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Eliminate unplanned downtime

Condition based maintenance: using big data to lower costs, improve efficiency and reduce maintenance.

Big data is staying true to its name—it’s doing big things. While great data-based strides have been made in medicine, entertainment and education, we’re just seeing its potential begin to bloom as an industrial tool that can help companies make their operations efficient, systematic and predictable.

Despite the fact that we’re just pioneers on the frontier of the industrial internet of things, the future of maintenance is clear: we’re headed toward a world where the use of enormous amounts of data will enable companies to learn, months or years in advance, which specific machinery assets are in need of service and when they’re likely to fail. And, perhaps most importantly, this will be a world in which companies can eliminate the costs incurred by lost business when a machinery asset unexpectedly fails.

Because we’re just at the onset of these exciting developments, it makes sense to put these technologies into context through an example. Imagine you’re a food producer: perhaps you raise cows for milk. One day the machinery you use to milk your cows breaks down. You call your engineer, but she tells you the machine needs a specific part that won’t arrive until tomorrow. Suddenly you can’t produce the milk that you’re contractually required to provide to the grocery chain in town. In all likelihood, that grocery chain and you have a strict contract in which it’s stipulated that if you don’t provide milk at a certain time and in a certain quantity, you pay a fine. All of this could be avoided if only you knew when your milking machinery would break down. Had you known about today’s disaster, you could have brought in that part months ago and scheduled maintenance at a time that didn’t interfere with your operation.

This example is a simple one. For most companies, a breakdown somewhere along the supply chain can cost many thousands per hour and produce unhappy partners and clients up and down the chain. Smart condition monitoring based on wireless sensors and artificial intelligence eliminates that risk.  Through the collection and analysis of mass amounts of data, an online monitoring system continually determines an asset’s current health and predicts its remaining useful lifetime. Smart condition monitoring is a fundamental building block for condition-based maintenance (CBM), a regime that enables scheduled maintenance based on actual developing faults, so that no surprise breakdowns occur and no unnecessary costs are incurred.

Because it relies on the AI-driven analysis of vast amounts of continuous data, true condition-based maintenance has only just become possible.  Once a company knows when its machinery will fail and in what way, gone is the risk that on any given day an asset might fail in the middle of production. While the use of CBM doesn’t mean your machinery will never fail, it does mean that you’ll know exactly when to expect failure, and it gives you the luxury of planning out, months in advance, when to service equipment so that it never fails unexpectedly, vastly improving your overall efficiency as a company.

It’s also much cheaper and more efficient to make small fixes to a machine in advance compared to fixing a serious fault at the last minute. CBM enables asset owners to perform regular maintenance on targeted problems based on knowledge of the equipment’s actual condition. CBM tells you the small faults that, if diligently addressed when they arise, hugely reduce the risk of major, expensive failures.

While smart condition monitoring and CBM are still a fluid, growing area of technology, the path to the future looks strong and exciting. As more companies collect bigger amounts of data, AI software will become even better at predicting how machinery will act, further improving efficiency and reducing cost. Regardless of how this field grows, it’s undeniable that we’re on the cusp of a fundamental change in industry, a development that will alter how companies use machinery and how machinery is maintained.


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