Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism

To address the issues of slow diagnostic speed, low accuracy, and poor generalization performance in traditional rolling bearing fault diagnosis methods, we propose a rolling bearing fault diagnosis method based on Markov Transition Field (MTF) image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module (CBAM-LCNN). Specifically, we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images. Then, we construct a lightweight convolutional neural network incorporating the convolutional attention module (CBAM-LCNN). Finally, the two-dimensional images obtained from MTF mapping are fed into the CBAM-LCNN network for image feature extraction and fault diagnosis. We validate the effectiveness of the proposed method on the bearing fault datasets from Guangdong University of Petrochemical Technology’s multi-stage centrifugal fan and Case Western Reserve University. Experimental results show that, compared to other advanced baseline methods, the proposed rolling bearing fault diagnosis method offers faster diagnostic speed and higher diagnostic accuracy. In addition, we conducted experiments on the Xi’an Jiaotong University rolling bearing dataset, achieving excellent results in bearing fault diagnosis. These results validate the strong generalization performance of the proposed method. The method presented in this paper not only effectively diagnoses faults in rolling bearings but also serves as a reference for fault diagnosis in other equipment.