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Diagnostics and Prognostics of safety critical systems using machine learning, time and frequency domain analysis
Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för tillämpad signalbehandling.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
Abstract [en]

The prime focus of this thesis was to develop a robust Prognostic and Diagnostic Health Management module (PDHM), capable of detecting faults, classifying faults, fault progression tracking and estimating time to failure. Priority was to obtain as much accuracy as possible with the bare minimum amount of sensors as possible. Algorithms like k-Nearest Neighbors (k-NN), Linear and Non- Linear regression and development of rule engine to identify safe operating limits were deployed. The entire solution was developed using R (v 3.5.0). The accuracy of around 98% was obtained in diagnostics. For Prognostics, our ability to predict time to failure more accurately increases with time. Some balance must be there between learning horizon and predicting horizon in order to get good predictions with reasonable time left to hit catastrophic failure. In conclusion, the PDHM module works just as desired and makes Predictive maintenance, smart replacement and crisis prediction possible ensuring the safety and security of people on board and assets.

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
Diagnostics, Prognostics, PHM, predictive maintenance
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-17603OAI: oai:DiVA.org:bth-17603DiVA, id: diva2:1288522
Fag / kurs
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Utdanningsprogram
ETASB Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Veileder
Examiner
Tilgjengelig fra: 2019-02-13 Laget: 2019-02-13 Sist oppdatert: 2019-02-13bibliografisk kontrollert

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BTH2019Purkayastha(3175 kB)44 nedlastinger
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