More Than Analytics: Why IIoT Demands Knowledge and Expertise
More Than Analytics: Why IIoT Demands Knowledge and Expertise
IIoT should be broken down into its component layers. From the design stage to maintenance, this is a blueprint for IIoT providers to consider.
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Industrial assets, from the smallest valve to the largest locomotive engine, each play a critical part in powering the world. To ensure these assets are reliable, industrial organizations need more than data analytics. The industrial approach to monitoring requires examination not as a collection of critical equipment, but as an entire ecosystem.
Industrial machines operate in some of the harshest environments on the planet. Whether in the middle of the ocean on an offshore platform or 35,000 feet in the air on a commercial airplane, assets must maintain mission-critical operations without relying on constant connectivity to troubleshoot issues.
When it comes to managing asset performance and measuring risk, “what if?” is difficult to quantify and terrifying to ponder for any business executive. This challenge is magnified in industrial environments where machines often need to run 24/7, every second of downtime impacts the bottom line, and equipment-related incidents can mean lives lost and serious environmental consequences. As a result, every asset counts.
In a gas turbine, a single blade can generate 500 gigabytes of data each day from sensors, and there are thousands of gas turbines worldwide, each with dozens of blades. While some common information technology (IT) software could run analytics on a turbine’s data and generate basic performance metrics, what good is the knowledge that a well-protected, $10 million turbine is 98% reliable when it’s a small valve that fails and shuts the entire plant down?
A Period of Change for Industrial Companies
Industrial companies are entering a new period of change marked by unlimited opportunities, but not every company is suited to take advantage of these opportunities. As the technology market becomes crowded by purported Industrial Internet of Things (IIoT) providers, we must remember that the industrial environment is not a playground for lessons in trial-and-error. In Iceland, a country powered by hydropower and geothermal energy, asset data and advanced software keep the entire population’s lights on by adjusting for increases in demand and changes in energy capacity. The stakes are extremely high for industrial asset failure and asset data must be analyzed as such. Keeping the lights on and assets running requires operational technology (OT) expertise married to IT excellence – the true definition of being an IIoT provider.
Embracing Every Stage of the Industrial Lifecycle
To be a true IIoT “player” – or partner – companies must embrace, participate with, and have experience in every stage of the industrial lifecycle:
- Design: It all starts with the design. The design blueprint of a machine can help operators establish baseline performance standards and even create a digital twin, a dynamic, digital representation of a machine or system that can think and act like humans, enabling companies to understand and optimize performance. The representation is so accurate, we can predict precisely when an engine is going to break down, or how it might be affected by adverse environmental conditions. However, without the proprietary design information that only original manufacturers have available, you can’t create this virtual replica to monitor behavior and improve business outcomes.
- Build: Engineers who create and prototype the equipment understand the physics behind the design to transform a blueprint into a machine. As with any piece of equipment, those that actually put it together are the ones that have the most experience with it and know if something is off, or out of spec. If you don’t have experience building machines made for industrial settings, you don’t know the nuances of that equipment and why you choose parts for a machine in that environment.
- Operate: Each piece of equipment has important characteristics and specs that help operators identify abnormalities or changes in equipment behavior to indicate signs of failure. Without hands-on operational expertise you can’t develop optimization strategies. To optimize equipment performance, you need to understand the behavior of machines and use analytics to inform changes and inefficiencies from that behavior.
- Maintain: The goal of maintaining the asset is to keep it in its ideal running state, and only to take the machine down for planned maintenance or when demand necessitates a shutdown or slowdown, preferably when demand is low. Maintenance professionals are acutely aware of the need to keep assets reliable and operating at their optimal performance levels in order to effectively manage risk, cost, production, and safety.
- Service: As with the prior stages in the industrial lifecycle, servicing equipment requires true equipment expertise to recognize various failure codes assigned to maintenance tasks. If a service provider doesn’t understand the real flaw to begin with, the equipment will never be properly restored.
Fundamentally, IT and OT play different roles in the industrial world. As data loops accelerate and the time between data collection and meaningful action gets shorter for industry, machines, and infrastructure systems are becoming engines of innovation. Adopting digital technology in the industrial world will change how we run our businesses, and we need the right tools for the job. The companies that design, build, operate, maintain, and service industrial assets have the knowledge, expertise, and skill sets to support the lifecycle of the asset, and as a result, are best suited to help others with the IIoT on their digital industrial transformation journey.
Published at DZone with permission of Eddie Amos , DZone MVB. See the original article here.
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