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Assessing partial discharge intensity of electrical equipment based on UV detection and the anfis algorithm

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第2期14卷 245-251页

作者： Chen, Junli Wang, Jingang He, Wei Xu, Zhi Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Yunnan Elect Power Co Elect Power Res Inst Kunming 650000 Yunnan Peoples R China

UV detection has been generally used in external insulation detection of equipment. Currently used detection methods have many shortcomings, such as incomprehensive consideration of influencing factors, poor model adaption, and low accuracy. We propose a method that detects the ultraviolet pulse number (P), temperature (T), and humidity (H). Meanwhile, discharge intensity (J(')) was preliminarily estimated by UV detection using a fuzzy algorithm. Next, the anfis algorithm with a self-study mechanism and an artificial neural network was introduced, which assessed that the discharge intensity is J. A UV detection circuit was established while carrying out a field test. The preliminary evaluation data of the fuzzy were used as training and testing data of anfis. The optimized model was obtained after anfis multiple trainings. Experimental results show that the relationships between P, T, H, and J in the optimized model are consistent with those of theoretical deduction. The assessment results remain highly stable with the occurrence of changes in external conditions, which indicates the feasibility of the anfis algorithm in UV detection of discharge intensity of electric equipment. This study can provide effective suggestions for online insulation monitoring and equipment maintenance. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： UV detection anfis algorithm partial discharge insulation condition discharge intensity assessment

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Remaining Useful Life Estimation Using anfis algorithm: A Data-Driven Approcah for Prognostics

Remaining Useful Life Estimation Using ANFIS Algorithm: A Da...

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Razavi, Seyed Ali Najafabadi, Tooraj Abbasian Mahmoodian, Ali Univ Tehran Dept Elect & Comp Engn Tehran Iran Univ Sharif Dept Mech Engn Tehran Iran

ISBN: (纸本)9781538653807

One of the important matters in every industrial process is maintaining an effective maintenance schedule. To that goal, predictive maintenance has been founded based on prognostics methods. One of the best ways to present the future state of a machine is to predict its useful remaining life (RUL). Since RUL as the output of the prognostics stage is a quantity related to how much more a machine can properly operate, it can be used to create a flexible and effective maintenance schedule. In this paper, a data-driven prognostics method using adaptive neuro-fuzzy inference system (anfis) algorithm is presented. This method uses a neuro-fuzzy structure that automatically provides us with optimum clustering, fuzzy rules and structural parameters. The main goal is to estimate the RUL of aircraft engines by studying the engine degradation process with only historical data provided. The effectiveness of the proposed methods is demonstrated by a implementing the method in a numerical example and providing evaluation of results.

关键词： Prognostics Health monitoring Remaining useful life estimation anfis algorithm

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Hybrid Solar and Wind Power Generation System Using anfis algorithm

Hybrid Solar and Wind Power Generation System Using ANFIS Al...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Rajkumar, K. Ambika, K. Abhinaya, K. Nafeeza Begum, A. Teena, K. Viveka, U. Sri Sairam Engineering College Department of Eee Chennai India Madras Institute of Technology Department of Mathematics Chennai India

ISBN: (纸本)9798350394580

The potential to combine solar photovoltaics (PV) and wind energy to solve the interrelated problem of renewable energy. Focusing on the Indian subcontinent, we evaluated the possibility of establishing connectivity in Chennai, Tamil Nadu, known for its solar and wind energy resources. We analyze solar irradiance and wind speed patterns using PVsyst software, and three decades of historical data. This work presents a new photovoltaic-wind hybrid system using a modified neuro-fuzzy inference system (anfis) algorithm for maximum power point finding (MPPT). The design aims to increase energy production efficiency and land use, paving the way for sustainable and effective solutions to meet the energy needs of the Indian subcontinent and similar regions around the world. This research represents an important step towards growing more sustainable, sustainable crops for the future. © 2023 IEEE.

