Failure prediction of diesel engine based on occurrence of selected wear particles in oil

•Contribution to system failure occurrence prediction based on real data from material wear – oil field data.•Contribution to inputs for maintenance optimisation, life cycle costing and mission planning procedures.•Development of mathematical application of diffusion processes – Wiener process with positive drift – in technical practice.

When assessing reliability, the principles of system failure prognostic are basic requirements. Condition-based maintenance is more demanding when estimating a system failure and residual/remaining technical life time (RTL). This paper introduces analytical and prognostic methods used for assessing system material wear to predict a failure occurrence. The principles presented in the article are based on indirect but real diagnostic oil data. We concentrate on wear metal particles such as iron (Fe) and lead (Pb) as potential failure indicators. Our approach is very different from other papers published in this area as their data were often artificial or viewed as potentially useful, but they never existed. The advantage and novelty of the outcomes presented in the article are that they might be used mainly for predicting failure occurrence and also for optimising intervals of preventive maintenance (PM), analyzing cost-benefit and planning operation/mission.