A Prognostics Method Based on Back Propagation Neural Network for Corroded Pipelines

A method that employs the back propagation (BP) neural network is used to predict the growth of corrosion defect in pipelines. This method considers more diversified parameters that affect the pipeline’s corrosion rate, including pipe parameters, service life, corrosion type, corrosion location, corrosion direction, and corrosion amount in a three-dimensional direction. The initial corrosion time is also considered, and, on this basis, the uncertainties of the initial corrosion time and the corrosion size are added to the BP neural network model. In this paper, three kinds of pipeline corrosion growth models are constructed: the traditional corrosion model, the corrosion model considering the uncertainties of initial corrosion time and corrosion depth, and corrosion model also considering the uncertainties of corrosion size (length, width, depth). The rationality and effectiveness of the proposed prediction models are verified by three case studies: the uniform model, the exponential model, and the gamma process model. The proposed models can be widely used in the prediction and management of pipeline corrosion.

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Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2020-0068 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Pengpeng Cheng ◽

Daoling Chen ◽

Jianping Wang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Principal Component Analysis ◽

Prediction Model ◽

Bp Neural Network ◽

Prediction Models ◽

Back Propagation ◽

Principal Component ◽

Component Analysis ◽

Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

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A Comparison of NARX and BP Neural Network in Short-Term Building Cooling Load Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1545 ◽

2014 ◽

Vol 513-517 ◽

pp. 1545-1548 ◽

Cited By ~ 3

Author(s):

Yan Li Xu ◽

Hong Xun Chen ◽

Wang Guo ◽

Qiu Yu Zhu

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Prediction Models ◽

Back Propagation ◽

Prediction Errors ◽

Cooling Load ◽

Climate Data ◽

Load Prediction ◽

Short Term ◽

Building Cooling

A comparison of nonlinear autoregression with exogenous inputs (NARX) neural network and back-propagation (BP) neural network in short-term prediction of building cooling load is presented in this dissertation. Both predictive models have been applied in a group of commercial buildings and analysis of prediction errors has been highlighted. Training and testing data for both prediction models have been generated from DeST (Designers Simulation Toolkits) with climate data of Shanghai. The simulation results indicate that NARX method can achieve better accuracy and generalization ability than traditional method of BP neural network. This work provides a key support in smooth and optimizing control in air-conditioning system.

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ON-LINE DETECTION OF CERASUS HUMILIS FRUIT BASED ON VIS/NIR SPECTROSCOPY COMBINED WITH VARIABLE SELECTION METHODS AND GA-BP MODEL

INMATEH Agricultural Engineering ◽

10.35633/inmateh-63-20 ◽

2021 ◽

pp. 199-210

Author(s):

Bin Wang ◽

Junlin He ◽

Shujuan Zhang ◽

Lili Li

Keyword(s):

Neural Network ◽

Bp Neural Network ◽

Near Infrared ◽

Prediction Models ◽

Recognition Rate ◽

Back Propagation ◽

Nir Spectroscopy ◽

Full Spectrum ◽

Cerasus Humilis ◽

Bp Model

In order to realize the rapid and non-destructive detection of fresh Cerasus Humilis’ (CH) classification, and promote the deep-processing of post-harvest fresh fruit and improve market competitiveness, this study proposed a nonlinear identification method based on genetic algorithm (GA) optimized back propagation (BP) neural network of different varieties of fresh CH fruit. “Nongda-4”, “Nongda-5”, and “Nongda-7” fresh CH fruit were selected as research objects to collect their visible/near-infrared spectral data dynamically. The original spectra were preprocessed by moving smoothing (MS) and standard normal variate (SNV) methods, for the characteristic wavelengths were extracted with four dimension-reducing methods, namely principal components analysis (PCA), competitive adaptive reweighed sampling (CARS), CARS-mean impact value (CARS-MIV), and random frog (RF) algorithm. Finally, the BP prediction models were established based on full-spectrum and characteristic wavelengths. At the same time, the GA optimization was used to optimize the initial weight and threshold of the BP neural network and compared with the partial least squares’ discrimination analysis (PLS-DA) linear model. Through comparing the MS (7)+SNV was proved to be the best preprocessing method, the CARS-MIV-GA-BP model had the best discriminant accuracy, the prediction set accuracy was 98.76%, of which the variety “Nongda-4” and “Nongda-5” recognition rate were 100%, the variety “Nongda-7” recognition rate was 96.29%. The results show that the GA can effectively optimize the initial weights and threshold randomization of the BP neural network, improve the discrimination accuracy of CH varieties, and the CARS-MIV algorithm can effectively reduce the number of input nodes of the BP neural network model, simplify the structure of BP neural network. This study provides a new theoretical basis for the detection of fresh CH fruit classification.

