scholarly journals Spacecraft Intelligent Fault Diagnosis under Variable Working Conditions via Wasserstein Distance-Based Deep Adversarial Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Gang Xiang ◽  
Kun Tian
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Manufacturing Industry ◽  
Probability Distributions ◽  
Wasserstein Distance ◽  
Target Domain ◽  
Target Feature ◽  
Generative Adversarial Network ◽  
Adversarial Network

In recent years, deep learning methods which promote the accuracy and efficiency of fault diagnosis task without any extra requirement of artificial feature extraction have elicited the attention of researchers in the field of manufacturing industry as well as aerospace. However, the problems that data in source and target domains usually have different probability distributions because of different working conditions and there are insufficient labeled or even unlabeled data in target domain significantly deteriorate the performance and generalization of deep fault diagnosis models. To address these problems, we propose a novel Wasserstein Generative Adversarial Network with Gradient Penalty- (WGAN-GP-) based deep adversarial transfer learning (WDATL) model in this study, which exploits a domain critic to learn domain invariant feature representations by minimizing the Wasserstein distance between the source and target feature distributions through adversarial training. Moreover, an improved one-dimensional convolutional neural network- (CNN-) based feature extractor which utilizes exponential linear units (ELU) as activation functions and wide kernels is designed to automatically extract the latent features of raw time-series input data. Then, the fault model classifier trained in one working condition (source domain) with sufficient labeled samples could be generalized to diagnose data in other working conditions (target domain) with insufficient labeled samples. Experiments on two open datasets demonstrate that our proposed WDATL model outperforms most of the state-of-the-art approaches on transfer diagnosis tasks under diverse working circumstances.

scholarly journals A Fault Diagnosis Method for Rolling Bearings Based on Parameter Transfer Learning under Imbalance Data Sets

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 944
Author(s):  
Cheng Peng ◽  
Lingling Li ◽  
Qing Chen ◽  
Zhaohui Tang ◽  
Weihua Gui ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Hot Spot ◽  
Data Sets ◽  
Target Domain ◽  
Rolling Bearings ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Recognition Ability ◽  
Diagnosis Method

Fault diagnosis under the condition of data sets or samples with only a few fault labels has become a hot spot in the field of machinery fault diagnosis. To solve this problem, a fault diagnosis method based on deep transfer learning is proposed. Firstly, the discriminator of the generative adversarial network (GAN) is improved by enhancing its sparsity, and then adopts the adversarial mechanism to continuously optimize the recognition ability of the discriminator; finally, the parameter transfer learning (PTL) method is applied to transfer the trained discriminator to target domain to solve the fault diagnosis problem with only a small number of label samples. Experimental results show that this method has good fault diagnosis performance.

scholarly journals Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

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 Materials Representation and Transfer Learning for Multi-Property Prediction

2021 ◽  
Author(s):  
Shufeng Kong ◽  
Dan Guevarra ◽  
Carla P. Gomes ◽  
John Gregoire
Keyword(s):  
Machine Learning ◽  
Optical Absorption ◽  
Transfer Learning ◽  
Materials Science ◽  
Training Data ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Property Prediction ◽  
Adversarial Network ◽  
Correlation Learning

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material’s composition, (ii) learning and exploitation of correlations among target properties in multitarget regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

scholarly journals A Novel Deep Learning System with Data Augmentation for Machine Fault Diagnosis from Vibration Signals

2020 ◽  
Vol 10 (17) ◽  
pp. 5765
Author(s):  
Qiang Fu ◽  
Huawei Wang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Data Augmentation ◽  
Measured Data ◽  
Learning System ◽  
Generative Adversarial Network ◽  
Vibration Signals ◽  
Adversarial Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

