Insulating Fault Diagnosis of XLPE Power Cables Using Multi-parameter Based on Artificial Neural Networks

2005 ◽  
pp. 609-615 ◽  
Author(s):  
Xiaolin Chen ◽  
Yonghong Cheng ◽  
Zhelei Zhu ◽  
Bo Yue ◽  
Xiaojun Xie
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Power Cables ◽  
Artificial Neural

Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Samia Chebira ◽  
Noureddine Bourmada ◽  
Abdelali Boughaba ◽  
Mebarek Djebabra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Real Time ◽  
Diagnostic Model ◽  
Operating Pressure ◽  
Content Type ◽  
Soft Computing Techniques ◽  
Blowout Preventer ◽  
Artificial Neural

PurposeThe increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.Design/methodology/approachThe starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.FindingsThe performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.Originality/valueThe performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

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