Processing of Instantaneous Angular Speed Signal for Detection of a Diesel Engine Failure
Adam Charchalis and
Mirosław Dereszewski
Mathematical Problems in Engineering, 2013, vol. 2013, 1-7
Abstract:
Continuous monitoring of diesel engine performance under its operating is critical for the prediction of malfunction development and subsequently functional failure detection. Analysis of instantaneous angular speed (IAS) of the crankshaft is considered as one of the nonintrusive and effective methods of the detection of combustion quality deterioration. In this paper results of experimental verification of fuel system's malfunction detecting, using optical encoder for IAS recording are presented. The implemented method relies on the comparison of measurement results, recorded under healthy and faulty conditions of the engine. Elaborated dynamic model of angular speed variations enables us to build templates of engine behavior. Recorded during experiment, values of cylinder pressure were taken for the approximation of pressure basic waveform. The main task of data processing is smoothing the raw angular speed signal. The noise is due to sensor mount vibrations, signal emitter machining, engine body vibrations, and crankshaft torsional vibrations. Smoothing of the measurement data was carried out by the implementation of the Savitzky-Golay filter. Measured signal after smoothing was compared with the model of IAS run.
Date: 2013
References: Add references at CitEc
Citations:
Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2013/659243.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2013/659243.xml (text/xml)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:659243
DOI: 10.1155/2013/659243
Access Statistics for this article
More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().