Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/847153
6 | July - August 2017 | www.machinerylubrication.com OCME (score of 72). Instead of 10 percent root cause saves, we now have 50 percent and only 10 percent misses. Case #3: World-class Condition Monitoring and Inspection This case applies high inspection frequency and intensity for both technology-based condition moni- toring and inspection tasks. At this high level of surveillance, most all reportable conditions are detected and remediated in the proactive domain (70 percent root cause saves). All others are detected early in the predictive domain. This translates to an impressive OCME score of 93 across all machines and reportable events. Optimization It would be negligent to conclude this column without a short reminder about optimization. There is a cost to condition monitoring, as we all know. This cost is influenced by frequency and intensity. The optimum reference state (ORS) for condition moni- toring and inspection must be established. Our objective is to optimize the OCME in the context of machine criticality and failure mode ranking. I've addressed this subject extensively in past columns. Please refer to my Machinery Lubrication articles on the ORS, overall machine criticality (OMC) and failure modes and effects analysis (FMEA). As a final note, my reference to intensity should not be glossed over as unimportant. It is a driving factor to boosting the OCME score. Achieving condi- tion monitoring and inspection intensity has as much to do with culture as it does with the available budget or access to technology. Training and management support define the maintenance culture. These soft, human factors require a high level of attention to achieve excellence in lubrication, reliability and asset management. You can read more about these factors at MachineryLubrication.com. About the Author Jim Fitch has a wealth of "in the trenches" experience in lubrication, oil analysis, tribology and machiner y failure investigations. Over the past two decades, he has presented hundreds of courses on these subjects. Jim has also published more than 200 technical ar ticles, papers and publications. He ser ves as a U.S. delegate to the ISO tribology and oil analysis working group. Since 2002, he has been the director and a board member of the International Council for Machiner y Lubrication. He is the CEO and a co-founder of Noria Corporation. Contact Jim at jf itch@noria.com. Terms and Definitions • Reportable Condition — This is an abnormal condition that requires correc- tion. A reportable condition could be either a root cause or an active failure event or fault. • Proactive Domain — This is the period of time when there is a reportable root cause condition but no significant loss of machine life has occurred. Unless detected and corrected, the condition will advance to the predic- tive domain. • Predictive Domain — This follows the proactive domain and is also known as the failure development period. The predictive domain begins at the inception of a reportable failure condition (e.g., severe misalignment) or fault and ends at the approaching end of operational service life. • RUL — Remaining useful life is an estimate of the remaining service life of a machine when an active wear or failure condition has been detected and remediated. Machines start with an RUL of 100 percent. As they age and wear occurs, the RUL approaches zero. • Root Cause (RC) Saves — Root cause saves is the percent of reportable conditions that were detected and remediated in the proactive domain. The higher this number the better. All RC saves leave RUL unchanged. • Predictive Saves — This refers to reportable conditions that have advanced to the predictive domain and are detected and remediated prior to operational failure. The RUL of the machine was lowered during the time the reportable condition remained undetected and uncorrected in the predictive domain. • X — This is a timeline point when a reportable condition (e.g., root cause of a fault) is detected and remediated. It also represents operational failure when not detected in the proactive or predictive domains. • Misses — Misses refer to the percentage of reportable conditions that advance to an undetected operational failure. The lower this number the better. • Overall Condition Monitoring Effectiveness (OCME) — This metric defines the overall effectiveness of condition monitoring (inspection combined with technology-based condition monitoring). This is quantified as the average change in percent of remaining useful life (RUL) across all machines and reportable conditions during the reporting period. The higher this number, the more effective condition monitoring is at detecting and correcting reportable conditions early. • Condition Monitoring Interval — This refers to the time interval between technology-based condition monitoring events (vibration, oil sampling, thermography, etc.). • Condition Monitoring Intensity — This refers to the number of condition monitoring technologies in use and the intensity of their use. For example, an oil analysis test slate involving numerous tests with skillful data interpreta- tion would be referred to as intense. • Inspection Interval — This refers to the time interval between machine inspections by operators and technicians. • Inspection Intensity — This refers to the number of inspection points and the examination skills of the inspector. AS I SEE IT