Zhang Weibo, Zhou Jianzhong
School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Entropy (Basel). 2019 Jul 11;21(7):680. doi: 10.3390/e21070680.
This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.
本研究提出了一种用于滚动轴承的综合故障诊断方法。该方法包括两个部分：故障检测和故障分类。在故障检测阶段，定义了基于局部最大尺度的精细复合多尺度分散熵（RCMDE）的阈值来判断滚动轴承的健康状态。如果轴承出现故障，则开发一种广义多尺度特征提取方法，通过结合快速集成经验模态分解（FEEMD）和RCMDE来充分提取故障信息。首先，通过FEEMD将故障振动信号分解为一组固有模态函数（IMF）。其次，计算多个IMF的RCMDE值以生成候选特征池。然后，采用最大相关性和最小冗余度（mRMR）方法从候选特征池中选择敏感特征以构建最终特征向量，并将最终特征向量输入随机森林（RF）分类器以识别不同的故障工作条件。最后，进行实验和对比研究以验证所提方法的性能。结果表明，所提方法能够有效地检测故障。同时，与其他传统方法相比，它在识别不同故障类型和严重程度方面具有更强健和优异的能力。
