The Missile Fault Diagnosis Expert System Based on GA-BPNN

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.

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.

scholarly journals Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

2022 ◽  
Author(s):  
Yangbing Zheng ◽  
Xiao Xue ◽  
Jisong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Fuzzy Relation ◽  
Analysis Procedure ◽  
Fuzzy Neural ◽  
Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

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

2011 ◽  
Vol 368-373 ◽  
pp. 3163-3166 ◽  
Author(s):  
Si Cong Yuan ◽  
Jing Qiang Shang ◽  
Xiao Yu Wang ◽  
Chao Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Architectural Engineering ◽  
Tower Crane ◽  
Monitoring And Diagnosis ◽  
Engineering Mechanics

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.

A Time Series Analysis and Neural Network Based Scheme for Fault Diagnosis of Transformers

2015 ◽  
Vol 742 ◽  
pp. 412-418
Author(s):  
Jian Jun Zhang ◽  
Ye Xin Song ◽  
Yong Qu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Characteristic Parameters ◽  
Series Analysis ◽  
Diagnosis Accuracy ◽  
Artificial Neural Network Ann

This research presents a time series analysis and artificial neural network (ANN)-based scheme for fault diagnosis of power transformers, which extracts the characteristic parameters of the faults of the transformer from the results of time series analysis and bases on this basis establishes the corresponding back propagation (BP) neural network to detect the transformer operating faults. The simulation experimental results show that as compared to the related works, the proposed approach effectively integrates the superiority of time series analysis and BP neural network and thus can greatly improve the diagnosis accuracy and reliability.

Fault Diagnosis of Grounding Grid Based on Genetic Algorithm and Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 4095-4099
Author(s):  
Wei Qian ◽  
Mei Hong Qiu ◽  
Miao Wu ◽  
Xian Yue Shi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Power Plants ◽  
Optimal Solution ◽  
Electric Power System ◽  
Bp Algorithm ◽  
Stable Operation ◽  
Oscillation Effect ◽  
Grounding Grid

The grounding grid of power plants and substations is an important device to ensure the safe and stable operation of electric power system. However, it is difficult to diagnose the fault of grounding grid using traditional method of identification. In recent years, the development of artificial neural network has provided effective ways to solve this problem. In this paper, neural network is used to diagnose fault of the grounding grid, because it has good learning and training characteristics, and performance of fault tolerance. It can search fault localization of the grounding grid. BP Algorithm has the advantage of the optimization accuracy, but there are some drawbacks, the majority of which is easy to fall into local minimum, slow convergence and cause oscillation effect. Genetic algorithm has a strong globe search capability, and can find the global optimal solution with high probability, so it can overcome the shortcomings of the BP algorithm using GA to complete the pre-search. This paper presents a hybrid training algorithm by GA combine with BP to optimize network. Simulation results show that the hybrid method has a fast convergence rate and high diagnostic accuracy for diagnosing the fault of grounding grid; it can be used in fault diagnosis of grounding grid effectively and reliably.

Sign in / Sign up

Export Citation Format

Share Document