In real engineering scenarios, it is difficult to collect adequate cases with faulty conditions to train an intelligent diagnosis system. To alleviate the problem of limited fault data, this paper proposes a fault diagnosis method combining a generative adversarial network (GAN) and stacked denoising auto-encoder (SDAE). The GAN approach augments the limited real measured data, especially in faulty conditions. The generated data are then transformed into the SDAE fault diagnosis model. The GAN-SDAE approach improves the accuracy of the fault diagnosis from the vibration signals, especially when the measured samples are few. The usefulness of this method is assessed through two condition-monitoring cases: one is a classic bearing example and the other is a more general gear failure. The results demonstrate that diagnosis accuracy for both cases is above 90% for various working conditions, and the GAN-SDAE system is stable.

scholarly journals One-Dimensional Multi-Scale Domain Adaptive Network for Bearing-Fault Diagnosis under Varying Working Conditions

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6039
Author(s):  
Kai Wang ◽  
Wei Zhao ◽  
Aidong Xu ◽  
Peng Zeng ◽  
Shunkun Yang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Target Domain ◽  
One Dimensional ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Classifier Adaptation ◽  
Feature Adaptation

Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-quality labeled data. However, machines usually work under different working conditions in practice, which challenges these prerequisites due to the fact that the data distributions under different working conditions are different. In this paper, the one-dimensional Multi-Scale Domain Adaptive Network (1D-MSDAN) is proposed to address this issue. The 1D-MSDAN is a kind of deep transfer model, which uses both feature adaptation and classifier adaptation to guide the multi-scale convolutional neural network to perform bearing-fault diagnosis under varying working conditions. Feature adaptation is performed by both multi-scale feature adaptation and multi-level feature adaptation, which helps in finding domain-invariant features by minimizing the distribution discrepancy between different working conditions by using the Multi-kernel Maximum Mean Discrepancy (MK-MMD). Furthermore, classifier adaptation is performed by entropy minimization in the target domain to bridge the source classifier and target classifier to further eliminate domain discrepancy. The Case Western Reserve University (CWRU) bearing database is used to validate the proposed 1D-MSDAN. The experimental results show that the diagnostic accuracy for the 12 transfer tasks performed by 1D-MSDAN was superior to that of the mainstream transfer learning models for bearing-fault diagnosis under variable working conditions. In addition, the transfer learning performance of 1D-MSDAN for multi-target domain adaptation and real industrial scenarios was also verified.

Generative Adversarial Network and Transfer Learning Based Fault Detection for Rotating Machinery with Imbalance Data Condition

2021 ◽  
Author(s):  
jun li ◽  
Yongbao Liu ◽  
Qijie Li
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Learning Method ◽  
Intelligent Fault Diagnosis ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Proposed Model ◽  
Diagnosis Method

Abstract Intelligent fault diagnosis achieves tremendous success in machine fault diagnosis because of the outstanding data-driven capability. However, the severe imbalanced dataset in practical scenarios of industrial rotating machinery is still a big challenge for the development of intelligent fault diagnosis method. In this paper, we solve this issue by constructing a novel deep learning model incorporated with a transfer learning method based on the Time-GAN and Efficient-Net models. Firstly, the proposed model so called Time-GAN-TL extends the imbalanced fault diagnosis of rolling bearings by using time series generative adversarial network. Secondly, balanced vibration signals are converted into two-dimensional images for training and classification by implementing the Efficient-Net into the transfer learning method. Finally, the proposed method is validated using two-types of rolling bearing experimental data. The high-precision diagnosis results of the transfer learning experiments and the comparison with other representative fault diagnosis classification methods reveal the efficiency, reliability, and generalization performance of the presented model.

Alignment subdomain based deep convolutional transfer learning for machinery fault diagnosis under different working conditions

2021 ◽  
Author(s):  
Yibing Li ◽  
Hu Wan ◽  
Li Jiang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Local Maximum ◽  
Mean Difference ◽  
Global Distribution ◽  
Target Domain ◽  
Learning Methods ◽  
Time Frequency ◽  
Machinery Fault Diagnosis

