Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

In recent years, the utilization of rotating parts, e.g., bearings and gears, has been continuously supporting the manufacturing line to produce a consistent output quality. Due to their critical role, the breakdown of these components might significantly impact the production rate. Prognosis, which is an approach that predicts the machine failure, has attracted significant interest in the last few decades. In this paper, the prognostic approaches are described briefly and advanced predictive analytics, namely a parsimonious network based on a fuzzy inference system (PANFIS), is proposed and tested for low speed slew bearing data. PANFIS differs itself from conventional prognostic approaches, supporting online lifelong prognostics without the requirement of a retraining or reconfiguration phase. The PANFIS method is applied to normal-to-failure bearing vibration data collected for 139 days to predict the time-domain features of vibration slew bearing signals. The performance of the proposed method is compared to some established methods, such as ANFIS, eTS, and Simp_eTS. From the results, it is suggested that PANFIS offers an outstanding performance compared to those methods.

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Machine Fault Detection Using Neuro-Fuzzy Inference System and Genetic Algorithms

Volume 3: 25th Computers and Information in Engineering Conference, Parts A and B ◽

10.1115/detc2005-84643 ◽

2005 ◽

Cited By ~ 1

Author(s):

B. Samanta

Keyword(s):

Genetic Algorithms ◽

Fault Detection ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Vibration Data ◽

Neuro Fuzzy ◽

Machine Fault ◽

The Time Domain ◽

Selection Of

A study is presented to show the performance of machine fault detection using adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithms (GAs), termed here as GA-ANFIS. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GA-ANFIS for two class (normal or fault) recognition. The number and the parameters of membership functions used in ANFIS along with the features are selected using GAs maximizing the classification success. The results of fault detection are compared with GA based artificial neural network (ANN), termed here as GA-ANN. In GA-ANN, the number of hidden nodes and the selection of input features are optimized using GAs. For each trial, the GA-ANFIS and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GA-ANFIS and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers (ANFIS and ANN) with GA based selection of features and classifier parameters.

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Influence of number of membership functions on prediction of membrane systems using adaptive network based fuzzy inference system (ANFIS)

Scientific Reports ◽

10.1038/s41598-020-73175-0 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Meisam Babanezhad ◽

Armin Masoumian ◽

Ali Taghvaie Nakhjiri ◽

Azam Marjani ◽

Saeed Shirazian

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Critical Role ◽

Membrane Technology ◽

Chemical Components ◽

Anfis Model ◽

Data Set ◽

Inference System ◽

Computational Costs ◽

R Value

Abstract In membrane separation technologies, membrane modules are used to separate chemical components. In membrane technology, understanding the behavior of fluids inside membrane module is challenging, and numerical methods are possible by using computational fluid dynamics (CFD). On the other hand, the optimization of membrane technology via CFD needs time and computational costs. Artificial Intelligence (AI) and CFD together can model a chemical process, including membrane technology and phase separation. This process can learn the process by learning the neural networks, and point by point learning of CFD mesh elements (computing nodes), and the fuzzy logic system can predict this process. In the current study, the adaptive neuro-fuzzy inference system (ANFIS) model and different parameters of ANFIS for learning a process based on membrane technology was used. The purpose behind using this model is to see how different tuning parameters of the ANFIS model can be used for increasing the exactness of the AI model and prediction of the membrane technology. These parameters were changed in this study, and the accuracy of the prediction was investigated. The results indicated that with low number of inputs, poor regression was obtained, less than 0.32 (R-value), but by increasing the number of inputs, the AI algorithm led to an increase in the prediction capability of the model. When the number of inputs increased to 4, the R-value was increased to 0.99, showing the high accuracy of model as well as its high capability in prediction of membrane process. The AI results were in good agreement with the CFD results. AI results were achieved in a limited time and with low computational costs. In terms of the categorization of CFD data-set, the AI framework plays a critical role in storing data in short memory, and the recovery mechanism can be very easy for users. Furthermore, the results were compared with Particle Swarm Optimization (PSOFIS), and Genetic Algorithm (GAFIS). The time for prediction and learning were compared to study the capability of the methods in prediction and their accuracy.

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Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data

IEEE EUROCON 2009 ◽

10.1109/eurcon.2009.5167779 ◽

2009 ◽

Cited By ~ 11

Author(s):

Malik S. Yilmaz ◽

Emine Ayaz

Keyword(s):

Fault Detection ◽

Fuzzy Inference System ◽

Induction Motors ◽

Fuzzy Inference ◽

Inference System ◽

Bearing Fault ◽

Bearing Fault Detection ◽

Vibration Data ◽

Neuro Fuzzy

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Application of an Adaptive Neuro Fuzzy Inference System for Low Speed Planetary Gearbox Vibration Control

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1260/0263-0923.34.3.323 ◽

2015 ◽

Vol 34 (3) ◽

pp. 323-341 ◽

Cited By ~ 12

Author(s):

Mohamed Watany ◽

Manar A. Eltantawie ◽

Shawki A. Abouel-Seoud

Keyword(s):

Vibration Control ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Low Speed ◽

Planetary Gearbox ◽

Neuro Fuzzy ◽

Gearbox Vibration

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

TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol ◽

10.15575/telka.v3i1.51 ◽

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.

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