Fault diagnosis based on convolutional autoencoders combined with multivariate information fusion

Abstract

A single type of sensor signal cannot fully represent the operational status of mechanical equipment, leading to incomplete state characterization and inaccurate diagnostics. This paper proposes an innovative fault diagnosis method based on Convolutional AutoEncoders combined with multivariate information fusion to accurately identify the overall health status of bearings by analyzing various sensor data. Our approach leverages the Convolutional AutoEncoders to effectively integrate heterogeneous sensor data from multiple sources, including vibration and sound signals, with data augmentation and normalization techniques for preprocessing, thereby improving the model’s generalization capability and accuracy. Furthermore, the integration of K-means clustering and a Sparse Attention mechanism enables precise recognition of critical fault features. The model’s effectiveness is validated through comprehensive performance evaluation using confusion matrices and visualization techniques. Experimental results demonstrate that our method achieves high accuracy and robustness in fault diagnosis tasks, offering a significant advancement in intelligent maintenance and fault prediction of rolling bearings by addressing the limitations of traditional methods.