Wyniki wyszukiwania - BazTech

1

Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications

Lakehal A.

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2020

|

Vol. 68, nr 3

467--476

EN

In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

2

Reliability based rehabilitation of water distribution networks by means of Bayesian networks

Lakehal A., Laouacheria F.

Journal of Water and Land Development

|

2017

|

no. 34

163--172

EN

Water plays an essential role in the everyday lives of the people. To supply subscribers with good quality of water and to ensure continuity of service, the operators use water distribution networks (WDN). The main elements of water distribution network (WDN) are: pipes and valves. The work developed in this paper focuses on a water distribution network rehabilitation in the short and long term. Priorities for rehabilitation actions were defined and the information system consolidated, as well as decision-making. The reliability data were conjugated in decision making tools on water distribution network rehabilitation in a forecasting context. As the pipes are static elements and the valves are dynamic elements, a Bayesian network (static-dynamic) has been developed, which can help to predict the failure scenario regarding water distribution. A relationship between reliability and prioritization of rehabilitation actions has been investigated. Modelling based on a Static Bayesian Network (SBN) is implemented to analyse qualitatively and quantitatively the availability of water in the different segments of the network. Dynamic Bayesian networks (DBN) are then used to assess the valves reliability as function of time, which allows management of water distribution based on water availability assessment in different segments. Before finishing the paper by giving some conclusions, a case study of a network supplying a city was presented. The results show the importance and effectiveness of the proposed Bayesian approach in the anticipatory management and for prioritizing rehabilitation of water distribution networks.

PL

Woda odgrywa istotną rolę w codziennym życiu ludzi. Aby zapewnić klientom stałe dostarczanie wody dobrej jakości, operatorzy wykorzystują sieci wodociągowe, ich głównymi elementami są rury i zawory. W pracy opisano odnawianie sieci wodociągowych w krótkim i długim przedziale czasowym. Zdefiniowano priorytety działań renowacyjnych i skonsolidowano system informacyjny oraz system podejmowania decyzji. Dane o wiarygodności zostały sprzężone z narzędziami podejmowania decyzji co do odnowy sieci w kontekście możliwości prognozowania. Ponieważ rury są elementem statycznym, a zawory dynamicznym, zbudowano statyczno- -dynamiczną sieć Bayesowską, która pozwala przewidywać niepowodzenia w dostawie wody. Badano zależności między wiarygodnością a ustaleniem priorytetów działań renowacyjnych. Wdrożono modelowanie ilościowej i jakościowej analizy dostępności wody w różnych segmentach sieci wodociągowej oparte na statycznej sieci Bayesowskiej. Następnie użyto dynamicznych sieci Bayesowskich do oceny wiarygodności zaworów w funkcji czasu, co umożliwiło zarządzanie dystrybucją wody bazującą na ocenie jej dostępności w różnych segmentach sieci. Przed wyciągnięciem wniosków przedstawiono przykład sieci zasilającej miasto. Wyniki dowodzą znaczenia i efektywności proponowanego podejścia Bayesowskiego w planowaniu gospodarki wodnej i ustalaniu priorytetów renowacji sieci wodociągowych.

3

Optimization of the logistics function by controlling risks using influence diagram: cases of risks related to road transport

Lakehal A., Tachi F.

Computer Assisted Methods in Engineering and Science

|

2017

|

Vol. 24, no. 4

239--252

EN

The growth in the number of logistics platforms served by road, rail, waterway, and sea is a logical consequence of the extensive and rapid development of merchandise trade in a globalized economy. Transportand logistics are part of the same activity chain that allowsgoods to be transported to their destination. Dependent on the requirements of their customers and suppliers and subject to strong competition,companies in this sector must manage challenges concerningdeadlines, flexibility, and diversity of goods,while handling other risks associated with transport and logistics. The Bayesian approach, proposed inthis paper, covers all the steps necessary to implement decision support solutions for risk managementand control, starting from the identification of risks and the preparation of intervention to the conductingof various operations in crisis In this work, the predictionand the control of the road risks are conductedusing the influence diagram method, whose final objective is the optimization of the logistics function.After identifying and analyzing the different risks, the Bayesian networks (BNs) are initially used to modelthese risks and to prevent the various challenging situations from taking place in the logistics chain. Asa second step, we use the influence diagram as a tool for the decision-making procedure. Finally, a casestudy is presented to highlight the substantial contribution of this tool to controlling road risks whiletransporting goods.