A novel intelligent fault diagnosis method based on dual convolutional neural network with multi-level information fusion

Due to the complicacy of mechanical instruments and the noise interference in the working environment, the equipment status information contained in a single sensor is insufficient, and multi-source information contains more complete status information. In order to effectively fuse multi-sensor information and improve the reliability of diagnosis, a multi-level fusion dual convolution neural network (MFDCNN) for fault diagnosis of rotating machinery is proposed in this paper. This approach realizes multi-level fusion of fault information by utilizing the flexibility of the structure of the convolutional neural network. During the training process, the two subnets automatically extract representative features from the multi-sensor timedomain signal and its frequency spectrum in parallel, and then fuse the extracted features for pattern recognition to achieve end-to-end fault diagnosis. Compared with the single sensor diagnosis method and single level information fusion method, this approach has better diagnosis performance.

Automated summary

This paper proposes a neural network model for fault diagnosis of rotating machinery, which utilizes multi-sensor information for multi-level fusion, improving the reliability and accuracy of diagnosis.