On‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning

2021 ◽  
Author(s):  
Zhichun Yang ◽  
Fan Yang ◽  
Yu Shen ◽  
Lei Yang ◽  
Lei Su ◽  
...  
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Data Stream ◽  
Distribution Transformer ◽  
On Line ◽  
Diagnosis Model

scholarly journals A transfer learning fault diagnosis model of distribution transformer considering multi‐factor situation evolution

2019 ◽  
Vol 15 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Zhichun Yang ◽  
Yu Shen ◽  
Renfei Zhou ◽  
Fan Yang ◽  
Zilin Wan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Distribution Transformer ◽  
Diagnosis Model

On-line fault diagnosis model for locomotive traction inverter based on wavelet transform and support vector machine

2018 ◽  
Vol 88-90 ◽  
pp. 1274-1280 ◽  
Author(s):  
Mei Fei ◽  
Liu Ning ◽  
Miao Huiyu ◽  
Pan Yi ◽  
Sha Haoyuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Support Vector ◽  
On Line ◽  
Diagnosis Model

A Fault Diagnosis Model for Power Transformer Using Association Rule Mining-Based on Rough Set

2014 ◽  
Vol 519-520 ◽  
pp. 1169-1172
Author(s):  
De Wen Wang ◽  
Lin Xiao He
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Association Rule ◽  
Association Rule Mining ◽  
Power Transformer ◽  
Monitoring Data ◽  
Rule Mining ◽  
Monitoring Technology ◽  
On Line ◽  
Diagnosis Model

With the development of on-line monitoring technology of electric power equipment, and the accumulation of both on-line monitoring data and off-line testing data, the data available to fault diagnosis of power transformer is bound to be massive. How to utilize those massive data reasonably is the issue that eagerly needs us to study. Since the on-line monitoring technology is not totally mature, which resulting in incomplete, noisy, wrong characters for monitoring data, so processing the initial data by using rough set is necessary. Furthermore, when the issue scale becomes larger, the computing amount of association rule mining grows dramatically, and its easy to cause data expansion. So it needs to use attribute reduction algorithm of rough set theory. Taking the above two points into account, this paper proposes a fault diagnosis model for power transformer using association rule mining-based on rough set.

A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

2021 ◽  
pp. 107754632110429
Author(s):  
Chongyu Wang ◽  
Guangya Zhu ◽  
Tianyuan Liu ◽  
Yonghui Xie ◽  
Di Zhang
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Research Work ◽  
Research Field ◽  
Residual Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
The Impact

Bearing fault diagnosis is an important research field for rotating machinery health monitoring. Recently, many intelligent fault diagnosis methods driven by big data, such as transfer learning, have been studied. However, there are two shortcomings for the prior transfer learning method in industry application. First, it is necessary to design a complex loss function to enhance the similarity between the two domains further. Second, previous studies required big data both in source and target task, without considering the lack of sufficient training samples. Inspired by relevant research work, this article proposes a local joint distribution discrepancy to increase similar features. A sub-domain adaptive transfer learning is designed to detect bearing faults based on the residual network. Two kinds of transfer experiments are designed to verify the method effectiveness. After that, the impact of small training samples and noise on the results is explored. The proposed method reaches high accuracy.

A new bearing fault diagnosis method using elastic net transfer learning and LSTM

2021 ◽  
pp. 1-9
Author(s):  
Xudong Song ◽  
Dajie Zhu ◽  
Pan Liang ◽  
Lu An
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Working Conditions ◽  
Short Term Memory ◽  
Elastic Net ◽  
Variable Load ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Model ◽  
Net Transfer

Although the existing transfer learning method based on deep learning can realize bearing fault diagnosis under variable load working conditions, it is difficult to obtain bearing fault data and the training data of fault diagnosis model is insufficient£¬which leads to the low accuracy and generalization ability of fault diagnosis model, A fault diagnosis method based on improved elastic net transfer learning under variable load working conditions is proposed. The improved elastic net transfer learning is used to suppress the over fitting and improve the training efficiency of the model, and the long short-term memory network is introduced to train the fault diagnosis model, then a small amount of target domain data is used to fine tune the model parameters. Finally, the fault diagnosis model under variable load working conditions based on improved elastic net transfer learning is constructed. Finally, through model experiments and comparison with conventional deep learning fault diagnosis models such as long short-term memory network (LSTM), gated recurrent unit (GRU) and Bi-LSTM, it shows that the proposed method has higher accuracy and better generalization ability, which verifies the effectiveness of the method.

scholarly journals A Fault Diagnosis and Visualization Method for High-Speed Train Based on Edge and Cloud Collaboration

2021 ◽  
Vol 11 (3) ◽  
pp. 1251
Author(s):  
Kunlin Zhang ◽  
Wei Huang ◽  
Xiaoyu Hou ◽  
Jihui Xu ◽  
Ruidan Su ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Transfer Learning ◽  
High Speed ◽  
Visual Analysis ◽  
Monitoring Data ◽  
Fault Classification ◽  
Training Time ◽  
Diagnosis Model ◽  
Sensor Monitoring

Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.

scholarly journals A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Yan Wang ◽  
Liguo Zhang
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Gas Analysis ◽  
Support Vector ◽  
Diagnosis Method ◽  
Scope Of Application ◽  
Diagnosis Model ◽  
Data Environment ◽  
Transformer Fault

The fault diagnosis method based on dissolved gas analysis (DGA) is of great significance to detect the potential faults of the transformer and improve the security of the power system. The DGA data of transformer in smart grid have the characteristics of large quantity, multiple types, and low value density. In view of DGA big data’s characteristics, the paper first proposes a new combined fault diagnosis method for transformer, in which a variety of fault diagnosis models are used to make a preliminary diagnosis, and then the support vector machine is used to make the second diagnosis. The method adopts the intelligent complementary and blending thought, which overcomes the shortcomings of single diagnosis model in transformer fault diagnosis, and improves the diagnostic accuracy and the scope of application of the model. Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment.

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