scholarly journals A New Universal Domain Adaptive Method for Diagnosing Unknown Bearing Faults

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1052
Author(s):  
Zhenhao Yan ◽  
Guifang Liu ◽  
Jinrui Wang ◽  
Huaiqian Bao ◽  
Zongzhen Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Adaptive Method ◽  
Transfer Model ◽  
Target Domain ◽  
Network Failure ◽  
Multiple Datasets ◽  
Mechanical Faults ◽  
Universal Domain

The domain adaptation problem in transfer learning has received extensive attention in recent years. The existing transfer model for solving domain alignment always assumes that the label space is completely shared between domains. However, this assumption is untrue in the actual industry and limits the application scope of the transfer model. Therefore, a universal domain method is proposed, which not only effectively reduces the problem of network failure caused by unknown fault types in the target domain but also breaks the premise of sharing the label space. The proposed framework takes into account the discrepancy of the fault features shown by different fault types and forms the feature center for fault diagnosis by extracting the features of samples of each fault type. Three optimization functions are added to solve the negative transfer problem when the model solves samples of unknown fault types. This study verifies the performance advantages of the framework for variable speed through experiments of multiple datasets. It can be seen from the experimental results that the proposed method has better fault diagnosis performance than related transfer methods for solving unknown mechanical faults.

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.

scholarly journals Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 320 ◽  
Author(s):  
Xiaodong Wang ◽  
Feng Liu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Training Dataset ◽  
Great Success ◽  
Target Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Class Level ◽  
Triplet Loss ◽  
Data Level

Recently, deep learning methods are becomingincreasingly popular in the field of fault diagnosis and achieve great success. However, since the rotation speeds and load conditions of rotating machines are subject to change during operations, the distribution of labeled training dataset for intelligent fault diagnosis model is different from the distribution of unlabeled testing dataset, where domain shift occurs. The performance of the fault diagnosis may significantly degrade due to this domain shift problem. Unsupervised domain adaptation has been proposed to alleviate this problem by aligning the distribution between labeled source domain and unlabeled target domain. In this paper, we propose triplet loss guided adversarial domain adaptation method (TLADA) for bearing fault diagnosis by jointly aligning the data-level and class-level distribution. Data-level alignment is achieved using Wasserstein distance-based adversarial approach, and the discrepancy of distributions in feature space is further minimized at class level by the triplet loss. Unlike other center loss-based class-level alignment approaches, which hasto compute the class centers for each class and minimize the distance of same class center from different domain, the proposed TLADA method concatenates 2 mini-batches from source and target domain into a single mini-batch and imposes triplet loss to the whole mini-batch ignoring the domains. Therefore, the overhead of updating the class center is eliminated. The effectiveness of the proposed method is validated on CWRU dataset and Paderborn dataset through extensive transfer fault diagnosis experiments.

scholarly journals A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 424
Author(s):  
Sixiang Jia ◽  
Jinrui Wang ◽  
Xiao Zhang ◽  
Baokun Han
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Local Maximum ◽  
Fault Classification ◽  
Target Domain ◽  
Local Data ◽  
Source Domain ◽  
Fine Grained ◽  
Realistic Situation ◽  
Partial Transfer

Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper. Our method pays more attention to the local data distribution while aligning the global distribution. An auxiliary classifier is introduced to obtain the class-level weights of the source samples, so the network can avoid negative transfer caused by unique fault classes in the source domain. Furthermore, a weighted local maximum mean discrepancy (WLMMD) is proposed to capture the fine-grained transferable information and obtain sample-level weights. Finally, relevant distributions of domain-specific layer activations across different domains are aligned. Experimental results show that our method could assign appropriate weights to each source sample and realize efficient partial transfer fault diagnosis.

scholarly journals Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions

2020 ◽  
Vol 319 ◽  
pp. 03001
Author(s):  
Weigui Li ◽  
Zhuqing Yuan ◽  
Wenyu Sun ◽  
Yongpan Liu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Domain Adaptation ◽  
Transfer Problem ◽  
Intelligent Manufacturing ◽  
Maximum Mean Discrepancy ◽  
Bearing Fault Diagnosis ◽  
Cross Domain ◽  
Conditional Maximum ◽  
Adaptation Method

Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.

