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

6. Visualization
Visualization is an important tool in predictive maintenance as it often closes the feedback loop, allowing maintenance managers and staff members to see the outputs of predictive models and direct their attention accordingly. Robust data science or data team tools allow maintenance managers and personnel on the ground to easily access and digest outputs in a familiar format so that the entire team—from analysts to technicians—receive the same feedback.

7. Iterate, Deploy and Automate
Deploying a predictive maintenance model into production means working with real-time data, but to iterate and deploy means providing visual real-time dashboards for maintenance teams on the ground. For some use cases, feedback can be integrated directly into the predictive maintenance process, requiring no (or little) human interaction.

Secondary Analytics
Once it's clear that a repair is necessary and initial first steps or processes have been kicked off, that's where secondary analytics come in. The goal of secondary analytics following predictive maintenance is to determine a plan of action for exactly when the asset should be taken out of service, so as to minimize disruption and loss (both imminent and future) and maximize resources.

Conclusions and Next Steps
The biggest initial win with predictive maintenance initiatives is cost savings. But after implementing a larger, more robust and more mature predictive maintenance strategy, larger opportunities begin to open from a business perspective, and high-value assets can bring in some additional revenue instead of just being costs.

Predictive maintenance also lends itself to the future of artificial intelligence (AI), in which operations will be entirely self-maintenance with very little human interaction whatsoever. AI in the predictive maintenance space would go one step beyond the steps discussed above, which would still require some manual analysis of models and outputs. These systems will watch thousands of variables and apply deep learning to find information that could otherwise be undetected that might lead to failure. Ultimately, predictive maintenance isn't so far off from AI, and businesses that get started with predictive maintenance programs now will be well-poised as market leaders in the future.

To learn more about implementing predictive maintenance, download the free whitepaper, "How To: Future-Proof Your Operations with Predictive Maintenance."

Dr. Ken Sanford is the U.S. lead analytics architect at Dataiku. He is a reformed academic economist who likes to empower customers to solve problems with data. In addition, Dr. Sanford teaches courses in applied forecasting, stress testing and big data tools at Economists at Boston College. He has a Ph.D. in economics from the University of Kentucky in Lexington, and his work on price optimization has been published in peer-reviewed journals.

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