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Engine is the most common and important component in all the vehicles. An improved understanding of noise signal is required for the early detection of incipient engine component failure to achieve high reliability. In diesel engine, the combustion noise is so named to distinguish it from the noise of mechanical sources in the engine such as the valve train, fuel injection system and timing gears. On the other hand, piston slap is a combination of combustion and mechanical sources within the engine, and is not straightforward one. However, the aim of the present work is to separate the noise contributions of combustion and mechanical (motored) sources. Moreover, the noise spectra obtained when the engine was in fired condition were used to detect engine events and faults. The noise spectra were predicted based on the vibration velocity measurements, which were taken over the diesel engine surfaces (panels).

All automotive fleets' ownerships want to increase their fleets' availability; therefore, they resort to consider the conventional maintenance policy. Despite of, this policy increases fleets availability but the cost will be high. This is due to replace the parts before the end of their lifetime. On the other hand, this policy is based on time where it is not cost-effective. Consequently, this conventional maintenance policy is not adequate to fulfill the needs of high availability for automotive fleets. However, in this paper the study is directed to condition-based predictive maintenance concept as an alternative policy to determine fleet's health for increasing the fleet availability and to reduce the operating cost. The concept is based on predicting the system degradation and calculating the reliability function for the component. An application carried out on internal combustion engine crankshaft main bearing to illustrate the effectiveness of this technique.