scholarly journals Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098056
Author(s):  
Walid Touzout ◽  
Djamel Benazzouz ◽  
Fawzi Gougam ◽  
Adel Afia ◽  
Chemseddine Rahmoune
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Relevant Information ◽  
Singular Value ◽  
Inference System ◽  
Operation Conditions ◽  
Neuro Fuzzy ◽  
Value Decomposition

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

scholarly journals Hybrid Model of Singular Value Decomposition, ANFIS and Genetic Algorithm for Prediction of Sediment Transport in Sewers

2020 ◽  
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Amir Mosavi ◽  
Seyed Hamed Ashraf Talesh ◽  
Ali Jamali ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Size ◽  
Singular Value Decomposition ◽  
Fuzzy Inference ◽  
Sediment Concentration ◽  
Singular Value ◽  
Inference System ◽  
Pareto Curve ◽  
Median Diameter ◽  
Value Decomposition

Densimetric Froude (Fr) is the minimum velocity required to prevent sediment deposition in pipes. Prediction of Fr is of utmost importance in numerous applications in civil engineering. In this paper through using a new hybrid method. Genetic Algorithm (GA) is used for optimum selection of membership functions of Adaptive Neuro-Fuzzy Inference System (ANFIS), and Singular Value Decomposition (SVD) method is used to compute the linear parameters of ANFIS’s results section (ANFIS-GA/SVD). Also, two different target functions are known as training error (TE) and prediction error (PE) by Pareto curve, the trade-off between these functions is selected as the optimal modeling point. First, different models will be presented using the parameters affecting Fr prediction, classifying them in different groups; then the Fr parameter will be predicted for all the different models through utilizing three different sets of data and the ANFIS-GA/SVD technique. The results of the models indicate that the best Fr prediction is obtained when independent parameters such as volumetric sediment concentration (CV), ratio of median diameter of particle size to pipe diameter (d/D), ratio of median diameter of particle size to hydraulic radius (d/R) and overall friction factor of sediment (λs) use as input variables in prediction of Fr. A sensitivity analysis is also conducted for the purpose of examining the effect of each of the dimensionless parameters on Fr prediction accuracy. Comparing the results of the suggested models with the existing regression-based equations shows that ANFIS-GA/SVD (R2=0.986, MAPE=4.397, RMSE=0.206, SI=0.053, ρ=0.026, BIAS=-0.025) is more accurate than the rest of the models.

scholarly journals Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

2017 ◽  
Author(s):  
Raúl Mario del Toro Matamoros ◽  
Rodolfo Haber
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Rotation Frequency ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Performance Indices ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Ultra Precision

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

scholarly journals Sistem Deteksi Kelainan Neuromuscular Menggunakan Adaptive Neuro Fuzzy Inference System dan Dekomposisi Wavelet Elektromyogram

2021 ◽  
Vol 7 (3) ◽  
pp. 426
Author(s):  
Nuryani Nuryani ◽  
Iftita Ida Sofia ◽  
Mohtar Yunianto
Keyword(s):  
Root Mean Square ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Approximate Entropy ◽  
Singular Value ◽  
Mean Square ◽  
Spectral Entropy ◽  
Inference System ◽  
Neuro Fuzzy

