There are several terms being used when referring to approaches for maintaining key assets and equipment. And for many organizations, maintenance programs, effective and not so effective, directly affect the bottom line. In addition, it is evident that there is some confusion about what each term really means. So, I did some digging and found Operations & Maintenance Best Practices: A Guide to Achieving Operational Efficiency published in August 2010 by Pacific Northwest National Laboratory for the Federal Energy Management Program U.S. Department of Energy.
The guide states that keeping equipment in top working order is not typically done:
Data obtained in many studies over the past decade indicates that most private and government facilities do not expend the necessary resources to maintain equipment in proper working order. Rather, they wait for equipment failure to occur and then take whatever actions are necessary to repair or replace the equipment.
The Difference Between Preventative and Predictive
The guide also does a great job in describing the different types of maintenance, by stating:
- Preventative maintenance– Actions performed on a time- or machine-run-based schedule that detect, preclude, or mitigate degradation of a component or system with the aim of sustaining or extending its useful life through controlling degradation to an acceptable level.
- Predictive maintenance – Measurements that detect the onset of system degradation (lower functional state), thereby allowing causal stressors to be eliminated or controlled prior to any significant deterioration in the component physical state. Results indicate current and future functional capability.
Basically, predictive maintenance differs from preventative maintenance by basing maintenance need on the actual condition of the machine rather than on some preset schedule.
Predictive Maintenance and Advanced Analytics
Predictive maintenance can now be accomplished much more easily with the ability to analyze large amounts of maintenance data economically. As noted in an earlier blog, Equipment Failure Comes in Six Flavors,
Predicting equipment failure now relies on monitoring to provide early warning. The consequences of monitoring is to create a stream of information that if sampled appropriately can be combined with statistical models to detect the onset of failure. This is the new economy of predictive maintenance where low cost sensors, ubiquitous telemetry and commoditized machine learning create new opportunities for predictive maintenance . .
Learn More at Our Webinar
If you would like to learn more about using analytics to predictive maintenance needs, watch our recent webinar 3 Ways to Save on Equipment Maintenance.