Abstract
The diagnosing of combustion engines with vibration methods is especially difficult due to the presence of multiple sources of vibration interfering with the symptoms of damages. The diagnosing of engines with vibro-acoustic methods is difficult also due to the necessity to analyse non-stationary and transient signals. Various methods for selection of usable signal are utilised in the diagnosing process. Changes of the engine technical condition resulting from early stages of wear are difficult to detect because of the effect of mechanical defect masking by adaptive engine control systems. The paper presents an attempt to evaluate the wear of a piston-cylinder assembly with the aid of vibration signal recorded on spark ignition engine body. The subject of the study was a four-cylinder combustion engine 1.2 dm3, based on the measurement of accelerations of body vibrations and entropy of wavelet packet transform for fault pattern identification. According to the studies carried out, it is possible to utilise probabilistic artificial neural networks for the evaluation of the clearance in piston-cylinder assembly.
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Czech, P. (2011). An Intelligent Approach to Wear of Piston-Cylinder Assembly Diagnosis Based on Entropy of Wavelet Packet and Probabilistic Neural Networks. In: Mikulski, J. (eds) Modern Transport Telematics. TST 2011. Communications in Computer and Information Science, vol 239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24660-9_12
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DOI: https://doi.org/10.1007/978-3-642-24660-9_12
Publisher Name: Springer, Berlin, Heidelberg
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