Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1061099
8 | November - December 2018 | www . machinerylubrication.com there are also some important drawbacks. Manual Analysis Manual analysis is the process of employing domain experts to review hundreds of lab samples per day and produce recommendations from those results. For large industrial businesses, this method requires a signifi cant number of trained employees to continually monitor laboratory results. As a quarter of the industrial workforce becomes eligible to retire in the next decade, retaining existing experts while training new, younger technicians will become a growing challenge. Analysts tasked with reviewing many samples on a daily basis across several asset types can experience fatigue. At worst, this can yield missed opportu- nities for alerting on issues that may result in catastrophic machine failures. In addition, human-centered analysis can be inconsistent due to diff ering views from analyst to analyst. All of these factors can lead to varying levels of value obtained from laboratory analysis. OEM-provided Alerts Manufacturers of industrial equipment and lubricants often provide acceptable levels of wear metals, contaminants and fl uid quality. Because of a lack of context concerning the usage of each asset, the acceptable ranges from original equipment manufacturers (OEMs) often lean toward the conservative end of the spectrum to protect the asset. In prac- tice, conservative ranges can produce a high number of alerts where no defect is found on the asset or with the lubrication. For example, a 2017 case study involving a Class I railroad found no defects were actually present in 86 percent of the alerts generated. is lack of precision by the OEM alerts has created distrust in oil analysis at many organizations. Moreover, these false alerts ultimately result in thousands of dollars in unnecessary labor and material costs every year. Statistical-based Alerts Statistical analysis is used to build acceptable ranges for lab results based on a representative repository of historical sample data. ese ranges are then converted to alerts for each customer. In this method, industrial assets must be paired with a dataset with similar assets so expected normal ranges can be created. Statistical analysis can produce better alerting than previous methods but is diffi cult to manage and is dependent on the historical dataset used. Over time, the asset's normal acceptable ranges may change based on operating conditions, weather, age and other factors. Oper- ators also may switch lubricant types or top off the fl uids in their equipment. With each change, the accuracy of COVER STORY 8 | November - December 2018 | www . machinerylubrication.com START YOUR FREE SUBSCRIPTION www.machinerylubrication.com