关键词： anfis algorithm matlab MPPT PV array PVSYST wind turbine

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Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING 2020年第1期34卷 114-126页

作者： Harandizadeh, Hooman Shahid Bahonar Univ Kerman Dept Civil Engn Fac Engn Pajoohesh SqImam Khomeni HighwayPOB 76169133 Kerman *** Iran

This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (anfis) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid anfis-GMDH topology, which leads to a new hybrid AI model called as anfis-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil-pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed anfis-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, anfis-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed anfis-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

关键词： anfis algorithm CPT-based models GMDH type neural network pile bearing capacity PSO algorithm

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A mathematical modelling and experimental study of annular-radial type magnetorheological damper

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2021年第4期66卷 543-560页

作者： Maharani, Elliza Tri Ubaidillah, Ubaidillah Imaduddin, Fitrian Wibowo, Wibowo Utami, Dewi Mazlan, Saiful Amri Univ Sebelas Maret Dept Mech Engn Surakarta Indonesia Univ Teknol Malaysia Malaysia Japan Int Inst Technol Skudai Malaysia

An experimental study was undertaken to evaluate the mathematical modelling of the magnetorheological (MR) damper featuring annular radial gap on its valve. The experiment was conducted using a fatigue dynamic test machine under particular excitation frequency and amplitude to get force-velocity and force-displament characteristics. Meanwhile, the mathematical modelling was done using quasi-steady modelling approach. Simulation using adaptive neuro fuzzy inference (anfis) algorithm (Gaussian and Generalized Bell) were also carried out to portray the damping force-displacement modelling that is used to compare with the experimental results. The experimental characteristics show that amplitudes excitation and current input affect the result damping force value. The comparison of the experimental and mathematical results presented in this paper shows a significant difference in damping force value and that the quasi-steady modelling could not significantly approach the damping force-velocity results. Moreover, the semi-active damper is compared to the passive damper. The results show that a semi-active damper performs better than a passive damper because it only requires a little power. Based on the damping force-displacement modelling, it can be seen that Gaussian has a higher accuracy rather than Generalized Bell. Discussion on the energy dissipation and equivalent damping coefficient were also accomodated in this paper. Having completed in mathematical modelling and simulation, the damper would be ready for further work in-vehicle application that is development of control system.

关键词： MR damper annular-radial MR valve quasi-steady modelling MR damper characteristics energy dissipated anfis algorithm

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An adaptive neuro-fuzzy inference model for prediction of coal char conversion in CO2 gasification

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2019年第1期41卷 21-25页

作者： Tayyebi, Shokoufe Hajjar, Zeinab RIPI Petr Refining Technol Dev Div Tehran Iran

Today, in industry, the gasification of coal as an important energy source is an interesting perspective. In this study, the application of adaptive neuro-fuzzy inference system (anfis) technique for estimation of char conversion in CO2 gasification is investigated. The main variables affecting char conversion are particle size, reaction time, and reaction temperature, which are chosen as input variables of the proposed model. Experimental data which are gathered from the literature are applied for training, testing, and validation of developed anfis model. The results reveal the exact estimation of char conversions with the corresponding experimental values with the regression coefficients (R-2) greater than 0.99.

关键词： anfis algorithm coal char conversion gasification modeling

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Assessment of groundwater corrosivity in Hamedan Province, Iran using an adaptive neuro-fuzzy inference system (anfis)

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GEOSCIENCES JOURNAL 2011年第4期15卷 433-439页

作者： Abyaneh, Hamid Zare Varkeshi, Maryam Bayat Mohammadi, Kourosh Howard, Ken Marofi, Safar Bu Ali Sina Univ Dept Irrigat & Drainage Engn Hamadan Iran Tarbiat Modares Univ Dept Environm Engn Water Engn Res Inst Tehran Iran Univ Toronto Dept Phys & Environm Sci Scarborough ON M1C 1A4 Canada

The Langelier Saturation Index (LSI) and Puckorius Scaling Index (PSI) were used to study the incrustation/corrosion potential of groundwaters in Hamedan Province, Iran. The LSI and PSI indices correlated strongly with total dissolved solids (TDS) (mgL(-1)), pH and HCO (3) (-) (mmolL(-1)) as revealed by determination coefficients of 0.90 and 0.99 for LSI and PSI, respectively. Subsequently, a trained adaptive neuro-fuzzy inference system (anfis) was deployed to predict the corrosion behavior of water in unsampled or partially sampled locations using available total dissolved solids (TDS), pH and HCO (3) (-) as input data. Excellent agreement between the anfis simulations and the indices determined using conventional LSI and PSI techniques confirms that modeling using the anfis approach will allow water corrosion potential to be determined reliably and inexpensively using only TDS, pH, and HCO (3) (-) data.

