scholarly journals Damage Detection of Irregular Plates and Regular Dams by Wavelet Transform Combined Adoptive Neuro Fuzzy Inference System

2018 ◽  
Vol 4 (2) ◽  
pp. 305 ◽  
Author(s):  
D. Hamidian ◽  
J. Salajegheh ◽  
E. Salajegheh
Keyword(s):  
Finite Element ◽  
Wavelet Transform ◽  
Damage Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Damage Zone ◽  
Detection Methods ◽  
Mode Shapes ◽  
Inference System ◽  
Neuro Fuzzy

This paper presents a technique for irregular plate and regular dam damage detection based on combination of wavelet with adoptive neuro fuzzy inference system (ANFIS). Many damage detection methods need response of structures (such as the displacements, stresses or mode shapes) before and after damage, but this method only requires response of structures after damage, otherwise many damage detection methods study regular plate but this method also studies irregular plate. First, the structure (irregular plate or regular dam) is modelled by using ANSYS software, the model is analysed and structure’s responses with damage are obtained by finite element approach. Second, the responses at the finite element points with regular distances are obtained by using ANFIS. The damage zone is represented as the elements with reduced elasticity modules. Then these responses of structures are analysed with 2D wavelet transform. It is shown that matrix detail coefficients of 2D wavelet transform can specified the damage zone of plates and regular dams by perturbation in the damaged area.

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System

2016 ◽  
Vol 6 (4) ◽  
pp. 1-10 ◽  
Author(s):  
Rajeev Ranjan
Keyword(s):  
Finite Element ◽  
Fuzzy Inference System ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Crack Depth ◽  
Multiple Cracks ◽  
Rotating Shaft ◽  
Element Analysis ◽  
Inference System ◽  
Neuro Fuzzy

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

Dynamic Behaviour and Crack Detection of a Multi Cracked Rotating Shaft using Adaptive Neuro-Fuzzy-Inference System

Fuzzy Systems ◽  
2017 ◽  
pp. 1540-1551
Author(s):  
Rajeev Ranjan
Keyword(s):  
Finite Element ◽  
Fuzzy Inference System ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Crack Depth ◽  
Rotating Shaft ◽  
Inference System ◽  
Response Characteristics ◽  
Neuro Fuzzy ◽  
Cracked Structures

The presence of crack changes the physical characteristics of a structure which in turn alter its dynamic response characteristics. So it is important to understand dynamics of cracked structures. Crack depth and location are the main parameters influencing the vibration characteristics of the rotating shaft. In the present study, a technique based on the measurement of change of natural frequencies has been employed to detect the multiple cracks in rotating shaft. The model of shaft was generated using Finite Element Method. In Finite Element Analysis, the natural frequency of the shaft was calculated by modal analysis using the software ANSYS. The Numerical data were obtained from FEA, then used to train through Adaptive Neuro-Fuzzy-Inference System. Then simulations were carried out to test the performance and accuracy of the trained networks. The simulation results show that the proposed ANFIS estimate the locations and depth of cracks precisely.

scholarly journals Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2269
Author(s):  
Ahmed F. Bendary ◽  
Almoataz Y. Abdelaziz ◽  
Mohamed M. Ismail ◽  
Karar Mahmoud ◽  
Matti Lehtonen ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection Methods ◽  
Photovoltaic Systems ◽  
Inference System ◽  
Photovoltaic Array ◽  
Neuro Fuzzy ◽  
Effective Manner ◽  
Simulation Results

In the last few decades, photovoltaics have contributed deeply to electric power networks due to their economic and technical benefits. Typically, photovoltaic systems are widely used and implemented in many fields like electric vehicles, homes, and satellites. One of the biggest problems that face the relatability and stability of the electrical power system is the loss of one of the photovoltaic modules. In other words, fault detection methods designed for photovoltaic systems are required to not only diagnose but also clear such undesirable faults to improve the reliability and efficiency of solar farms. Accordingly, the loss of any module leads to a decrease in the efficiency of the overall system. To avoid this issue, this paper proposes an optimum solution for fault finding, tracking, and clearing in an effective manner. Specifically, this proposed approach is done by developing one of the most promising techniques of artificial intelligence called the adaptive neuro-fuzzy inference system. The proposed fault detection approach is based on associating the actual measured values of current and voltage with respect to the trained historical values for this parameter while considering the ambient changes in conditions including irradiation and temperature. Two adaptive neuro-fuzzy inference system-based controllers are proposed: (1) the first one is utilized to detect the faulted string and (2) the other one is utilized for detecting the exact faulted group in the photovoltaic array. The utilized model was installed using a configuration of 4 × 4 photovoltaic arrays that are connected through several switches, besides four ammeters and four voltmeters. This study is implemented using MATLAB/Simulink and the simulation results are presented to show the validity of the proposed technique. The simulation results demonstrate the innovation of this study while proving the effective and high performance of the proposed adaptive neuro-fuzzy inference system-based approach in fault tracking, detection, clearing, and rearrangement for practical photovoltaic systems.

scholarly journals High Impedance Fault Detection in MV Distribution Network using Discrete Wavelet Transform and Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Author(s):  
Veerapandiyan Veerasamy ◽  
Noor Izzri Abdul Wahab ◽  
Rajeswari Ramachandran ◽  
Muhammad Mansoor ◽  
Mariammal Thirumeni
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Discrete Wavelet ◽  
Inference System ◽  
High Impedance ◽  
Neuro Fuzzy ◽  
High Impedance Fault ◽  
Anfis Classifier

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

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