Diagnosing faults in the timing system of a passenger car spark ignition engine using the bayes classifier and entropy of vibration signals

Today's systems for diagnosing the technical condition of machines, including vehicles, use very advanced methods of acquiring and processing input data. Presently, work is being conducted globally to solve related problems. At the moment, it is not yet possible to create a single procedure that would enable the construction of a properly functioning diagnostic system, regardless of the selected object to be diagnosed. Hence, there is a need to conduct further research into the possibility of using already developed methods, as well as their modification to other diagnostic cases. This article presents the results of research related to the use of the Bayes classifier for diagnosing the technical condition of passenger car engine components. Damage to the exhaust valve of a spark ignition engine was diagnosed. The source of information on the technical condition was vibration signals recorded at various measuring points and under different operating conditions of the car. To describe the nature of changes in the vibration signals, the entropy measures were determined for the decomposed signal using the discrete wavelet transform is proposed.