关键词： corrosion groundwater Langelier index Puckorius index anfis algorithm

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The Effect of Adaptive Neuro-fuzzy Inference System (anfis) on Determining the Leadership Perceptions of Construction Employees

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2023年第6期47卷 4145-4157页

作者： Keles, Abdullah Emre Haznedar, Bulent Keles, Muemine Kaya Arslan, Mustafa Turan Adana Alparslan Turkes Sci & Technol Univ Fac Engn Dept Civil Engn Adana Turkiye Gaziantep Univ Engn Fac Dept Comp Engn Gaziantep Turkiye Adana Alparslan Turkes Sci & Technol Univ Fac Comp & Informat Dept Comp Engn Adana Turkiye Mustafa Kemal Univ Dept Comp Technol Hatay Turkiye

In the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees' productivity has been investigated using machine learning. The effects of anfis method and the use of genetic algorithm (GA) on the training of anfis for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by anfis-genetic algorithm (anfis-GA), anfis-backpropagation algorithm (anfis-BP), and anfis-hybrid algorithm (anfis-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when anfis-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, anfis, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.

关键词： anfis algorithm Construction management Feature selection Genetic algorithm Machine learning Leadership perception

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Rolling Bearing Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition 4

Rolling Bearing Fault Diagnosis Based on Improved Complete E...

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4th International Conference on Control Engineering and Information Technology (CEIT)

作者： Attoui, Issam Fergani, Nadir Oudjani, Brahim Deliou, Adel Res Ctr Ind Technol CRTI POB 64 Cheraga Algeria

ISBN: (纸本)9781509010554

In order to rolling bearing fault diagnosis using vibration signal analysis, this paper presents a new procedure based on the Improved Complete Ensemble Empirical Mode Decomposition ICEMD. In this procedure, firstly, in order to calculate the feature vector, we propose the use a combination of the Improved Complete Ensemble Empirical Mode Decomposition ICEMD and Entropy techniques for determining the entropy values for each one of the five first intrinsic mode functions (IMFs) of the ICEMD. Lastly, using the calculated feature vector, the Adaptive-Network-based Fuzzy Inference System anfis algorithm is used as a classifier system. In the experimental step, twelve different health bearing conditions were introduced to provide that the proposed approach can be an effective and efficient method for processing bearing fault signals.

关键词： vibration signals analysis processing bearing fault signals anfis algorithm improved CEEMDAN

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Estimate and Predict the Foreign Currency Exchanging Rates by Using Adaptive Neuro Fuzzy Inference System and Deep Learning algorithms 4

Estimate and Predict the Foreign Currency Exchanging Rates b...

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4th International Conference on Information Communication and Software Engineering (ICICSE)

作者： Bai, Ying Wang, Dali Johnson C Smith Univ Dept Comp Sci & Math Charlotte NC 28216 USA Christopher Newport Univ Dept Phys & Comp Sci Newport News VA 23606 USA

ISBN: (纸本)9798350351705;9798350351699

To correctly and accurately predict and estimate the exchanging rates among various currencies to get the maximum profit is a challenging task, and it is critical important to all financial institutions under the current fluctuation situation. In this study, we try to use different AI methods and algorithms, such as Adaptive Neuro Fuzzy Inference System ( anfis) and Deep Learning (DL), to easily and correctly predict and estimate the current currency exchanging rate. Combining with some appropriate pre-data-processing techniques, the current currency exchanging rates could be accurately and quickly estimated via those models. In this research, both algorithms are designed and built to help decision makers working in the financial institutions to easily and conveniently predict the current exchanging rates. The minimum training and checking RMSE values for anfis model can be 0.0009828 and 0.001713. The minimum MSE value for DL model is 0.0000047 with a regression value of 0.9958.

关键词： anfis algorithm deep learning estimate and predict exchanging rates for currencies AI applications in financial implementations

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