Ge Mao, Lv Yong, Yi Cancan, Zhang Yi, Chen Xiangjun
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Entropy (Basel). 2018 Mar 3;20(3):161. doi: 10.3390/e20030161.
Gears are key components in rotation machinery and its fault vibration signals usually show strong nonlinear and non-stationary characteristics. It is not easy for classical time-frequency domain analysis methods to recognize different gear working conditions. Therefore, this paper presents a joint fault diagnosis scheme for gear fault classification via tensor nuclear norm canonical polyadic decomposition (TNNCPD) and multi-scale permutation entropy (MSPE). Firstly, the one-dimensional vibration data of different gear fault conditions is converted into a three-dimensional tensor data, and a new tensor canonical polyadic decomposition method based on nuclear norm and convex optimization called TNNCPD is proposed to extract the low rank component of the data, which represents the feature information of the measured signal. Then, the MSPE of the extracted feature information about different gear faults can be calculated as the feature vector in order to recognize fault conditions. Finally, this researched scheme is validated by practical gear vibration data of different fault conditions. The result demonstrates that the proposed scheme can effectively recognize different gear fault conditions.
齿轮是旋转机械中的关键部件，其故障振动信号通常表现出强烈的非线性和非平稳特性。经典的时频域分析方法难以识别不同的齿轮工作状态。因此，本文提出了一种基于张量核范数典范多向分解（TNNCPD）和多尺度排列熵（MSPE）的齿轮故障分类联合诊断方案。首先，将不同齿轮故障状态下的一维振动数据转换为三维张量数据，并提出一种基于核范数和凸优化的新的张量典范多向分解方法——TNNCPD，以提取数据的低秩分量，该分量代表测量信号的特征信息。然后，计算提取的不同齿轮故障特征信息的MSPE作为特征向量，以识别故障状态。最后，通过不同故障状态下的实际齿轮振动数据对该研究方案进行验证。结果表明，所提方案能够有效识别不同的齿轮故障状态。
