Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 792 ◽  
pp. 243-247 ◽  
Author(s):  
Alexandra Khalyasmaa ◽  
Artem Aminev ◽  
Dmitry Bliznyuk
Keyword(s):  
Expert Systems ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Gas Analysis ◽  
Assessment System ◽  
Technical Diagnostics ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Equipment State

The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for the expert system model using fuzzy inference on the basis of technical diagnostics and tests. As a case study of assessment of power transformers state based on dissolved gas analysis in the oil is presented.

scholarly journals Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases

2019 ◽  
Author(s):  
Shahaboddin Shamshirband ◽  
Alireza Baghban ◽  
Masoud Hadipoor ◽  
Amir Mosavi
Keyword(s):  
Power Plants ◽  
Fuzzy Inference ◽  
Pollution Assessment ◽  
Mercury Content ◽  
Operational Parameters ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Predicted Values ◽  
Relative Errors

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed ANFIS-PSO model the statistical meter of MARE% was implemented, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, which confirm the accuracy of the model to deal nonlinearity and representing the dependency of flue gas mercury content into the specifications of coal and the boiler type.

scholarly journals Analysis of Diagnostic Methods and Energy of Production Systems Drives

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 843
Author(s):  
Ivan Kuric ◽  
Ivana Klačková ◽  
Yury Rafailovich Nikitin ◽  
Ivan Zajačko ◽  
Miroslav Císar ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Diagnostic System ◽  
Sensory Systems ◽  
Technical Condition ◽  
Repair Time ◽  
Diagnostic Methods ◽  
Technical Diagnostics ◽  
Inference System ◽  
Diagnostic Parameters ◽  
Industrial Enterprises

This article deals with solving the urgent scientific problem of the diagnostics of drives of technological robotized workplaces with support of sensors. The dependence of diagnostic parameters on the technical state of drives of automated technological systems, which is of great economic importance for industrial enterprises, is being investigated. Diagnostic models have been developed based on sensory systems to diagnose drive models of technological robotized workplaces. The use of these models may also include monitoring systems in which it is possible to build a system for identifying detected changes. These systems identify many contradictory changes and thereby reduce the false alarm frequency of monitoring sensory systems. Numerous methods for solving technical diagnostics problems are often based on methods based on mathematical models describing work processes, as well as on spectral analysis of measured parameters, such as vibrations, noise, and electric current. A fuzzy inference system for assessing the technical condition, a system for estimating the residual resource of drives, and asystem for calculating diagnostic intervals based on fuzzy knowledge have been developed. Based on the historical trend of the diagnostic parameters, the intelligent diagnostic system determines the current technical condition of the actuator and predicts future technical condition changes, determines the remaining service life and the time intervals for diagnostics. The analysis of the time spent on planned preventive maintenance of technological equipment makes it possible to conclude that, after the modernization of equipment in 2018, the repair time was reduced from 350 h to 260 h per year (26%). Since 2019, there is a tendency to increase repair time by 30 h each year.

scholarly journals The distributed system of multipoint daily monitoring of the technical condition of the working equipment of mobile lifts with mast-type work platforms

2021 ◽  
Vol 2021 (2) ◽  
pp. 294-302
Author(s):  
Maria S. KOROVINA ◽  
◽  
Sergey K. KOROVIN ◽  
Keyword(s):  
Distributed System ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Dynamic Processes ◽  
Inference System ◽  
Daily Monitoring ◽  
Neuro Fuzzy ◽  
Work Cycle ◽  
Matlab Package ◽  
Safety Systems

Objective: To consider the requirements for safety devices of mobile lifts with working platforms and ana- lyze the specifics of daily monitoring of their technical condition. To consider the need to expand the func- tionality of the built-in safety systems of these lifts. To demonstrate the need for in-depth control and anal- ysis of the work cycle dynamic processes of mobile lifts with mast-type work platforms and provide tech- nical solutions for their implementation. Methods: The relationship of mechanical and electromechanical characteristics of electric drives of mobile lifts with mast-type work platforms with the output parameters of a distributed system of multipoint daily monitoring in various modes has been revealed using the Adap- tive Neuro-Fuzzy Inference System (ANFIS) of the MATLAB package. Results: On the laboratory bench, the values of the minimum root-mean-square errors in determining the speed, torque on the shaft, and the stator current of an asynchronous electric motor with a squirrel-cage rotor were obtained according to the data of the three-axis accelerometer LIS331DLH, gyroscope I3G4250D, and magnetometer LIS3MDL, as well as an electret microphone on training and test samples, various training methods and membership func- tions of the generated structures of ANFIS, the Adaptive Neuro-Fuzzy Inference System. Practical impor- tance: The possibility of expanding the functionality of built-in safety systems of mobile lifts with mast- type work platforms is shown based on the control of the work cycle dynamic processes by a distributed system of multipoint daily monitoring trained using the ANFIS of the MATLAB package, which can be recommended for practical use.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

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