Intelligent Industrial Machinery: Moving beyond predictive maintenance
How a holistic view of your business can help you reproduce optimal conditions for continued peak performance.
IoT technology and the subsequent interconnectivity allows us to understand the correlation between machine inputs and productivity outputs. Imagine integrating environmental conditions into these inputs and understanding the correlations for optimal machine performance so optimal conditions can be emulated.” – Eric Croeser, Director of Partnerships – Mining
When it comes to human function performance, companies spend exorbitant amounts of money creating peak performance conditions. Office environments are curated, furniture specially designed and, when it comes to sport, race conditions emulated. In fact, altitude chambers and sports psychologists are becoming more popular, even amongst non-professional sports enthusiasts, and studies into peak experiences or ‘flow’ are well documented, all with the single goal of helping humans become more productive, with the same tool – their body.
“In flow, as attention heightens, the slower and energy-expensive extrinsic system (conscious processing) is swapped out for the far faster and more efficient processing of the subconscious, intrinsic system. “It’s an efficiency exchange,” says American University in Beirut neuroscientist Arne Dietrich, who helped discover this phenomena. “We’re trading energy usually used for higher cognitive functions for heightened attention and awareness.” – Steven Kotler, Time.com
Yet, when it comes to industrial machinery used in primary operational processes, the conversation seems somewhat stunted. Machine performance varies greatly from shift to shift and, while machinery gets stronger, faster and lasts longer, but it doesn’t get smarter.
As digitalisation becomes more common place, more insights are being gathered into the performance of machinery. Unbiased, factual insights.
There’s an increasing drive to deploy IoT technology to drive predictive and prescriptive maintenance that utilises engineering downtime and minimises disruptions to operations, but imagine a world that takes it one step further.
Imagine a world where interoperability between different disparate systems within the mining environment allows us to not only delve into machine learning and predictive maintenance, but to understand the correlation between machine inputs and productivity outputs. Imagine integrating environmental conditions into these inputs and understanding the correlations for optimal machine performance. Imagine a world where you can easily create a holistic view of your business to help you reproduce optimal conditions for continued peak performance.
Now stop imagining. That world is here.
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