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As the management of manufacturing plants and other assets grows more complex, integrating automated monitoring of key performance indicators (KPIs) at the discrete level provides a built-in safeguard against unplanned downtime or consequential damages.
And it addresses one of the roadblocks many have discovered with a top-down enterprise-level path to IoT: the requirement for significant investments in infrastructure around equipment and business processes, with little ROI to show for it in the near term.
A detailed analysis by Accenture and GE revealed that in the Connect-Monitor-Analyze-Predict-Optimize continuum, companies reported a higher percentage of Analyze activity than Connect and Monitor over the course of IoT’s time in their industrial space. In an increasingly connected world, these findings point to an out-of-sequence disconnect.
They suggest that efforts to understand the voluminous amount of data have come before establishing the connected infrastructure needed for data-driven decision making – and well before making a coherent business case for the data collection. In other words, the raw data available often has not been connected to the specific insights required.
The discrete IoT approach to ROI
Discrete IoT views a large machine asset as a collection of systems and subsystems with data onboard that can be given a voice with sensors, and deliver ROI in short order. Every subsystem contributes to the overall performance of an asset, and knowledge in that subsystem can be harnessed to assess its health or predict its failure. Consider which products in a motion control subsystem could provide safety, reliability and productivity insights. Pumps, actuators, valves, and filters all contain current-state performance and diagnostic data.
Having a current-state view of machine health is better than nothing, but simply publishing it on a dashboard screen stops short of being able to optimize a system or a process. The greatest power of IoT stems from the ability to perform historical analysis with time-series data, whether it be KPI-driven alerts and alarms to maintenance personnel; profiles and trend curves that pinpoint root causes of failures; or notifications to ordering systems. One primary goal is to be able to examine the conditions preceding work orders, unplanned downtime or other failures as data is correlated and presented on user-friendly interfaces that make the decision options very clear.
What is the sensor?
In motion control subsystems, pumps, actuators, valves, and filters all contain current-state data about a machine’s health that can be inspected, unlocked and sent from the equipment to a decision-maker via networked, wireless sensors. A sensor converts a physical condition such as air pressure, in-line and ambient temperature, flow or vibration into an electrical signal capable of being analyzed via the cloud or local applications for a manufacturing system.
Sensors are plugged into a machine’s existing ports or through a custom integration, and trigger alerts or alarms when tied into a control system or gateway programmed to observe when thresholds for defined performance parameters are exceeded. In this way, plant professionals can safeguard the peak performance of their operations.
With today’s wireless sensors and powerful software, it is possible to:
Notify responsible parties of an event requiring preventative or corrective action through text alerts
View real-time actionable data in graphical form from a dashboard
Export data for further analysis of historic trends to any internet-connected device
Download our Ebook, Voice of the Machine: Manufacturing's Digital Transformation for an in-depth look into how a discrete approach to the Internet of Things can lay the foundation for your company's growth.
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19 Mar 2020