Assessment of Off-Line Diagnostic Oil Data with Using Selected Mathematical Tools

The paper deals with assessment of oil filed data from heavy off-road vehicle. The oil sample is collected off-line and processed consequently in a tribolaboratory. We call the outcomes from tribolaboratory as oil field data. Firstly we apply selected regression functions for description of the most interesting oil particles generation. It is the vehicle engine and its metal – ferrum, lead or cooper – oil data which are explored for further utilisation. We apply and present methods of multi-variate regression analysis to model the metal – Fe and Pb – data and provide outcomes + estimations for system operation so far and also proposals for system further operation. The novelty is to providing inputs for soft failure identification, to helping to change the life cycle costing, to change the system of maintenance policy, system operation and mission planning.