method for element rolling bearing based on the integrated interaction relationship of vibration nonlinear features. The nonlinear features of vibration signals are extracted using recurrence quantification analysis (RQA) and regrouped into different subsets of nonredundant features with the same level of discrimination ability through the technology of ReliefF-affinity propagation clustering. The weighted voting variable predictive model class discrimination (WV-VPMCD) is proposed to fully utilize the interaction of RQA features to do intelligent diagnostics for bearing faults. The experimental results have showed that the WV-VPMCD outperformes the conventional intelligent diagnosis methods in terms of accuracy, consistency, stability, and robustness, especially in the case of small number of samples.
机译：由于不同的原因，生产系统的关键轴承的不可预见的故障有可能导致整个生产线中断，导致经济和生产损失。为提高行业生产的可靠性，本文介绍了基于振动非线性特征的综合相互作用关系的元件滚动轴承智能诊断方法。使用复制量化分析（RQA）提取振动信号的非线性特征，并通过Relieff-Affinity传播聚类技术重新组合到具有相同级别的辨别能力的非还原功能子集中。提出了加权投票可变预测模型类鉴别（WV-VPMCD）以充分利用RQA功能对轴承故障进行智能诊断的交互。实验结果表明，WV-VPMCD在准确性，一致性，稳定性和稳健性方面优于传统的智能诊断方法，特别是在少量样品的情况下。

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