Bayesian regularized artificial neural network for fault detection and isolation in wind turbine

2017 ◽  
Author(s):  
Ayoub El Bakri ◽  
Youssef Berrada ◽  
Ismail Boumhidi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wind Turbine ◽  
Fault Detection And Isolation ◽  
Artificial Neural

scholarly journals Fault detection and classification in wind turbine by using artificial neural network

2019 ◽  
Vol 10 (3) ◽  
pp. 1687
Author(s):  
N. F. Fadzail ◽  
S. Mat Zali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Wind Turbine ◽  
Renewable Energy Sources ◽  
Maintenance Cost ◽  
Ann Model ◽  
Fault Detection And Classification ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

scholarly journals Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

2019 ◽  
Vol 24 ◽  
pp. 01004
Author(s):  
Siwanu Lawbootsa ◽  
Prathan Chommaungpuck ◽  
Jiraphon Srisertpol
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Ball Bearing ◽  
Fault Detection And Isolation ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Detection ◽  
Artificial Neural ◽  
Bearing Damage

Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.

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