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 A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijian Wang ◽  
Likang Zheng ◽  
Wenhua Du ◽  
Wenan Cai ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Weight Coefficient ◽  
Convergence Condition ◽  
Intelligent Fault Diagnosis ◽  
Wind Turbine Gearbox ◽  
Time Frequency ◽  
Proposed Model ◽  
Novel Method ◽  
Relationship Of

In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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 An Unsupervised Intelligent Fault Diagnosis System Based on Feature Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nannan Lu ◽  
Songcheng Wang ◽  
Hanhan Xiao
Keyword(s):  
Fault Diagnosis ◽  
Intelligent Manufacturing ◽  
Intelligent Fault Diagnosis ◽  
Target Domain ◽  
Data Set ◽  
Diagnosis System ◽  
Time Frequency ◽  
Recognition Ability ◽  
Fault Diagnosis System ◽  
Original Feature

With the booming development of intelligent manufacturing in modern industry, intelligent fault diagnosis systems have become a necessity to equipment and machine, which have attracted many researchers’ attention. However, due to the requirements of enough labeled data for most of the current approaches, it is difficult to implement them in real industrial scenarios. In this paper, an unsupervised intelligent fault diagnosis system based on feature transfer is constructed to extract the historical labeled data of the source domain, using feature transfer to facilitate the fault diagnosis of the target domain. The original feature set is acquired by EEMD time-frequency analysis. Then, the transfer component analysis algorithm is adopted to minimize the distance between the marginal distributions of the source and target domains which reduces the discrepancy of features between the different domains. Finally, SVM is used in multiclassification to identify different categories of faults. The performance of the fault diagnosis system under different loads is tested on the CWRU bearing data set, and the experiments show that the proposed system could effectively improve the recognition ability of unsupervised fault diagnosis.

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.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

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