Proceedings of SerbiaTrib '13

values influence of themeasurement noise (fromlower to higher)ISO 4406 class of oil contamination ISO 4406 class of oil contaminationISO 4406 class of oil contaminationnumber of cyclesnumber of cyclesnumber of cyclesFigure 3. Kalman filter prognostic with step-changee incontamination for different values of measurement noise4. CONCLUSIONBased on shownresults some generalconclusionss about process of prognostic using theKalman filter could be defined: Theexamples of practicall applicationn ofprognostic using Kalman filter obtainedverygood results in tracking of realmeasured values with acceptable projectionerror in case ofmeasured diagrams withoutsuddenand significant changes ofcontamination value. The biggest mistakee of projection, as a rule,is rightt at the beginning at first projectedpoint. Variations in the values of the measuredsignal and a noise measurement have a directimpact on the accuracy of the prognostic. The influence of the measurement noise onthe result of the projection canbe adjusteddusing the t definition of the value of thecorresponding parameter in the t Kalmanfilter equations. Increasing the value of thisparameter indicatess the presence of moreintensive measuringg noise and vice versa. In the case c of a sharp and abrupt prognosticusing a Kalman filter have visible and theexpected delay in the response to change. Itis clear that there is no method offorecasting,which can predicttheoccurrence of sudden, unexpected andabrupt changes in the values followed bythe diagnostic parameter. In anycase, theseephenomenapoint to theserioussirregularities and problems in the systemand certainly represent an alarmsignal. The great advantage of using Kalman filterlies in its fullindependenceandinsensitivity to the shape and characteristicssof the measured m contaminationtrend charts.ACKNOWLEDGMENTResearch presented in this paper was supportedby the Ministry of Education, Science andTechnological DevelopmenDnt of Republic of Serbia,Grant 35021REFERENCES[1] A. Muller, M.C. M Suhner, B. Iung: Formalisation of anew prognosis model for supporting proactivemaintenancee implementation on industrial system, ,Reliability Engineering & System Safety 93,pp. 234-253, , 2008.[2] J. Lee, J. Ni, N D. Djurdjanovic, H. Qiu, H. Liao:Intelligent prognostic ptools and e-maintenance,Computers in Industry 57, , pp. 476-489, 2006.[3] R.E. Kalman: A New Approach to Linear Filteringand PredictionProblems,Journal of BasicEngineering 82 (Series D) ), pp. 35-45., 1960.[4] D. Simon: Kalman Filtering, Embedded SystemssProgramming, 2001.[5] G. Welch, G. Bishop: An Introduction to theKalman Filter, Department of Computer ScienceeUniversity of North Carolina13 th International Conference on Tribology – Serbiatrib’13387