Towards Prediction Constraints: A Novel Domain Adaptation Method for Machine Fault Diagnosis

2021 ◽  
pp. 1-1
Author(s):  
Jinyang Jiao ◽  
Kaixuan Liang ◽  
Chuancang Ding ◽  
Jing Lin
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Adaptation Method

A multi-source ensemble domain adaptation method for rotary machine fault diagnosis

Measurement ◽  
2021 ◽  
pp. 110213
Author(s):  
Shengkang Yang ◽  
Xianguang Kong ◽  
Qibin Wang ◽  
Zhongquan Li ◽  
Han Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rotary Machine ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Adaptation Method

Intelligent cross-machine fault diagnosis approach with deep auto-encoder and domain adaptation

2020 ◽  
Vol 383 ◽  
pp. 235-247 ◽  
Author(s):  
Xiang Li ◽  
Xiao-Dong Jia ◽  
Wei Zhang ◽  
Hui Ma ◽  
Zhong Luo ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
Diagnosis Approach

scholarly journals Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3753
Author(s):  
Xiaodong Wang ◽  
Feng Liu ◽  
Dongdong Zhao
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Rotating Machinery ◽  
Working Environment ◽  
Training Data ◽  
Target Domain ◽  
Machine Fault Diagnosis ◽  
Machine Fault ◽  
The Cross

Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.

Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals

Measurement ◽  
2021 ◽  
Vol 176 ◽  
pp. 109186
Author(s):  
Dengyu Xiao ◽  
Chengjin Qin ◽  
Honggan Yu ◽  
Yixiang Huang ◽  
Chengliang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Acoustic Signals ◽  
Denoising Autoencoder ◽  
Machine Fault Diagnosis ◽  
Machine Fault
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