Contaminant materials in lubricants are signs for prediction and measurement of wear in engines. Therefore oil monitoring is considered as one of the most effective techniques for maintaining the heavy equipments. However based on oil analysis data, the identification of engine problem is very hard due to different contribution factors. Hence, this paper is purposed to analyze the wear behavior of some sets of selected heavy equipment engine based on documented oil analysis data during two years in different conditions to track engine failures using trend analysis. Furthermore the selected equipments are divided into two major groups (plantation and forestry, and general construction) based on their environment, to show the effects of conditions on the engine wear behavior.
Oil analysis technique is used as predictive and proactive tools to identify the wear modes of rubbing parts and to diagnose the faults in machinery. In this paper, the wear behavior of diesel engines, especially on oil analysis, is studied based on condition data. In terms of analyzing historical data, descriptive statistics is used as data mining tools to find the relationship between condition factors of the machine and its final status. The equipments have been monitored in two different environments which are: plantation-forestry, and general construction. Based on this relationship a specific baseline is achieved for selected sets of equipment in their specific conditions. A striking result is that the new baseline for each material is different significantly in each condition, which shows that for each condition making a specific baseline is essential.
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