What Is Predictive Maintenance?

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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.

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Jeffrey Dalto

Jeffrey Dalto

Jeffrey Dalto is an Instructional Designer and the Senior Learning & Development Specialist at Convergence Training. He's worked in training/learning & development for 25 years, in safety and safety training for more than 10, is an OSHA Authorized Outreach Trainer for General Industry OSHA 10 and 30, has completed a General Industry Safety and Health Specialist Certificate from the University of Washington/Pacific Northwest OSHA Education Center and an Instructional Design certification from the Association of Talent Development (ATD), and is a member of the committee creating the upcoming ANSI/ASSP Z490.2 national standard on online environmental, health, and safety training. Jeff frequently writes for magazines related to safety, safety training, and training and frequently speaks at conferences on the same issues, including the Washington Governor's Safety and Health Conference, the Oregon Governor's Occupational Safety and Health Conference, the Wisconsin Safety Conference, the MSHA Training Resources Applied to Mining (TRAM) Conference, and others.

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