In our continuing introduction to maintenance series, we're turning our attention to predictive maintenance.
For now, read and enjoy the article, and let us know if you have any questions about your own maintenance training program at work. Plus, check out our recorded webinar on maintenance, maintainability, organizational learning, and continuous improvement and consider catching out our upcoming case-study webinar on creating training paths for maintenance-tech career development programs.
Predictive maintenance is an organized attempt to monitor and determine the condition of equipment so that maintenance is conducted only when a problem is close to occurring. In particular, predictive maintenance attempts to estimate the degree to which equipment or parts have degraded.
Predictive maintenance is unlike corrective maintenance, because it does not wait for a problem to occur before maintenance is conducted. As a result, it aims to reduce unplanned machine downtime. However, predictive maintenance is also unlike preventive maintenance, in that it doesn't rely on simply performing maintenance on a schedule, and therefore risking performing risking too early and/or when it's unnecessary.
Here's a formal definition of predictive maintenance from the Society of Maintenance & Reliability Professionals (SMRP):
Predictive maintenance, or often PDM, is an equipment maintenance strategy based on assessing the condition of an asset to determine the likelihood of failure.
Our friend and partner Dr. Klaus Blache of the University of Tennessee's Reliability & Maintainability Center explains predictive maintenance this way:
The key thing here is to note that PDM or predictive maintenance only indicates the likelihood of failure. It kind of puts you in a window of time that you can go do something about it...it's the likelihood, it’s not an exact time, like the failure and then taking appropriate action to avoid failure.
In preventive maintenance, the conditions of equipment can be measured using condition-monitoring technologies, statistical process control equipment, performance indicators, or through the use of human senses. Technologies used may include ultrasound, vibrational analysis, infrared technology, motor circuit analysis, and oil analysis.
For more on this, you might enjoy this comparison of predictive maintenance and conditions-based maintenance.