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Predictive Maintenance—the First Step Toward Self-Maintenance and AI

How to pave the way for artificial intelligence and self-maintenance, by first optimizing your operations with predictive maintenance
By Kenneth Sanford
Jan 07, 2018

Predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets looking to harness machine learning to minimize equipment maintenance costs. Predictive maintenance takes data from multiple and varied sources, combines it, and uses machine-learning techniques to anticipate equipment failure before it happens.

Many businesses are already using continuous monitoring technologies—Internet of Things (IoT)-connected devices, for example. This is a good start, but the key lies in not just simply monitoring the output of various data (which is how many companies currently use it), but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights and anticipate future outcomes.

Going one step further, the most innovative enterprises, no matter what type of high-capital assets they maintain, see the largest cost savings from predictive maintenance—not only by putting a system in place that returns simple predictive outputs, but by rethinking and optimizing their entire maintenance strategy as a whole, from top to bottom. This means:

• Paving the way for artificial intelligence (AI) and self-maintenance by optimizing for (and automating) the immediate next steps once predictive systems point to imminent failure, whether this automatically triggers a work order, notifies a technician or certain team, places an order for a replacement part, etc.

• Considering a combination of maintenance strategies to determine the optimal cost-saving combination of predictive and traditional maintenance, perhaps even on an asset-by-asset basis.

• Identifying how to best execute necessary repairs through second-order or secondary analytics. This means having a process in place for an entire deeper layer of analysis to determine the best time to actually remove the asset from service, as well as which additional repairs, if any, should be conducted simultaneously to minimize the cost of having to remove the asset again for a different failure within a short window of time.

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