Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1 Jin-De Road, Changhua City 50007, Taiwan.
Sensors (Basel). 2021 Nov 10;21(22):7467. doi: 10.3390/s21227467.
Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.
滚动轴承在旋转机械和设备中起着重要作用。本研究提出了变分模态分解(VMD)-DenseNet 来诊断轴承故障。研究的特点包括通过 VMD 分析希尔伯特谱，从而将振动信号转换为图像。健康和各种故障在图像上表现出不同的特征，因此无需选择特征。结合轻量级网络 DenseNet 进行图像分类和预测。DenseNet 用于构建电机故障诊断模型；其结构简单，计算速度快。使用 DenseNet 进行图像特征学习的方法可以对图像的每个图像块进行特征提取，充分发挥深度学习的优势，获得准确的结果。该研究方法通过渥太华大学的时变轴承实验装置的数据得到验证。通过信号采集、特征提取、故障识别和预测这四个环节，建立了机械智能故障诊断系统的轴承状态。实验结果表明，该方法能够准确识别四种常见的电机故障，VMD-DenseNet 的预测准确率达到 92%。为轴承故障诊断提供了更有效的方法，在故障诊断工程中有广泛的应用前景。未来，可以实现智能故障的在线和实时诊断。
