Machinery Lubrication

ML_July_August_2017_Digital

Machinery Lubrication magazine published by Noria Corporation

Issue link: https://www.e-digitaleditions.com/i/847153

Contents of this Issue

Navigation

Page 6 of 80

4 | July - August 2017 | www.machinerylubrication.com AS I SEE IT This is quantified as the average percentage of remaining useful life (RUL) across all machines and reportable conditions during the reporting period (say, one year). The higher this number, the more effective condition monitoring is in detecting and correcting reportable conditions early. Machines that have no reportable condi- tions or failures are not included in this metric. A perfect OCME score is 100, meaning the RUL across all machines from the beginning of the reporting period to the end was unchanged. This can be normalized to total machine operating hours (for the entire group of machines included in the OCME metric). To show how the OCME works, let's look at three examples. Refer to the sidebar on page 6 for definitions of the terms used. Each of the three cases considers different condition monitoring and inspection intervals (frequency) and intensity. Again, intensity refers to the skill and effectiveness of condition monitoring and inspection tasks. Ten hypothetical reportable conditions are used in each case. These could be misalignment, unbalance, hot running bearings, high wear debris, wrong oil, lubricant starvation, water contamination, etc. Reportable conditions detected in the proactive domain are considered to have 100 percent RUL. Operational failure means zero percent RUL. Those conditions detected early in the predictive domain have a higher RUL than those approaching operational failure. The beginning point of the predictive domain is the inception of failure. Case #1: Common Intervals at Low Intensity In this scenario, very few of the reportable conditions are detected in the proactive domain (at the root cause stage). Most conditions advance to the predictive domain or operational failure. The causes of this are low skill and inten- sity of the condition monitoring and inspection tasks. The RUL of each reportable condition is estimated and tallied up to derive the OCME score, which is 35.5 in this case. Some 40 percent of the reportable conditions were misses, and only 10 percent were root cause saves. Case #2: Common Intervals at High Inspection Intensity This case is the same as the first with the exception of the inspection skill and competency (high intensity). This dramatically affects the 20 100 100 80 90 100 100 30 0 100 100 100 100 90 100 50 100 100 90 100 PROACTIVE DOMAIN PREDICTIVE DOMAIN OPERATIONAL FAILURE RUL PROACTIVE DOMAIN PREDICTIVE DOMAIN OPERATIONAL FAILURE RUL RUL 100% RUL 100% CONDITION MONITORING SUMMARY RC Saves = 50% Predictive Saves = 40% Misses = 10% CONDITION MONITORING SUMMARY RC Saves = 70% Predictive Saves = 30% Misses = 0% OCME OCME RUL 0% RUL 0% 72 93 INTERVAL INTENSITY Condition Monitoring Moderate Low Inspection Frequent High INTERVAL INTENSITY Condition Monitoring Moderate High Inspection Frequent High CASE 2 CASE 3 Reportable Conditions Reportable Conditions x x x x x x x x x x x x x x x x x x x x

Articles in this issue

Links on this page

Archives of this issue

view archives of Machinery Lubrication - ML_July_August_2017_Digital