Transformer Fault Diagnosis of Rough-Neural Network Based on MEA

2012 ◽  
Vol 217-219 ◽  
pp. 2585-2589
Author(s):  
Jin Lan Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Bp Neural Network ◽  
Attribute Reduction ◽  
Global Optimum ◽  
Optimum Solution ◽  
Transformer Fault ◽  
Speed And Accuracy ◽  
Global Optimum Solution

Combining mind evolutionary algorithm with rough set and neural network, this paper proposed a rough set neural network based on MEA for transformer fault diagnosis. Rough set attribute reduction as the front-processor of neural network diagnostic device, and using MEA to search rough set discrete breakpoints and optimize neural network weights and thresholds, it avoided complex manual trial of the conventional rough set attribute reduction and slow convergence speed and low precision of BP neural network, then faster convergence to the global optimum solution and improves the diagnosis speed and accuracy. Simulation results show that this method is effective.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

Transformer Fault Diagnosis Based on Improved Quantum Genetic Algorithm and BP Network

2010 ◽  
Vol 29-32 ◽  
pp. 1543-1549 ◽  
Author(s):  
Jie Wei ◽  
Hong Yu ◽  
Jin Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fuzzy Theory ◽  
Back Propagation ◽  
Gas Analysis ◽  
Bp Network ◽  
Quantum Genetic Algorithm ◽  
Transformer Fault

Three-ratio of the IEC is a convenient and effective approach for transformer fault diagnosis in the dissolved gas analysis (DGA). Fuzzy theory is used to preprocess the three-ratio for its boundary that is too absolute. As the same time, an improved quantum genetic algorithm IQGA (QGASAC) is used to optimize the weight and threshold of the back propagation (BP). The local and global searching ability of the QGASAC approach is utilized to find the BP optimization solution. It can overcome the slower convergence velocity and hardly getting the optimization of the BP neural network. So, aiming at the shortcoming of BP neural network and three-ratio, blurring the boundary of the gas ratio and the QGASAC algorithm is introduced to optimize the BP network. Then the QGASAC-IECBP method is proposed in this paper. Experimental results indicate that the proposed algorithm in this paper that both convergence velocity and veracity are all improved to some extent. And in this paper, the proposed algorithm is robust and practical.

Improved BP Neural Network for Transformer Fault Diagnosis

2007 ◽  
Vol 17 (1) ◽  
pp. 138-142 ◽  
Author(s):  
Yan-jing SUN ◽  
Shen ZHANG ◽  
Chang-xin MIAO ◽  
Jing-meng LI
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Transformer Fault

scholarly journals Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhenwei Zhao ◽  
Weining Jiang ◽  
Weidong Gao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Medical Imaging ◽  
Network Model ◽  
Rough Set ◽  
Bp Neural Network ◽  
Medical Equipment ◽  
Large Scale ◽  
Hybrid Network ◽  
Mechanical Equipment

In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.

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