Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1061099
WE HAVE A BETTER SOLUTION. 800-435-7003 www.ifhgroup.com statistical analysis will inevitably deteriorate over the life of the equipment, and the rate of false alerts or missed recommendations will increase. In the end, statistical analysis proves capable in a never-changing application but often falls short in today's highly variable industrial spaces. Artifi cial Intelligence Fortunately, AI is able to resolve many of the drawbacks of these other methods. A I a nd machine lea rning techniques utilize both lab analysis data and asset failure data to recognize the diff erence between normal and alarming lab results. ese techniques can be signifi cantly more precise, as they take into account an asset's full dataset over its lifespan as opposed to relying on acceptable high and low set points to produce recommendations. By utilizing multiple signals at once, AI can provide very specifi c recommen- dations, such as alerting on a bearing failure based on tin, lead and copper content changes in the oil. AI and machine learning can also distinguish the diff erence between the slow, acceptable rise of soot in a normal operating engine and the fast rise of soot in an engine with an injector issue. rough feedback from the end user, AI and machine learning can even adapt with machinery to ensure false alerts or missed alerts don't occur due to changes in the lubricant manufacturer, machinery age or a new operation. Some organizations have begun using AI on lab data to obtain better insights from the analysis they already perform. In a recent case study conducted for a Class I railroad, 7,683 assets were tracked using conventional laboratory oil analysis as well as AI and machine learning. Over the course of the study, the AI and machine learning analytics proactively identifi ed twice as many failures as compared to the conventional lab alerts. e AI and machine learning alerts also saw an increase in precision by 3.9 times as compared to the conventional alerts. Additionally, the predictive ability of AI and machine learning increased the number of critical alerts with at least 30 or more days of forewarning by 4.5 times. is increase in alerts, accuracy and lead time off ered by AI and machine learning is causing many organizations to take notice. As the reliability and uptime improvements continue across multiple industries, more and more companies are likely to jump on the AI bandwagon. Will yours be one of them? ML About the Author Eric Holzer is the fl uids analysis lead at Uptake. Contact Eric at eric.holzer@uptake.com.