School of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu Province, China.
Comput Intell Neurosci. 2022 Jun 3;2022:9312876. doi: 10.1155/2022/9312876. eCollection 2022.
The rotor, as the power output device of a cage motor, is subject to a type of invisible fault, BRB, during long-term use. The conventional motor vibration signal fault monitoring system only analyzes the rotor qualitatively for the fault of BRBs and cannot evaluate the fault degree of BRBs quantitatively. Moreover, the vibration signal used for monitoring has nonstationary and nonlinear characteristics. It is necessary to manually determine the time window and basis function when extracting the characteristics of the time-frequency domain. To address these problems, this paper proposes a method for quantitative analysis of BRBs based on CEEMD decomposition and weight transformation for feature extraction and then uses the AdaBoost to construct a classifier. The method applies CEEMD for adaptive decomposition while extracting IMFs' energy as the initial feature values, uses OOB for contribution evaluation of features to construct weight vectors, and performs a spatial transformation on the original feature values to expand the differences between the feature vectors. To verify the effectiveness and superiority of the method, vibration signals were collected from motors in four BRB states to produce rotor fault data sets in this paper. The experiment results show that the feature extraction method based on CEEMD decomposition and weight transformation can better extract the feature vectors from the vibration signals, and the constructed classifier can accurately perform quantitative analysis of BRB fault.
转子作为笼式电机的动力输出装置，在长期使用过程中会出现一种无形的故障，即 BRB。传统的电机振动信号故障监测系统仅对 BRB 故障进行定性分析，无法对 BRB 故障程度进行定量评估。而且，用于监测的振动信号具有非平稳和非线性的特点。在提取时频域特征时，需要手动确定时间窗口和基函数。针对这些问题，本文提出了一种基于 CEEMD 分解和权重变换的 BRB 定量分析方法，用于特征提取，然后使用 AdaBoost 构建分类器。该方法应用 CEEMD 进行自适应分解，同时提取 IMF 的能量作为初始特征值，使用 OOB 对特征的贡献进行评估以构建权重向量，并对原始特征值进行空间变换以扩大特征向量之间的差异。为了验证该方法的有效性和优越性，本文从四种 BRB 状态的电机中采集振动信号，生成转子故障数据集。实验结果表明，基于 CEEMD 分解和权重变换的特征提取方法可以更好地从振动信号中提取特征向量，构建的分类器可以准确地对 BRB 故障进行定量分析。
