Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications
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...
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2020-06-01
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| Series: | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/116530/PDF/08_467-476_01372_Bpast.No.68-3_30.06.20_KA.pdf |
| _version_ | 1798041068345753600 |
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| author | A. Lakehal |
| author_facet | A. Lakehal |
| author_sort | A. Lakehal |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:16:21Z |
| format | Article |
| id | doaj.art-6276e48c685047b58a0a542a6aa8944a |
| institution | Directory Open Access Journal |
| issn | 2300-1917 |
| language | English |
| last_indexed | 2024-04-11T22:16:21Z |
| publishDate | 2020-06-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | Bulletin of the Polish Academy of Sciences: Technical Sciences |
| spelling | doaj.art-6276e48c685047b58a0a542a6aa8944a2022-12-22T04:00:23ZengPolish Academy of SciencesBulletin of the Polish Academy of Sciences: Technical Sciences2300-19172020-06-0168No. 3467476https://doi.org/10.24425/bpasts.2020.133374Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applicationsA. LakehalIn 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.https://journals.pan.pl/Content/116530/PDF/08_467-476_01372_Bpast.No.68-3_30.06.20_KA.pdfmaintenance planpredictive actionsprioritizationcritical asynchronous motorbayesian approach |
| spellingShingle | A. Lakehal Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications Bulletin of the Polish Academy of Sciences: Technical Sciences maintenance plan predictive actions prioritization critical asynchronous motor bayesian approach |
| title | Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications |
| title_full | Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications |
| title_fullStr | Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications |
| title_full_unstemmed | Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications |
| title_short | Bayesian graphical model based optimal decision-making for fault diagnosis of critical induction motors in industrial applications |
| title_sort | bayesian graphical model based optimal decision making for fault diagnosis of critical induction motors in industrial applications |
| topic | maintenance plan predictive actions prioritization critical asynchronous motor bayesian approach |
| url | https://journals.pan.pl/Content/116530/PDF/08_467-476_01372_Bpast.No.68-3_30.06.20_KA.pdf |
| work_keys_str_mv | AT alakehal bayesiangraphicalmodelbasedoptimaldecisionmakingforfaultdiagnosisofcriticalinductionmotorsinindustrialapplications |