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A comparison of on-engine surge detection algorithms using knock accelerometers
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Internal Combustion Engines.ORCID iD: 0000-0001-9483-7992
KTH, School of Engineering Sciences (SCI), Mechanics.ORCID iD: 0000-0001-7330-6965
2017 (English)In: SAE Technical Papers, 2017Conference paper, Published paper (Refereed)
Abstract [en]

On-engine surge detection could help in reducing the safety margintowards surge, thus allowing higher boost pressures and ultimatelylow-end torque. In this paper, experimental data from a truckturbocharger compressor mounted on the engine is investigated. Ashort period of compressor surge is provoked through a sudden, largedrop in engine load. The compressor housing is equipped with knockaccelerometers. Different signal treatments are evaluated for theirsuitability with respect to on-engine surge detection: the signal rootmean square, the power spectral density in the surge frequency band,the recently proposed Hurst exponent, and a closely related conceptoptimized to detect changes in the underlying scaling behavior of thesignal. For validation purposes, a judgement by the test cell operatorby visual observation of the air filter vibrations and audible noises, aswell as inlet temperature increase, are also used to diagnose surge.The four signal treatments are compared with respect to theirreliability as surge indicator and the time delay between surge onsetand indication. Results show that the signal power in the surgefrequency band has reasonably good properties as surge indicator.The normal Hurst exponent is problematic, since periodic vibrationsfrom engine firing dominate the scaling behavior. Root mean squareand the above mentioned scaling exponent do not measure vibrationscaused by surge directly, but rather the reduction in housingvibrations due to the engine load drop. Nevertheless, it was found tobe possible to design an indicator that gives good results based on thechange in scaling behavior.

Place, publisher, year, edition, pages
2017.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-243019DOI: 10.4271/2017-01-2420Scopus ID: 2-s2.0-85030838869OAI: oai:DiVA.org:kth-243019DiVA, id: diva2:1284882
Conference
SAE FFL 2017
Note

QC 20190227

Available from: 2019-02-01 Created: 2019-02-01 Last updated: 2019-02-27Bibliographically approved

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Mihaescu, Mihai

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