Applied Technology on Artificial Neural Network in Fault Diagnosis System

2013 ◽  
Vol 859 ◽  
pp. 448-452
Author(s):  
Qi Zhu ◽  
Jian Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Membership Function ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Bp Networks ◽  
Applied Technology ◽  
Fault Diagnosis System ◽  
Improved Bp Algorithm

This paper combined Rumelhart’s adding inertial impulse and dynamically adjusting the learning rate and proposed an improved algorithm to optimize the Back Propagation (BP) networks with applied technology. This improved BP networks is used to determining membership function and applied in fuzzy diagnosing vapor congealing equipment. The application results prove that the improved BP algorithm is effective and the convergence speed is accelerated and is much faster than the classic BP algorithm. The applied technology is very useful in the application course.

Artificial Neural Network in the Autopilot System Application of Troubleshooting

2012 ◽  
Vol 263-266 ◽  
pp. 3198-3202
Author(s):  
Peng Zhang ◽  
Shi Chao Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Fault Isolation ◽  
Bp Algorithm ◽  
Diagnosis System ◽  
System A ◽  
Neural Network Structure ◽  
Fault Diagnosis System ◽  
Artificial Neural

the existing fault diagnosis system in fault detection aspects of Boeing 737 A/P is effective, but in fault isolation aspects performance is poor, therefore using ANN technology need to improve its diagnosis system. A/P for the typical fault, the three layers feed forward artificial neural network structure, this paper introduces the conjugate gradient BP algorithm and gives the diagnosis results. Diagnosis results show that artificial neural network can accurately identify system three typical faults, improve the efficiency of fault diagnosis and fault isolation capability.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2722-2725
Author(s):  
Jian Xue Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hybrid Algorithm ◽  
Chemical Process ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Continuous Stirred Tank Reactor ◽  
The Neural Network ◽  
Continuous Stirred Tank

Fault diagnosis is an important problem in the process of chemical industry and the artificial neural network is widely applied in fault diagnosis of chemical process. A hybrid algorithm combining ant colony optimization (ACO) algorithm with back-propagation (BP) algorithm, also referred to as ACO-BP algorithm, is proposed to train the neural network weights and thresholds. The basic theory and steps of ACO-BP algorithm are given, and applied in fault diagnosis of the continuous stirred-tank reactor (CSTR). Experimental results prove that ACO-BP algorithm has good fault diagnosis precision, and it can detect the fault in CSTR promptly and effectively.

Modeling the Hot Deformation Behaviors of As-Extruded 7075 Aluminum Alloy by an Artificial Neural Network with Back-Propagation Algorithm

2017 ◽  
Vol 36 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Guo-zheng Quan ◽  
Zhen-yu Zou ◽  
Tong Wang ◽  
Bo Liu ◽  
Jun-chao Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Strain Rate ◽  
Hot Deformation ◽  
Back Propagation ◽  
Bp Algorithm ◽  
7075 Aluminum Alloy ◽  
Ann Model ◽  
Deformation Behaviors

AbstractIn order to investigate the hot deformation behaviors of as-extruded 7075 aluminum alloy, the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and 723 K and the strain rates of 0.01, 0.1, 1 and 10 s−1 on a Gleeble 1500 thermo-mechanical simulator. The flow behaviors showing complex characteristics are sensitive to strain, strain rate and temperature. The effects of strain, temperature and strain rate on flow stress were analyzed and dynamic recrystallization (DRX)-type softening characteristics of the flow behaviors with single peak were identified. An artificial neural network (ANN) with back-propagation (BP) algorithm was developed to deal with the complex deformation behavior characteristics based on the experimental data. The performance of ANN model has been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). A comparative study on Arrhenius-type constitutive equation and ANN model for as-extruded 7075 aluminum alloy was conducted. Finally, the ANN model was successfully applied to the development of processing map and implanted into finite element simulation. The results have sufficiently articulated that the well-trained ANN model with BP algorithm has excellent capability to deal with the complex flow behaviors of as-extruded 7075 aluminum alloy and has great application potentiality in hot deformation processes.

An Improved Neural Network Algorithm and its Application in Fault Diagnosis

2013 ◽  
Vol 765-767 ◽  
pp. 2355-2358
Author(s):  
Tai Shan Yan ◽  
Guan Qi Guo ◽  
Wu Li ◽  
Wei He
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Convergence Speed ◽  
Bp Algorithm ◽  
Network Algorithms ◽  
Convergence Error ◽  
Neural Network Algorithm ◽  
Transformer Fault ◽  
Improved Bp Algorithm

Aiming at BP neural network algorithms limitation such as falling into local minimum easily and low convergence speed, an improved BP algorithm with two times adaptive adjust of training parameters (TA-BP algorithm) was proposed. Besides the adaptive adjust of training rate and momentum factor, this algorithm can gain appropriate permitted convergence error by adaptive adjust in the course of training. TA-BP algorithm was applied in fault diagnosis of power transformer. A fault diagnosis model for power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both convergence speed and precision. We can realize a fast and accurate diagnosis for power transformer fault by this algorithm.

Fault Diagnosis of Rolling Bearings Based on Multi-Scale Entropy and Ensembled Artificial Neural Network

2014 ◽  
Author(s):  
Fen Chen ◽  
Quan Liu ◽  
Qin Wei ◽  
Deng Ting ◽  
Yan Ting ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Mechanical System ◽  
Back Propagation ◽  
Rolling Bearing ◽  
Fault Detection And Diagnosis ◽  
Rolling Bearings ◽  
Multi Scale ◽  
Artificial Neural

Rolling bearing is widely used in rotating mechanical system, and its operating state has great effects on availability, reliability and the life cycle of whole mechanical system. Therefore, fault diagnosis of rolling bearing is indispensable for the health monitoring in rotating machinery system. In this paper, a method based on multi-scale entropy (MSE) and ensembled artificial neural network (EANN) is proposed for feature extraction and fault recognition in rolling bearings respectively. MSE is mainly in charge for quantizing the complexity of the nonlinear time series in different scales. Then, EANN is employed to identify various faults of rolling bearing after overcoming the two disadvantages like local minimization and slow convergence speed in back propagation neural network (BPNN). The experimental results indicate that the method based on MSE and EANN is feasible and effective to classify different categories of faults and to identify the severity level of fault in the rolling bearings. Therefore, it is available for fault detection and diagnosis in rolling bearings with good performance.

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