Bearing Fault Detection Using Beamforming Technique and Artificial Neural Networks

2013 ◽  
pp. 73-80 ◽  
Author(s):  
Walace de Souza Pacheco ◽  
Fernando A. N. C. Pinto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Artificial Neural ◽  
Beamforming Technique

Artificial neural networks and genetic algorithm for bearing fault detection

2005 ◽  
Vol 10 (3) ◽  
pp. 264-271 ◽  
Author(s):  
B. Samanta ◽  
K. R. Al-Balushi ◽  
S. A. Al-Araimi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Artificial Neural

scholarly journals Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ramón Fernando Colmenares-Quintero ◽  
Eyberth R. Rojas-Martinez ◽  
Fernando Macho-Hernantes ◽  
Kim E. Stansfield ◽  
Juan Carlos Colmenares-Quintero
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Photovoltaic Arrays ◽  
Artificial Neural

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

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