<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samia Chebira</style></author><author><style face="normal" font="default" size="100%">Noureddine Bourmada</style></author><author><style face="normal" font="default" size="100%">Abdelali Boughaba</style></author><author><style face="normal" font="default" size="100%">Mébarek Djebabra</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Quality &amp; Reliability Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.emerald.com/insight/content/doi/10.1108/IJQRM-07-2019-0249/full/html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1409-1424</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Purpose The 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/approach The 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. Findings The 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/value The 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.</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bennouna, Aida</style></author><author><style face="normal" font="default" size="100%">Abdelali Boughaba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Perception des risques psychosociaux en milieu hospitalier : evaluation et prévention</style></title><secondary-title><style face="normal" font="default" size="100%">المؤتمر الدولي الافتراضي حول المخاطر النفسية الاجتماعية في مجال العمل -التقييم و الوقاية على ضوء التجارب الإفريقية و الفرنسية</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pub-location><style face="normal" font="default" size="100%">Constantine, Algérie</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samia Chebira</style></author><author><style face="normal" font="default" size="100%">Noureddine Bourmada</style></author><author><style face="normal" font="default" size="100%">Abdelali Boughaba</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial Neural Networks for Fault Diagnosis of Milk Pasteurization Process - A Comparative Study</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Industrial Engineering and Operations Management , March 10-12</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ieomsociety.org/ieom2020/papers/603.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Dubai, UAE</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R2 ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Samia Chebira</style></author><author><style face="normal" font="default" size="100%">Noureddine Bourmada</style></author><author><style face="normal" font="default" size="100%">Abdelali Boughaba</style></author><author><style face="normal" font="default" size="100%">Djebabra, MEBAREK</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Quality &amp; Reliability ManagementInternational Journal of Quality &amp; Reliability Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><isbn><style face="normal" font="default" size="100%">0265-671X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelali Boughaba</style></author><author><style face="normal" font="default" size="100%">CHAABANE, MABROUK</style></author><author><style face="normal" font="default" size="100%">Said Benaggoune</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust sensorless control of BLDC Motor using second derivative function of the sum of terminal voltages</style></title><secondary-title><style face="normal" font="default" size="100%">Serbian Journal of Electrical EngineeringSerbian Journal of Electrical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">275-291</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>