Zhu Huibin, He Zhangming, Wei Juhui, Wang Jiongqi, Zhou Haiyin
College of Liberal Arts and Sciences, National University of Defense Technology, Changsha 410073, China.
Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China.
Sensors (Basel). 2021 Apr 4;21(7):2524. doi: 10.3390/s21072524.
Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.
轴承是旋转机械中最重要的部件之一，故障率很高，其工作状态直接影响整个设备的性能。因此，诊断轴承故障具有重要意义，这有助于保证运行稳定性和维护，从而提高生产效率和经济效益。通常，轴承故障特征难以有效提取，导致诊断性能较低。为了解决这个问题，本文提出了一种轴承故障特征提取方法，并建立了基于特征融合的轴承故障诊断方法。该方法的基本思想如下：首先，通过小波包变换（WPT）提取轴承信号的时频特征，形成信号的时频特征矩阵；其次，根据奇异值贡献率和熵权构建多权重奇异值分解（MWSVD）。对WPT得到的时频特征矩阵的特征进行进一步提取，保留时频特征矩阵中对故障敏感的特征，去除不敏感的特征；最后，将提取的特征矩阵作为支持向量机（SVM）分类器的输入进行轴承故障诊断。通过渥太华大学时变轴承数据和凯斯西储大学轴承数据中心的数据集对所提方法进行了验证。结果表明，该算法能够有效地诊断稳态和非稳态下的轴承故障。本文提出，与基于传统特征技术的方法相比，该算法具有更好的故障诊断能力和特征提取能力。
