Recurrent Neural Network Based On-line Fault Diagnosis Approach for Power Electronic Devices

2007 ◽  
Author(s):  
Xiang Xu ◽  
Ruqing Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
Electronic Devices ◽  
Power Electronic ◽  
On Line ◽  
Diagnosis Approach

A Fault Diagnosis Approach for Rolling Element Bearing Based on S-Transform and Artificial Neural Network

2017 ◽  
Author(s):  
Ningbo Zhao ◽  
Hongtao Zheng ◽  
Lei Yang ◽  
Zhitao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Health Status ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Rolling Element Bearing ◽  
S Transform ◽  
Rolling Element ◽  
Element Bearing ◽  
Diagnosis Approach

The condition monitoring and fault diagnosis of rolling element bearing is a very important research content in the field of gas turbine health management. In this paper, a hybrid fault diagnosis approach combining S-transform with artificial neural network (ANN) is developed to achieve the accurate feature extraction and effective fault diagnosis of rolling element bearing health status. Considering the nonlinear and non-stationary vibration characteristics of rolling element bearing under stable loading and rotational speeds, S-transform and singular value decomposition (SVD) theory are firstly used to process the vibration signal and extract its time-frequency information features. Then, radical basis function (RBF) neural network classification model is designed to carry out the state pattern recognition and fault diagnosis. As a practical application, the experimental data of rolling element bearing including four health status are analyzed to evaluate the performance of the proposed approach. The results demonstrate that the present hybrid fault diagnosis approach is very effective to extract the fault features and diagnose the fault pattern of rolling element bearing under different rotor speed, which may be a potential technology to enhance the condition monitoring of rotating equipment. Besides, the advantages of the developed approach are also confirmed by the comparisons with the other two approaches, i.e. the Wigner-Ville (WV) distribution and RBF neural network based method as well as the S-transform and Elman neural network based one.

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

scholarly journals A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2458 ◽  
Author(s):  
Zhuozheng Wang ◽  
Yingjie Dong ◽  
Wei Liu ◽  
Zhuo Ma
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Sequence Data ◽  
Experimental Results ◽  
Learning Technology ◽  
Global Features ◽  
Gated Recurrent Unit ◽  
Diagnosis Approach

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.

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