scholarly journals Application of CMAC Neural Network to Solar Energy Heliostat Field Fault Diagnosis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Neng-Sheng Pai ◽  
Her-Terng Yau ◽  
Tzu-Hsiang Hung ◽  
Chin-Pao Hung
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Solar Energy ◽  
Bp Neural Network ◽  
Large Scale ◽  
Rbf Neural Network ◽  
Back Propagation ◽  
Training Time ◽  
Cmac Neural Network ◽  
The Individual

Solar energy heliostat fields comprise numerous sun tracking platforms. As a result, fault detection is a highly challenging problem. Accordingly, the present study proposes a cerebellar model arithmetic computer (CMAC) neutral network for automatically diagnosing faults within the heliostat field in accordance with the rotational speed, vibration, and temperature characteristics of the individual heliostat transmission systems. As compared with radial basis function (RBF) neural network and back propagation (BP) neural network in the heliostat field fault diagnosis, the experimental results show that the proposed neural network has a low training time, good robustness, and a reliable diagnostic performance. As a result, it provides an ideal solution for fault diagnosis in modern, large-scale heliostat fields.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

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.

scholarly journals Research on Virtual Machine Response Time Prediction Method Based on GA-BP Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jun Guo ◽  
Shu Liu ◽  
Bin Zhang ◽  
Yongming Yan
Keyword(s):  
Neural Network ◽  
Response Time ◽  
Virtual Machine ◽  
Bp Neural Network ◽  
Large Scale ◽  
Virtual Machines ◽  
Back Propagation ◽  
Prediction Method ◽  
Critical Issue ◽  
Machine Component

Cloud application provides access to large pool of virtual machines for building high-quality applications to satisfy customers’ requirements. A difficult issue is how to predict virtual machine response time because it determines when we could adjust dynamic scalable virtual machines. To address the critical issue, this paper proposes a prediction virtual machine response time method which is based on genetic algorithm-back propagation (GA-BP) neural network. First of all, we predict component response time by the past virtual machine component usage experience data: the number of concurrent requests and response time. Then, we could predict virtual machines service response time. The results of large-scale experiments show the effectiveness and feasibility of our method.

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.

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

2019 ◽  
Vol 10 (04) ◽  
pp. 1950024 ◽  
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.

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.

scholarly journals A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Chenxi Ding ◽  
Wuhong Wang ◽  
Xiao Wang ◽  
Martin Baumann
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Driving Simulator ◽  
Back Propagation ◽  
Urban Traffic ◽  
Lane Changing ◽  
Training Time

The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

Fault Diagnosis Method for the Rolling Bearing Based on Information Fusion and BP Neural Network

2012 ◽  
Vol 538-541 ◽  
pp. 1956-1961 ◽  
Author(s):  
Jin Min Zhang ◽  
Yin Hua Huang ◽  
Si Ming Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Back Propagation Neural Network ◽  
Weighted Fusion ◽  
Diagnosis Method

Abstract. In order to diagnose the fault of rolling bearing by the vibration signal, a new method of fault diagnosis based on weighted fusion and BP (Back Propagation) neural network was put forward. At first, the vibration signal from the sensors was wave filtered through the method of correlation function, then the fused signal was obtained by the classical adaptive weighted fusion method, the multi-type characteristics parameters was to be as a neural network input. Finally, the fault diagnosis of rolling bearing was realized by the BP neural network, and the results show that the multi-sensor information fusion fault diagnosis method can be proved effectively to achieve the fault diagnosis of rolling bearing.

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