Xu Ming, Gao Jinfeng, Zhang Zhong, Wang Heshan
College of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
Entropy (Basel). 2022 Oct 31;24(11):1569. doi: 10.3390/e24111569.
Deep learning bearing-fault diagnosis has shown strong vitality in recent years. In industrial practice, the running state of bearings is monitored by collecting data from multiple sensors, for instance, the drive end, the fan end, and the base. Given the complexity of the operating conditions and the limited number of bearing-fault samples, obtaining complementary fault features using the traditional fault-diagnosis method, which uses statistical characteristic in time or frequency, is difficult and relies heavily on prior knowledge. In addition, intelligent bearing-fault diagnosis based on a convolutional neural network (CNN) has several deficiencies, such as single-scale fixed convolutional kernels, excessive dependence on experts' experience, and a limited capacity for learning a small training dataset. Considering these drawbacks, a novel intelligent bearing-fault-diagnosis method based on signal-to-RGB image mapping (STRIM) and multichannel multiscale CNN (MCMS-CNN) is proposed. First, the signals from three different sensors are converted into RGB images by the STRIM method to achieve feature fusion. To extract RGB image features effectively, the proposed MCMS-CNN is established, which can automatically learn complementary and abundant features at different scales. By increasing the width and decreasing the depth of the network, the overfitting caused by the complex network for a small dataset is eliminated, and the fault classification capability is guaranteed simultaneously. The performance of the method is verified through the Case Western Reserve University's (CWRU) bearing dataset. Compared with different DL approaches, the proposed approach can effectively realize fault diagnosis and substantially outperform other methods.
近年来，深度学习轴承故障诊断展现出了强大的生命力。在工业实践中，通过从多个传感器收集数据来监测轴承的运行状态，例如驱动端、风扇端和基座。鉴于运行条件的复杂性以及轴承故障样本数量有限，使用传统的基于时间或频率统计特征的故障诊断方法来获取互补的故障特征既困难又严重依赖先验知识。此外，基于卷积神经网络（CNN）的智能轴承故障诊断存在一些缺陷，如单尺度固定卷积核、过度依赖专家经验以及对小训练数据集的学习能力有限。考虑到这些缺点，提出了一种基于信号到RGB图像映射（STRIM）和多通道多尺度CNN（MCMS-CNN）的新型智能轴承故障诊断方法。首先，通过STRIM方法将来自三个不同传感器的信号转换为RGB图像以实现特征融合。为了有效提取RGB图像特征，建立了所提出的MCMS-CNN，它可以在不同尺度上自动学习互补且丰富的特征。通过增加网络宽度并减小网络深度，消除了小数据集复杂网络导致的过拟合，同时保证了故障分类能力。通过凯斯西储大学（CWRU）的轴承数据集验证了该方法的性能。与不同的深度学习方法相比，所提出的方法能够有效地实现故障诊断，并且显著优于其他方法。