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 Synthetic Source Universal Domain Adaptation through Contrastive Learning

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7539
Author(s):  
Jungchan Cho
Keyword(s):  
Domain Adaptation ◽  
Synthetic Data ◽  
Real Data ◽  
Target Domain ◽  
Source Domain ◽  
Universal Domain ◽  
Model Training ◽  
Using Data ◽  
Imaging Sensors ◽  
Target Data

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more challenging for UDA to train models. I hypothesize that the degradation of trained models in the target domain is caused by the lack of direct training loss to improve the discriminative power of the target domain data. As a result, the target data adapted to the source representations is biased toward the source domain. I found that the degradation was more pronounced when I used synthetic data for the source domain and real data for the target domain. In this paper, I propose a UDA method with target domain contrastive learning. The proposed method enables models to leverage synthetic data for the source domain and train the discriminativeness of target features in an unsupervised manner. In addition, the target domain feature extraction network is shared with the source domain classification task, preventing unnecessary computational growth. Extensive experimental results on VisDa-2017 and MNIST to SVHN demonstrated that the proposed method significantly outperforms the baseline by 2.7% and 5.1%, respectively.

scholarly journals Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1361 ◽  
Author(s):  
Jianwen Guo ◽  
Jiapeng Wu ◽  
Shaohui Zhang ◽  
Jianyu Long ◽  
Weidong Chen ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Transfer Learning ◽  
Domain Adaptation ◽  
Industrial Applications ◽  
Classification Model ◽  
Intelligent Fault Diagnosis ◽  
Target Domain ◽  
Wind Turbine Gearbox ◽  
Time Frequency

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time–frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

scholarly journals Pre-Processing Method to Improve Cross-Domain Fault Diagnosis for Bearing

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4970
Author(s):  
Taeyun Kim ◽  
Jangbom Chai
Keyword(s):  
Fault Diagnosis ◽  
Ball Bearing ◽  
Domain Adaptation ◽  
Processing Method ◽  
Operating Conditions ◽  
Target Domain ◽  
Shift Problem ◽  
Cross Domain ◽  
The Difference ◽  
Intelligent Models

Models trained with one system fail to identify other systems accurately because of domain shifts. To perform domain adaptation, numerous studies have been conducted in many fields and have successfully aligned different domains into one domain. The domain shift problem is caused by the difference of distributions between two domains, which is solved by reducing this difference. Source domain data are labeled and used for training the models to extract the features while the target domain data are unlabeled or partially labeled and only used for aligning. Bearings play important roles in rotating machines, so many artificial intelligent models have been developed to diagnose bearings. Bearing diagnosis has also faced a domain shift problem due to various operating conditions such as experimental environment, number of balls, degree of defects, and rotational speed. Cross-domain fault diagnosis has been successfully performed when the systems are the same but operating conditions are different. However, the results are poor when diagnosing different bearing systems because the characteristics of the signals such as specific frequencies depend on the specifications. In this paper, the pre-processing method was used for improving the diagnosis without prior knowledge such as fault frequencies. The signals were first transformed to a common pattern space before entering the models. To develop and to validate the proposed method for different domains, vibration signals measured from two ball-bearing systems (Case Western Reserve University datasets and Paderborn University datasets) were used. One dimensional CNN models were utilized for verification of the proposed method and the results of the models using raw datasets and pre-processed datasets were compared. Even though each of the ball-bearing systems have their own specifications, using the proposed method was very helpful for domain adaptation, and cross-domain fault diagnosis was performed with high accuracy.

Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

2020 ◽  
pp. 147592172098071
Author(s):  
Kun Yu ◽  
Qiang Fu ◽  
Hui Ma ◽  
Tian Ran Lin ◽  
Xiang Li
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Data Driven ◽  
Mechanical Equipment ◽  
Target Domain ◽  
Simulation Data ◽  
Source Domain ◽  
Label Information ◽  
Actual Field ◽  
Adaptation Method

In current research works, a number of intelligent fault diagnosis methods have been proposed with the assistance of domain adaptation approach, which attempt to distinguish the health modes for target domain data using the diagnostic knowledge learned from source domain data. An important assumption for these methods is that the label information for the source domain data should be known in advance. However, the high-quality condition monitoring data with sufficient label information is difficult to be acquired in the actual field, which can greatly hinder the effectiveness of domain adaptation–based fault diagnosis methods. The simulation model of the rotating machine is an effective approach to provide an insight into the characteristics of the mechanical equipment, which can also easily carry the sufficient label information for the mechanical equipment under various operating conditions. In this study, a simulation data–driven domain adaptation approach is proposed for the intelligent fault diagnosis of mechanical equipment. The simulation data from a rotor-bearing system are used to build the source domain data set, and the diagnostic knowledge learned from the simulation data is used to realize the healthy mode identification of mechanical equipment in the actual field. The proposed domain adaption approach consists of two parts. The first part is to achieve the conditional distribution alignment between source domain data and target domain supervised data in an alternative way. The second part is to achieve the marginal distribution alignment between source domain data and target domain unsupervised data in an adversarial training process. The proposed domain adaptation method is evaluated on two case studies, the diagnostic results on two case studies indicate that the proposed domain adaptation method is capable of realizing the fault diagnosis of mechanical equipment using the diagnostic knowledge learned from simulation data.

scholarly journals Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jing An ◽  
Ping Ai ◽  
Dakun Liu
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Feature Learning ◽  
Target Domain ◽  
Source Domain ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Discriminative Feature ◽  
Joint Training ◽  
Domain Alignment

Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

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