Intra-class adaptation fault diagnosis method for bearing under variable working conditions

US11886311B2 · kind B2 · utility

Assignee

• Soochow University · CN

Inventors

• ChangQing Shen · Suzhou, CN
• Xu Wang · Beijing, CN
• Jing Xie · Lo Wu, CN
• Aiwen Zhang · Suzhou, CN
• Dong Wang · Shanghai, CN
• Xiaofeng Shang · Suzhou, CN
• Dongmiao Song · Suzhou, CN
• Xingxing JIANG · Suzhou, CN
• Jun Wang · Nanjing, CN
• Juanjuan SHI · Beijing, CN
• Weiguo Huang · Suzhou, CN
• Zhongkui ZHU · Suzhou, CN

Key dates

• Filing date: Aug 4, 2020
• Grant date: Jan 30, 2024
• Expiry date: Jan 16, 2041

Classification

• Technology area (CPC Y): Emerging Cross-Sectional Technologies
• CPC primary: Y02T90/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

The invention relates to a fault diagnosis method for a rolling bearing under variable working conditions. Based on a convolutional neural network, a transfer learning algorithm is combined to handle the problem of the reduced universality of deep learning models. Data acquired under different working conditions is segmented to obtain samples. The samples are preprocessed by using FFT. Low-level features of the samples are extracted by using improved ResNet-50, and a multi-scale feature extractor analyzes the low-level features to obtain high-level features as inputs of a classifier. In a training process, high-level features of training samples and test samples are extracted, and a conditional distribution distance between them is calculated as a part of a target function for backpropagation to implement intra-class adaptation, thereby reducing the impact of domain shift, to enable a deep learning model to better carry out fault diagnosis tasks.