Abstract
The turbomachinery systems on oil platforms are extremely sensitive, as their failure can cause total shutdown of the platform’s production activity. Scheduled stoppages for preventive maintenance are effective, since qualified personnel and replacement parts are available and ready. On the other hand, shutting down a turbo machine ceases petroleum production. Not rarely, maintenance people conclude the machine could have worked much longer. Monitoring the machines’ behaviors to predict the best time to stop has been an industry trend. Failure prognosis has been performed using statistical methods and Artificial Intelligence techniques, such as Neural Networks, Fuzzy Systems and Expert Systems. This study presents the results obtained by developing an expert system based on case-based reasoning for turbomachinery failure prognostics.
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Ferraz, I.N., Garcia, A.C.B. (2014). Turbo Machinery Failure Prognostics. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_37
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DOI: https://doi.org/10.1007/978-3-319-07455-9_37
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