Abstract
In 1999, CS96, In-Service Lubricant Testing and Condition Monitoring Services Industry Support, was formed to address the needs of monitoring in-service oils. The standards developed by this subcommittee provide equipment customers with a known basis for the quality of the data they are receiving [Improving Used Oil Analysis Standards: Recent Efforts of ASTM D02 Subcommittee CS96, Bryan Johnson, Practicing Oil Analysis, July–Aug 2006, pp 38–41]. However, test measurements have little meaning in a condition-monitoring program if they cannot be associated with a failure mechanism of the oil or the machine. Maximum reliability of in-service machine components and fluids requires that the condition-monitoringprogram provide timely indications of machinery and oil performance and remaining usable life. In order to address these critical aspects of condition monitoring, D02.96.4, Guidelines, initiated two task forces to develop Standard Guidelines to address Alarm Limits and Trends. Reliable alarm limits for fluid and equipment characteristics are required in order to properly interpret raw lubricant test data. However, these level alarms only state how much damage has occurred. In order to generate meaningful diagnostic and prognostic information on equipment and fluid condition, the rate of change must be trended. The predictive or forecasting nature of condition monitoring is based on trending, which determines the remaining useful life of the component and fluid. This paper presents an overview of practical alarm limit calculations using statistical analysis of equipment and fluid condition data and trend analysis of condition data in the dynamic equipment-operating environment. Various trending techniques and formulas will be presented with their associated benefits and limitations. These limit and trend calculations may be applied to all techniques that provide numerical test results and for all types of equipment (diesel, pumps, gas turbines, industrial turbines, hydraulics, etc.) whether large fleets or individual industrial machines.
