Application of cepstrum and spectrum histograms of vibration engine body for setting up the clearance model of the piston-cylinder assembly for rbf neural classifier

The paper presents an attempt to evaluate the wear of piston-cylinder assembly with the aid of vibration signal recorded on spark ignition (SI) engine body. The subject of the study was a four-cylinder combustion engine 1.1 dm3. Diagnosing combustion engines with vibration methods is specifically difficult due to the presence of multiple sources of vibration interfering with the symptoms of damages. Diagnosing engines with vibro-accoustic methods is difficult also due to the necessity to analyse non-stationary and transient signals [1,7]. 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 for the effect of mechanical defect masking by adaptive engine control systems [5]. According to the studies carried out, it is possible to utilise artificial neural networks for the evaluation of the clearance in piston-cylinder assembly.