Sistem neuromuscular terdiri dari saraf motorik dan otot rangka yang menghasilkan aktivitas kelistrikan pada otot dan menyebabkan otot dapat berkontraksi dan menghasilkan gerak tubuh. Gangguan neuromuscular dapat terjadi pada sel saraf yang dinamakan Neuropathy dan pada sel otot yang dinamakan Myopathy. Aktivitas kelistrikan pada otot direkam melalui suatu alat yang dinamakan Electromiography (EMG). Pada penelitian ini dilakukan identifikasi sinyal EMG pasien sehat, myopathy dan neuropathy. Neuropathy merupakan gangguan yang disebabkan oleh kerusakan sel saraf. Myopathy merupakan gangguan yang disebabkan oleh kerusakan sel otot. Penanganan dan pengobatan myopathy dan neuropathy berbeda, sehingga diperlukan suatu metode yang dapat mendiagnosis dengan tepat jenis gangguan yang dialami. Analisis karakteristik sinyal EMG dilakukan menggunakan metode dekomposisi Wavelet Discrete Dyadic dan variasi fitur Root Mean Square (RMS), approximate entropy, spectral entropy dan Singular Value Decompotition (SVD) entropy. Sinyal karakteristik yang diperoleh di identifikasi menggunakan metode klasifikasi Adaptive Neuro Fuzzy Inference System (ANFIS). Performa ANFIS dalam mengidentifikasi karakteristik sinyal EMG pada masing-masing koefisien dekomposisi, menghasilkan performa terbaik pada koefisien aproksimasi ke-5 (cA5), dengan akurasi 100%, sensitivitas 100% dan spesivitas 100%.

scholarly journals Modeling Electromechanical Overcurrent Relays Using Singular Value Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Feng-Jih Wu ◽  
Chih-Ju Chou ◽  
Ying Lu ◽  
Jarm-Long Chung
Keyword(s):  
Singular Value Decomposition ◽  
Fuzzy Inference ◽  
Singular Value ◽  
Time Characteristic ◽  
Characteristic Curves ◽  
Inference System ◽  
Novel Approach ◽  
Curve Fit ◽  
Overcurrent Relays ◽  
Value Decomposition

This paper presents a practical and effective novel approach to curve fit electromechanical (EM) overcurrent (OC) relay characteristics. Based on singular value decomposition (SVD), the curves are fitted with equation in state space under modal coordinates. The relationships between transfer function and Markov parameters are adopted in this research to represent the characteristic curves of EM OC relays. This study applies the proposed method to two EM OC relays: the GE IAC51 relay with moderately inverse-time characteristic and the ABB CO-8 relay with inverse-time characteristic. The maximum absolute values of errors of hundreds of sample points taken from four time dial settings (TDS) for each relay between the actual characteristic curves and the corresponding values from the curve-fitting equations are within the range of 10 milliseconds. Finally, this study compares the SVD with the adaptive network and fuzzy inference system (ANFIS) to demonstrate its accuracy and identification robustness.

Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading

2016 ◽  
Vol 234 (3) ◽  
pp. 368-378 ◽  
Author(s):  
A Jamali ◽  
H Babaei ◽  
N Nariman-Zadeh ◽  
SH Ashraf Talesh ◽  
T Mirzababaie Mostofi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
System Model ◽  
Hydrodynamic Impact ◽  
Inference System ◽  
Central Deflection ◽  
Multi Objective ◽  
Singular Value Decomposition Method ◽  
Neuro Fuzzy ◽  
Value Decomposition

Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables and multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system model. Also, the singular value decomposition method is applied to compute the linear parameters of the adaptive neuro-fuzzy inference system method. The important conflicting objectives of developed adaptive neuro-fuzzy inference system, namely, training error and prediction error, are obtained by dividing date sets into two parts. Hence, various optimal choices of adaptive neuro-fuzzy inference system model are provided which are non-dominated states from each other. Moreover, optimal Pareto front of such model leads to trade-off between the conflicting pair of considered objectives for two series of experiments. The results of this work indicate that multi-objective Pareto optimal design of adaptive neuro-fuzzy inference system predicts central deflection of plates with a good accuracy. In addition, the comparison between the adaptive neuro-fuzzy inference system model and exiting one demonstrates superior performance of the present approach in simulating central deflection of plates.

scholarly journals Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

2017 ◽  
Author(s):  
Raúl M. del Toro ◽  
Rodolfo E. Haber
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Rotation Frequency ◽  
Vibration Signal ◽  
Frequency Domain Analysis ◽  
Performance Indices ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Ultra Precision

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

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.

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