School of Technology, Beijing Forestry University, Beijing 100083, China.
Key Laboratory of State Forestry Administration on Forestry Equipment and Automation, Beijing 100083, China.
Sensors (Basel). 2022 May 23;22(10):3936. doi: 10.3390/s22103936.
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods.
基于数据驱动的滚动轴承故障诊断方法大多基于深度学习模型，其多层非线性映射能力可以提高智能故障诊断的准确性。但是，随着网络层数的增加，会出现梯度消失等问题。此外，直接将滚动轴承的原始振动信号作为网络输入会导致特征提取不完整。为了以图像形式有效地表示振动信号的状态特征并提高网络的特征学习能力，本文提出了基于马尔可夫转移场和深度残差网络的故障诊断模型 MTF-ResNet。首先，使用滑动窗口对原始振动信号数据进行扩充。然后，通过 MTF 将振动信号样本转换为二维图像，保留了时间序列信号的时间相关性和频率结构，并建立深度残差神经网络进行特征提取，并通过图像分类识别轴承故障的严重程度和位置。最后，在轴承数据集上进行了实验，验证了 MTF-ResNet 模型的有效性和优越性。通过 t-SNE 对模型学习到的特征进行可视化，实验结果表明，与几种常用的诊断方法相比，MTF-ResNet 表现出更好的平均准确率。
