Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network

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.

Automated summary

This paper proposes a rolling-bearing fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. By using a sliding window to augment the raw vibration signal data and converting vibration signal samples into two-dimensional images, the model performs feature extraction through a deep residual neural network in image classification to identify the severity and location of bearing faults. Experimental results show that MTF-ResNet model has better average accuracy compared to other diagnostic methods.