Soochow University

Xiaofeng Shang

Jun Wang

Zhongkui Zhu

Weiguo Huang

Changqing Shen

Xu Wang

Xingxing Jiang

Dong Wang

Juanjuan Shi

Dongmiao Song

Aiwen Zhang

Jing Xie

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.

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

Growing community of inventors

Suzhou, China

Xiaofeng Shang