The Fault Diagnosis of Tower Crane Based on Genetic Algorithm and BP Neural Network

As the most important architectural engineering mechanics in the processing of architectural construction, the progress of construction will be put off by the appearance of the fault of Tower Crane, so it is absolutely crucial to take the monitoring and diagnosis of the condition. BP Neural Network ,which is optimized by Genetic Algorithm, is constructed to have the prediction and identification of the fault of Tower Crane, and it proved that it is effectively and precisely to justify the fault of Tower Crane through using the structure of improving BP Neural Network.

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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.

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A Fault Diagnosis Method of Switch Current Based on Genetic Algorithm to Optimize the BP Neural Network

Lecture Notes in Electrical Engineering - Electrical Power Systems and Computers ◽

10.1007/978-3-642-21747-0_122 ◽

2011 ◽

pp. 943-950 ◽

Cited By ~ 2

Author(s):

Chengjun Tang ◽

Yigang He ◽

Lifen Yuan

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Diagnosis Method

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Fault diagnosis of PV array based on optimised BP neural network by improved adaptive genetic algorithm

The Journal of Engineering ◽

10.1049/joe.2017.0567 ◽

2017 ◽

Vol 2017 (13) ◽

pp. 1427-1431 ◽

Cited By ~ 6

Author(s):

Yawei Liu ◽

Xiaorong Zhu ◽

Jin Yang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Bp Neural Network ◽

Adaptive Genetic Algorithm ◽

Pv Array

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Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962319500247 ◽

2019 ◽

Vol 10 (04) ◽

pp. 1950024 ◽

Cited By ~ 5

Author(s):

Dawei Zhang ◽

Weilin Li ◽

Xiaohua Wu ◽

Xiaofeng Lv

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Simulated Annealing ◽

Fault Diagnosis ◽

Flight Control ◽

Bp Neural Network ◽

Learning Algorithm ◽

Back Propagation ◽

Good Convergence ◽

Simulated Annealing Genetic Algorithm

Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight training algorithm in neural network application, the back propagation (BP) neural network learning algorithm combined with simulated annealing genetic algorithm (SAGA) is put forward. The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations. The simulated annealing mechanism is incorporated into the Genetic Algorithm (GA) to optimize the design and optimization of neural network conformation and network weights simultaneously. The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process, also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon. The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm. The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.

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The Missile Fault Diagnosis Expert System Based on GA-BPNN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.255-260.2164 ◽

2011 ◽

Vol 255-260 ◽

pp. 2164-2168

Author(s):

Jian Xiao Zou ◽

Yao Zhang ◽

Gang Zheng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Expert System ◽

Bp Neural Network ◽

Optimal Solution ◽

Convergence Speed ◽

Diagnosis Accuracy ◽

Simulation Results ◽

Locally Optimal Solution

To improve the performance of fault diagnosis expert system based on ANN IN fields of convergence speed, locally optimal solution and the low accuracy, an missile fault diagnosis expert system based on GA-BPNN is proposed in this paper. The genetic algorithm (GA) is adopt to optimize the weight and threshold of matrix while BP neural network realizes the non-linear map relations between failure feature and failure cause. The simulation results indicate that the method proposed in this paper significantly increase the convergence speed and globally optimal solution of neural network, the fault diagnosis accuracy of expert system for a missile has been improved also.

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