Cross-domain mechanical fault diagnosis method based on multi-channel feature fusion of CBAM and use thereof

US12313497B2 · kind B2 · utility

Assignee

• SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY, MINISTRY OF NATURAL RESOURCES · CN

Inventors

• Huaiqian Bao · Qingdao, CN
• Jinrui Wang · Great Neck, US
• Zongzhen Zhang · Fuzhou, CN
• Baokun Han · Qingdao, CN
• Shuo Xing · Beijing, CN

Key dates

• Filing date: Oct 25, 2023
• Grant date: May 27, 2025
• Expiry date: Oct 25, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06F18/253
• WIPO field: Measurement
• WIPO sector: Instruments

Abstract

A cross-domain mechanical fault diagnosis method based on multi-channel feature fusion of a CBAM and use thereof includes conducting preliminary feature extraction in a grey-scale graph formed by original signals with convolutional neural network, obtaining high-level features, and compressing the high-level features with a full-connection layer module; conducting deep-level multi-sensor feature extraction with an improved convolutional block attention module (CBAM); conducting fusion for multi-sensor features extracted with an improved convolutional block attention module and obtaining multi-sensor fusion features; and inputting the multi-sensor fusion features into a tag assignor for fault diagnosis results. In the present invention, the latest multi-channel domain adaptation fault diagnosis method is used to realize efficiently intelligent fault diagnosis tasks of bearings in different working states.