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

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189279 ◽

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

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Assessment of water resource security in karst area of Guizhou Province, China

Scientific Reports ◽

10.1038/s41598-021-87066-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Liying Liu

Keyword(s):

Neural Network ◽

Water Resources ◽

Water Resource ◽

Bp Neural Network ◽

Back Propagation ◽

Water Shortage ◽

Guizhou Province ◽

Karst Area ◽

Water Resource Security ◽

Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

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Modeling Corrosion Property of High Chromium Cast Iron under H3PO4 Medium Condition Using Artificial Intelligence

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.150-151.1054 ◽

2010 ◽

Vol 150-151 ◽

pp. 1054-1057

Author(s):

Song Min Zhang ◽

Liu Jie Xu

Keyword(s):

Neural Network ◽

Weight Loss ◽

Cast Iron ◽

Bp Neural Network ◽

Corrosion Property ◽

Corrosion Properties ◽

High Chromium ◽

High Chromium Cast Iron ◽

Chromium Cast Iron ◽

Corrosion Time

The components in slurry pump suffer serious corrosion and abrasion in the phosphorus fertilizer manufacturing process because they undergo corrosion of H3PO4 medium and impact of particles at the same time. Presently, High chromium cast irons are often used to produce the components in slurry pump. In order to reveal the corrosive law, the corrosion properties of high chromium cast iron with 26wt.%Cr content (Cr26) were tested under different H3PO4 medium concentration conditions. Using back-propagation (BP) neural network, the non-linear relationship between the corrosion weight losses (W) and H3PO4 concentration, corrosion time (C, t) is established on the base of the dealing with experimental data. The results show that the well-trained BP neural network can predict the wear weight loss precisely according to H3PO4 concentration and corrosion time. The prediction results reveal that corrosion weight loss rises linearly with increasing corrosion time. The H3PO4 concentration has obvious effect on corrosion property. When H3PO4 concentration is lower than about 0.5mol/L, high chromium cast iron has well resistance to H3PO4 corrosion. However, the corrosion resistance of high chromium cast iron rapidly decreases when the H3PO4 concentration exceed about 0.8 mol/L. It is suggest the high chromium cast iron be used under the condition of H3PO4 concentration of lower 0.8 mol/L.

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Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4033156 ◽

2016 ◽

Vol 138 (6) ◽

Cited By ~ 4

Author(s):

Lizhi Gu ◽

Tianqing Zheng

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Sheet Metal ◽

Bp Neural Network ◽

Back Propagation ◽

Shape Error ◽

Back Propagation Algorithm ◽

Minor Strain ◽

Major Strain ◽

Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

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Stock Market Prediction Using Artificial Neural Networks

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.1055 ◽

2012 ◽

Vol 6-7 ◽

pp. 1055-1060 ◽

Cited By ~ 4

Author(s):

Yang Bing ◽

Jian Kun Hao ◽

Si Chang Zhang

Keyword(s):

Neural Network ◽

Prediction Models ◽

Learning Algorithm ◽

Stock Exchange ◽

Back Propagation ◽

Composite Index ◽

Network Models ◽

Back Propagation Neural Network ◽

Neural Network Models ◽

The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

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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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Discrete wavelet transformation and genetic algorithm – back propagation neural network applied in monitoring woodworking tool wear conditions in the milling operation spindle power signals

BioResources ◽

10.15376/biores.16.2.2369-2384 ◽

2021 ◽

Vol 16 (2) ◽

pp. 2369-2384

Author(s):

Weihang Dong ◽

Xiaolei Guo ◽

Yong Hu ◽

Jinxin Wang ◽

Guangjun Tian

Keyword(s):

Neural Network ◽

Tool Wear ◽

Bp Neural Network ◽

Back Propagation ◽

Discrete Wavelet ◽

Feature Extraction Method ◽

Monitoring Model ◽

Plastic Composite ◽

Spindle Power

Tool wear conditions monitoring is an important mechanical processing system that can improve the processing quality of wood plastic composite furniture and reduce industrial energy consumption. An appropriate signal, feature extraction method, and model establishment method can effectively improve the accuracy of tool wear monitoring. In this work, an effective method based on discrete wavelet transformation (DWT) and genetic algorithm (GA) – back propagation (BP) neural network was proposed to monitor the tool wear conditions. The spindle power signals under different spindle speeds, depths of milling, and tool wear conditions were collected by power sensors connected to the machine tool control box. Based on the feature extraction method, the approximate coefficients of spindle power signal were extracted by DWT. Then, the extracted approximate coefficients, spindle speeds, depths of milling, and tool wear conditions were taken as samples to train the monitoring model. Threshold and weight of BP neural network were optimized by GA, and the accuracy of monitoring model established by the GA – BP neural network can reach 100%. Thus, the proposed monitoring method can accurately monitor tool wear conditions with different milling parameters, which can achieve the purpose of improving the processing quality of wood plastic composite furniture and reducing energy consumption.

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