Given the importance of diagnosing internal combustion engines and their expandingmarket, it is crucial to investigate the complex dynamics of these engines and developa model to detect and localize component and sensor faults, independent of operatingconditions. This master’s thesis explores capturing dynamic relationships in engine signalsto detect faults. Internal combustion engines have highly nonlinear dynamics that arenot easy to capture with basic system identification methods. Consequently, some newlydeveloped methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) andAuto Regressive (AR) models, have been implemented to address the governing equations.Following previous research in this field, some analytical relations between the sensorswere known. Additionally, these equations provide the sensitivity of residuals based onthe input sensor fault. By implementing the mentioned methods and using informationfrom the analytical equations, some relationships between the sensor values and the inputfault have been identified.After finding these dynamic relationships between the sensor values, a classificationalgorithm was selected to classify the sensor faults. Additionally, to estimate the faultseverities (sensor inaccuracies), some regression models have been implemented. Further-more, it was desired to evaluate the isolability of the faults, and in this regard, some newconstraints have been considered to reconstruct the relations with the desired isolability.Finally, through several evaluations, it was shown that the proposed method is not af-fected by the driving cycle. However, this research established these methods based on aspecific case study, which is a specific turbocharged internal combustion engine.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205097 |
| Date | January 2024 |
| Creators | Sadeghi Naeini, Mohammadreza |
| Publisher | Linköpings universitet, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | LiTH-ISY-EX--24/5711--SE |
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