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.

An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

2021 ◽  
pp. 095440622110329
Author(s):  
Jianqun Zhang ◽  
Qing Zhang ◽  
Xianrong Qin ◽  
Yuantao Sun
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Rolling Bearing ◽  
Training Data ◽  
Variable Load ◽  
K Nearest Neighbor ◽  
Target Domain ◽  
Bearing Faults ◽  
Load Conditions

To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

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 Tensor Transfer Learning for Intelligence Fault Diagnosis of Bearing with Semisupervised Partial Label Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaofan Hu ◽  
Zhichao Zhou ◽  
Biao Wang ◽  
WeiGuang Zheng ◽  
Shuilong He
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Subspace Learning ◽  
Rotating Machinery ◽  
Training Data ◽  
Tensor Representation ◽  
Vibration Signals ◽  
Tensor Representations ◽  
Partial Label Learning ◽  
And Training

A new tensor transfer approach is proposed for rotating machinery intelligent fault diagnosis with semisupervised partial label learning in this paper. Firstly, the vibration signals are constructed as a three-way tensor via trial, condition, and channel. Secondly, for adapting the source and target domains tensor representations directly, without vectorization, the domain adaptation (DA) approach named tensor-aligned invariant subspace learning (TAISL) is first proposed for tensor representation when testing and training data are drawn from different distribution. Then, semisupervised partial label learning (SSPLL) is first introduced for tackling a problem that it is hard to label a large number of instances and there exists much data left to be unlabeled. Ultimately, the proposed method is used to identify faults. The effectiveness and feasibility of the proposed method has been thoroughly validated by transfer fault experiments. The experimental results show that the presented technique can achieve better performance.

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

2019 ◽  
Vol 234 (1) ◽  
pp. 168-182 ◽  
Author(s):  
Jialin Li ◽  
Xueyi Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Deep Neural Network ◽  
Domain Adaptation ◽  
Diagnostic Model ◽  
Target Domain ◽  
Vibration Signals ◽  
Learning Network

In recent years, research on gear pitting fault diagnosis has been conducted. Most of the research has focused on feature extraction and feature selection process, and diagnostic models are only suitable for one working condition. To diagnose early gear pitting faults under multiple working conditions, this article proposes to develop a domain adaptation diagnostic model–based improved deep neural network and transfer learning with raw vibration signals. A particle swarm optimization algorithm and L2 regularization are used to optimize the improved deep neural network to improve the stability and accuracy of the diagnosis. When using the domain adaptation diagnostic model for fault diagnosis, it is necessary to discriminate whether the target domain (test data) is the same as the source domain (training data). If the target domain and the source domain are consistent, the trained improved deep neural network can be used directly for diagnosis. Otherwise, the transfer learning is combined with improved deep neural network to develop a deep transfer learning network to improve the domain adaptability of the diagnostic model. Vibration signals for seven gear types with early pitting faults under 25 working conditions collected from a gear test rig are used to validate the proposed method. It is confirmed by the validation results that the developed domain adaptation diagnostic model has a significant improvement in the adaptability of multiple working conditions.

scholarly journals Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

2021 ◽  
Vol 13 (8) ◽  
pp. 168781402110402
Author(s):  
Jiajie Shao ◽  
Zhiwen Huang ◽  
Yidan Zhu ◽  
Jianmin Zhu ◽  
Dianjun Fang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Diagnostic Performance ◽  
Domain Adaptation ◽  
Rotating Machinery ◽  
Learning Method ◽  
Migration Ability ◽  
Adversarial Learning ◽  
Machinery Fault Diagnosis

Rotating machinery fault diagnosis is very important for industrial production. Many intelligent fault diagnosis technologies are successfully applied and achieved good results. Due to the fact that machine damages usually happen under different working conditions, and manual scale labeled data are too expensive, domain adaptation has been developed for fault diagnosis. However, the current methods mostly focus on global domain adaptation, the application of subdomain adaptation for fault diagnosis is still limited. A deep transfer learning method is proposed for rotating machinery fault diagnosis in this study, where subdomain adaptation and adversarial learning are introduced to align local feature distribution and global feature distribution separately. Experiments are performed on two rotating machinery datasets to verify the effectiveness of this method. The results reveal that this method has outstanding mutual migration ability and can improve the diagnostic performance.

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