Dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions

US12044595B2 · kind B2 · utility

Assignee

• Soochow University · CN

Inventors

• ChangQing Shen · Suzhou, CN
• Shuangjie Liu · Suzhou, CN
• Xu Wang · Beijing, CN
• Dong Wang · Shanghai, CN
• Yongjun Shen · Suzhou, CN
• Zaigang Chen · Suzhou, CN
• Aiwen Zhang · Suzhou, CN
• Xingxing JIANG · Suzhou, CN
• Juanjuan SHI · Beijing, CN
• Weiguo Huang · Suzhou, CN
• Jun Wang · Nanjing, CN
• Guifu Du · Suzhou, CN
• Zhongkui ZHU · Suzhou, CN

Key dates

• Filing date: Jan 13, 2021
• Grant date: Jul 23, 2024
• Expiry date: Jan 20, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/096
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The present invention provides a dynamic joint distribution alignment network-based bearing fault diagnosis method under variable working conditions, including acquiring bearing vibration data under different working conditions to obtain a source domain sample and a target domain sample; establishing a deep convolutional neural network model with dynamic joint distribution alignment; feeding both the source domain sample and the target domain sample into the deep convolutional neural network model with initialized parameters, and extracting, by a feature extractor, high-level features of the source domain sample and the target domain sample; calculating a marginal distribution distance and a conditional distribution distance; obtaining a joint distribution distance according to the marginal distribution distance and the conditional distribution distance, and combining the joint distribution distance and a label loss to obtain a target function; and optimizing the target function by using SGD, and training the deep convolutional neural network model.