Adversarial Domain Adaptation of Asymmetric Mapping with Coral Alignment for Intelligent Fault Diagnosis

Abstract Rolling bearings play a vital role in the overall operation of rotating machineries. In practical diagnosis, many learning methods for variable speed fault diagnosis ignore task-specific decision boundaries, which make it very difficult to match feature distributions between different domains completely. Therefore, an adversarial domain adaptation of asymmetric mapping with coral alignment (ADA-AMCA) is presented to dispose this problem. By using the asymmetric mapping feature extractor, more features of specific domain with obvious distinction can be extracted. Meanwhile, combining the maximum classifier discrepancy of deep transfer to form an adversarial approach, and the task-specific decision boundary is taken into account, the class-level alignment between the features of source domain and target domain is attempted. For the sake of preventing degenerate learning which is possibly caused by asymmetric mapping and adversarial learning, the model is constrained by deep coral to extract more domain invariant features. Experimental results show that the proposed method can solve the variable speed fault diagnosis problem well, with high transfer accuracy and strong generalization.

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Triplet Loss Guided Adversarial Domain Adaptation for Bearing Fault Diagnosis

Sensors ◽

10.3390/s20010320 ◽

2020 ◽

Vol 20 (1) ◽

pp. 320 ◽

Cited By ~ 9

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.

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

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211032995 ◽

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.

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

Entropy ◽

10.3390/e23040424 ◽

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.

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Envelope demodulation method based on SET for fault diagnosis of rolling bearings under variable speed

Journal of Advanced Mechanical Design Systems and Manufacturing ◽

10.1299/jamdsm.2020jamdsm0104 ◽

2020 ◽

Vol 14 (7) ◽

pp. JAMDSM0104-JAMDSM0104

Author(s):

Zengqiang MA ◽

Xin LI ◽

Suyan LIU ◽

Yongjie GE ◽

Feiyu LU

Keyword(s):

Fault Diagnosis ◽

Variable Speed ◽

Rolling Bearings ◽

Demodulation Method

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ITERATIVE RE-WEIGHTED INSTANCE TRANSFER FOR DOMAIN ADAPTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-339-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 339-346 ◽

Cited By ~ 1

Author(s):

A. Paul ◽

F. Rottensteiner ◽

C. Heipke

Keyword(s):

Domain Adaptation ◽

Training Data ◽

Aerial Images ◽

Target Domain ◽

Data Set ◽

Source Domain ◽

The Difference ◽

A New Technique ◽

Class Labels ◽

Decision Boundaries

Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source domain to a new data set (target domain) where the features may have different distributions. In this paper, we propose a new technique for domain adaptation based on logistic regression. Starting with a classifier trained on training data from the source domain, we iteratively include target domain samples for which class labels have been obtained from the current state of the classifier, while at the same time removing source domain samples. In each iteration the classifier is re-trained, so that the decision boundaries are slowly transferred to the distribution of the target features. To make the transfer procedure more robust we introduce weights as a function of distance from the decision boundary and a new way of regularisation. Our methodology is evaluated using a benchmark data set consisting of aerial images and digital surface models. The experimental results show that in the majority of cases our domain adaptation approach can lead to an improvement of the classification accuracy without additional training data, but also indicate remaining problems if the difference in the feature distributions becomes too large.

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

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x19867776 ◽

2019 ◽

Vol 234 (1) ◽

pp. 168-182 ◽

Cited By ~ 4

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.

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Rolling Bearing Fault Diagnosis by Aperiodic Stochastic Resonance Under Variable Speed Conditions

Fluctuation and Noise Letters ◽

10.1142/s0219477522500158 ◽

2021 ◽

Author(s):

Xuzhu Zhuang ◽

Chen Yang ◽

Jianhua Yang ◽

Chengjin Wu ◽

Zhen Shan ◽

...

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Fault Simulation ◽

Rolling Bearing ◽

Damping Coefficient ◽

Variable Speed ◽

Rolling Bearings ◽

Bearing Fault ◽

Speed Condition ◽

Fault Characteristic

The fault characteristic of rolling bearings under variable speed condition is a typical non-stationary stochastic signal. It is difficult to extract due to the interference of strong background noise makes the applicability of traditional noise reduction methods less. In this paper, an aperiodic stochastic resonance (ASR) method is proposed to study the fault diagnosis of rolling bearings under variable speed conditions. Based on numerical simulation, the effect of noise intensity and damping coefficient on the ASR of the second-order underdamped system is discussed, and an appropriate damping coefficient is found to reach the optimal ASR. The proposed method enhances the fault characteristic information of bearing fault simulation signal. Corresponding to rising-stationary and the stationary-declining running conditions, the method is verified by both simulated and experimental signals. It provides reference for fault diagnosis under variable speed condition.

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