Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

P. Charles, Jyoti K. Sinha, F. Gu, L. Lidstone, A. D. Ball

Research output: Contribution to journalArticle

96 Citations (Scopus)

Abstract

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

LanguageEnglish
Pages1171-1185
Number of pages15
JournalJournal of Sound and Vibration
Volume321
Issue number3-5
DOIs
Publication statusPublished - 10 Apr 2009

Fingerprint

torsional vibration
Crankshafts
diesel engines
Diesel engines
Engine cylinders
Fast Fourier transforms
engines
fault detection
Fault detection
Engines
polar coordinates
moments of inertia
coders
Failure analysis
discrimination
signal processing
Signal processing
waveforms
signatures
Monitoring

Cite this

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title = "Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis",
abstract = "Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.",
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Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis. / Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

In: Journal of Sound and Vibration, Vol. 321, No. 3-5, 10.04.2009, p. 1171-1185.

Research output: Contribution to journalArticle

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