Research on Transformer Fault Diagnosis Method Based on Rough Set Optimization BP Neural Network

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.

Download Full-text

Improved PSO-BP neural network for power transformer fault diagnosis

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00783 ◽

2010 ◽

Vol 30 (3) ◽

pp. 783-785 ◽

Cited By ~ 2

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

Download Full-text

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1014.501 ◽

2014 ◽

Vol 1014 ◽

pp. 501-504 ◽

Cited By ~ 2

Author(s):

Shu Guo ◽

You Cai Xu ◽

Xin Shi Li ◽

Ran Tao ◽

Kun Li ◽

...

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Roller Bearing ◽

Vibration Signal ◽

Classification Performance ◽

Outer Race ◽

Mode Decomposition ◽

Diagnosis Method ◽

Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

Download Full-text

Fault diagnosis method for scintillation detector based on BP neural network

Journal of Instrumentation ◽

10.1088/1748-0221/16/07/t07006 ◽

2021 ◽

Vol 16 (07) ◽

pp. T07006

Author(s):

Y.X. Xie ◽

Y.J. Yan ◽

X. Li ◽

T.S. Ding ◽

C. Ma

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Scintillation Detector ◽

Diagnosis Method

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.29-32.1543 ◽

2010 ◽

Vol 29-32 ◽

pp. 1543-1549 ◽

Cited By ~ 1

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.

Download Full-text