Abstract In recent years, transfer learning methods have been extensively used in machinery fault diagnosis under different working conditions. However, most of these transfer learning methods perform poorly in the actual industrial applications, due to the fact that they mainly focus on the global distribution of different domains without considering the distribution of subdomains belonging to the same category in different domains. Therefore, we propose an alignment subdomain-based deep convolutional transfer learning (AS-DCTL) network for machinery fault diagnosis. First, continuous wavelet transform is used to transform the original vibration signal into a two-dimensional time-frequency image. Then, AS-DCTL uses convolutional neural network as the feature extractor to extract the features of the source and target domain samples and introduces maximum mean difference to align the global distribution of the extracted features. Simultaneously, we use local maximum mean difference as a metric criterion to align the distribution of related subdomains, by adding weights to similar samples in the source domain and target domain. The experimental results of the two case studies show that the proposed AS-DCTL network can achieve higher recognition accuracy and classification effect, in comparison with the current mainstream transfer learning methods.

Transfer Learning Method for Rolling Bearing Fault Diagnosis under Different Working Conditions Based on CycleGAN

2021 ◽  
Author(s):  
Jiantong Zhao ◽  
Wentao Huang
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Working Condition ◽  
Learning Method ◽  
Target Domain ◽  
Simulation Data ◽  
Source Domain ◽  
Bearing Fault Diagnosis ◽  
Cross Domain

Abstract In practical bearing fault diagnosis tasks, the available labelled data are often not from the equipment to be diagnosed and cannot cover all manner of working conditions. The adopted data-driven method is required to have a certain degree of cross-domain and cross-working condition transfer learning diagnosis ability. However, limited by the performance of existing transfer learning methods, the potential difference between the source domain and the target domain poses a challenge for the accuracy of transfer diagnosis. In this paper, a cross-working condition data supplement method based on the cycle generative adversarial network (CycleGAN) and a dynamics model is proposed, which can use limited available data to approximate the missing parts of existing data and be used for diagnosis of the target domain. First, we considered the limited experimental data as the target domain, the simulation data corresponding to the working condition as the source domain and used the working condition as the benchmark to constrain the data correspondence between the two datasets. We then used the CycleGAN model to learn the feature mapping from simulation to experiment. Second, based on the working condition of the data to be tested, the corresponding simulation data were input into the trained generator to obtain labeled data with experimental characteristics under the corresponding working conditions, and transferred the dataset as the source domain data to the data to be tested. In the test using self-made simulation and experimental datasets, combined with the transfer learning method based on the probability distribution adaptation, it was shown that the proposed method could effectively improve the diagnostic impact of the single transfer learning method in cross-domain and cross-working conditions when the working condition span was large.

scholarly journals Deep Adaptive Adversarial Network-Based Method for Mechanical Fault Diagnosis under Different Working Conditions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinrui Wang ◽  
Shanshan Ji ◽  
Baokun Han ◽  
Huaiqian Bao ◽  
Xingxing Jiang
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Wasserstein Distance ◽  
Health Condition ◽  
Generative Adversarial Networks ◽  
Adversarial Learning ◽  
Learning Module ◽  
Adversarial Network ◽  
Invariant Features ◽  
Condition Recognition

The demand for transfer learning methods for mechanical fault diagnosis has considerably progressed in recent years. However, the existing methods always depend on the maximum mean discrepancy (MMD) in measuring the domain discrepancy. But MMD can not guarantee the different domain features to be similar enough. Inspired by generative adversarial networks (GAN) and domain adversarial training of neural networks (DANN), this study presents a novel deep adaptive adversarial network (DAAN). The DAAN comprises a condition recognition module and domain adversarial learning module. The condition recognition module is constructed with a generator to extract features and classify the health condition of machinery automatically. The domain adversarial learning module is achieved with a discriminator based on Wasserstein distance to learn domain-invariant features. Then spectral normalization (SN) is employed to accelerate convergence. The effectiveness of DAAN is demonstrated through three transfer fault diagnosis experiments, and the results show that the DAAN can converge to zero after approximately 15 training epochs, and all the average testing accuracies in each case can achieve over 92%. It is expected that the proposed DAAN can effectively learn domain-invariant features to bridge the discrepancy between the data from different working conditions.

Sign in / Sign up

Export Citation Format

Share Document