Fault Diagnosis of Power Transformers Based on Membrane Computing Optimizing Neural Network

2012 ◽  
Vol 214 ◽  
pp. 740-744
Author(s):  
Zhi Jian Yuan ◽  
Hao Deng ◽  
Wen Jun Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Membrane Computing ◽  
Power Transformer ◽  
Back Propagation ◽  
Optimization Procedure ◽  
Back Propagation Neural Network ◽  
Gas Content ◽  
Back Propagation Algorithm ◽  
Strict Requirement

Presently, dissolved gas content analysis and fault diagnosis are the important segments of power transformer. As to the problem of the back propagation algorithm of neural network commonly used lies in the optimization procedure getting easily stacked into the minimal value locally and strict requirement on the initial value, a fault diagnostic method is presented, based on the membrane computing optimizing back propagation neural network. Throughout the process, compromise is satisfactorily reached among the network complexity, the convergence and the generalization ability. The results of diagnosis test show that the algorithm proposed has high classification accuracy, which proves its robustness and effectiveness.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

Power Quality Disturbance Classification Using Wavelet Packet Transform and Back Propagation Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 7099-7109
Author(s):  
M. K Elango ◽  
A Jagadeesan ◽  
K. Mohana Sundaram
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Virtual Instrument ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Wavelet Packet Transform ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Power Quality Disturbances

This paper develops a real time solution for detecting the Power Quality events. Fourteen events are generated through experimental setup and the signals are acquired through a voltage Data Acquisition Card, NI DAQ-9225, controlled by a Virtual Instrument software package. The features extracted from the Wavelet Transformation are fed into the Back Propagation Neural Network for training. By the virtue of a Neural Network property, it gets self-adapted and self-learned aiding in automatic classification of Power Quality Events. A combination of Wavelet Transform technique and Neural Networks are employed to detect and characterize the Power Quality Disturbances. The result obtained shows the effectiveness of the Wavelet Packet Transform based Back Propagation algorithm in classifying the Power Quality Disturbances. The results produced by the proposed methodology based Back Propagation Algorithm is verified with the Power Quality Analyser.

Development of No-Load Noise Power Transformer Model using Back Propagation Neural Network

2021 ◽  
Author(s):  
Wahyudi Budi Pramono ◽  
F. Danang Wijaya ◽  
Sasongko Pramono Hadi ◽  
Agus Indarto ◽  
Mohammad Slamet Wahyudi
Keyword(s):  
Neural Network ◽  
Power Transformer ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Noise Power ◽  
Transformer Model ◽  
Load Noise

scholarly journals A Data Classification Algorithm of Internet of Things Based on Neural Network

2017 ◽  
Vol 13 (09) ◽  
pp. 28 ◽  
Author(s):  
Zhenjun Li
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Back Propagation ◽  
Data Classification ◽  
Back Propagation Neural Network ◽  
Multidimensional Data ◽  
Back Propagation Algorithm ◽  
Huge Data ◽  
Neural Network Algorithm ◽  
Working Principle

<p style="margin: 1em 0px;"><span lang="EN-US"><span style="font-family: 宋体; font-size: medium;">To alleviate the pressure of data size, data transmission and data processing in the huge data dimension of the Internet of things., data classification is realized based on back propagation (BP) neural network algorithm. The working principle is deduced in detail. For the shortcomings of slow convergence and easy to fall into the local minimum, the combination of variable learning and momentum factors is used to improve the traditional back propagation algorithm. The results show that the optimized algorithm improves the convergence speed of the network to a certain extent. Therefore, it is concluded that the back propagation neural network has higher classification success rate when classifying multidimensional data in Internet of things.</span></span></p>

Application of Back Propagation Neural Network Algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

2012 ◽  
Vol 225 ◽  
pp. 505-510 ◽  
Author(s):  
Wael G. Abdelrahman ◽  
Ahmed Z. Al-Garni ◽  
Waheed Al-Wadiee
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Aircraft Engine ◽  
Weibull Model ◽  
Back Propagation Neural Network ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Failure Data ◽  
Feed Forward Back Propagation ◽  
Air System

Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators’ profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

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