Maintenance strategies based on the “prediction of failure” have always been the goal of reliability teams tasked with reducing unplanned downtime and overall maintenance costs. In fact, nearly every industry has assets where unplanned downtime is either catastrophic or extremely expensive. For these scenarios, the traditional response of reliability teams has been to build mean-time-to-failure models whereby past machine failures of similar assets in similar production modes are compiled into a statistical model. Based on the risk of failure of critical components and the severity of each failure, a preventive maintenance (PM) schedule is put in place to direct maintenance teams to replace or refurbish those components at regular intervals. If you’re reading this blog, you likely know the shortcomings of preventive maintenance schedules.

Shortcomings of Preventative Maintenance Schedules

Lack of Failure Mode Data: For failure mode data to be useful, the data points must be numerous and comparable. Many high-value assets rarely fail, which means very little failure mode data is available, which decreases the accuracy of the model. Failure mode data points must also be comparable (same asset type, same production mode, same environmental conditions), and this is also unlikely.

Needless Maintenance Cycles: When a PM schedule is put into practice, it is well known that components are being replaced far in advance of their useful life cycle. This needlessly increases maintenance costs and burdens maintenance teams who must perform the work.

Staff Shortages: A recent study by the United States census bureau found that nearly one-fourth of the manufacturing workforce is age 55 or older and a global shortage of skilled labor means the days of reliability engineers single-handedly keeping a site running are coming to an end.

Unexpected Failures: Even when a PM schedule has been developed and is being followed, it is well known that unexpected machine failures still occur.

There is a reason for this that you may not know.

Asset Failure Models Rarely Work. A comprehensive study by NASA showed that 82% of assets display a random failure pattern. This means that only 18% of assets display a failure pattern that is amenable to models based on hours or cycles of operation.

Recommended Reading: The Ultimate Guide to Predictive Maintenance 

As reliability teams began to understand the obvious shortcomings of preventive maintenance, the advancements in IoT and the ability to install industrial sensors on every asset offered a new data-driven approach. The new data-driven approach finally solved the problem that everyone knew for decades: that each asset is different. With the introduction of real-time data, reliability teams could at last begin tailoring their maintenance strategy for each individual asset.

A few years later, with the data renaissance no longer in its infancy and databases starting to fill, reliability teams were surprised that they were no closer to the promised 100% uptime. If you’re looking for a way to guard against unplanned downtime, costly repairs, and productivity loss, let’s start a conversation about a pilot program. Contact us today.

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Reed Johnson is a mechanical engineer with a Masters Degree from the University of Minnesota. Reed specializes in industrial control systems including AI-based computer vision and analytics. Reed has worked with heavy equipment in aviation, maritime, pharmaceutical manufacturing, and agriculture industries.


Source: Reliability-Centered Maintenance Guide for Facilities and Collateral Equipment”. NASA. September 2008