He Kecheng, Xu Yanwei, Wang Yun, Wang Junhua, Xie Tancheng
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Intelligent Numerical Control Equipment Engineering Laboratory of Henan Province, Luoyang 471003, China.
Sensors (Basel). 2023 Jul 10;23(14):6281. doi: 10.3390/s23146281.
Existing diagnosis methods for bearing faults often neglect the temporal correlation of signals, resulting in easy loss of crucial information. Moreover, these methods struggle to adapt to complex working conditions for bearing fault feature extraction. To address these issues, this paper proposes an intelligent diagnosis method for compound faults in metro traction motor bearings. This method combines multisignal fusion, Markov transition field (MTF), and an optimized deep residual network (ResNet) to enhance the accuracy and effectiveness of diagnosis in the presence of complex working conditions. At the outset, the acquired vibration and acoustic emission signals are encoded into two-dimensional color feature images with temporal relevance by Markov transition field. Subsequently, the image features are extracted and fused into a set of comprehensive feature images with the aid of the image fusion framework based on a convolutional neural network (IFCNN). Afterwards, samples representing different fault types are presented as inputs to the optimized ResNet model during the training phase. Through this process, the model's ability to achieve intelligent diagnosis of compound faults in variable working conditions is realized. The results of the experimental analysis verify that the proposed method can effectively extract comprehensive fault features while working in complex conditions, enhancing the efficiency of the detection process and achieving a high accuracy rate for the diagnosis of compound faults.
现有的轴承故障诊断方法往往忽略信号的时间相关性，导致关键信息容易丢失。此外，这些方法难以适应复杂工况下的轴承故障特征提取。为解决这些问题，本文提出一种地铁牵引电机轴承复合故障智能诊断方法。该方法结合多信号融合、马尔可夫转移场（MTF）和优化的深度残差网络（ResNet），以提高在复杂工况下诊断的准确性和有效性。首先，通过马尔可夫转移场将采集到的振动和声发射信号编码为具有时间相关性的二维彩色特征图像。随后，借助基于卷积神经网络的图像融合框架（IFCNN）提取图像特征并融合成一组综合特征图像。之后，在训练阶段将代表不同故障类型的样本作为输入提供给优化后的ResNet模型。通过这一过程，实现了模型在可变工况下对复合故障进行智能诊断的能力。实验分析结果验证了所提方法在复杂工况下工作时能够有效提取综合故障特征，提高检测过程的效率，并实现复合故障诊断的高准确率。
