Gone are the days when plant operators used to wander around, listening to hear if everything sounded right. Now, diagnostics can read sensors for vibration or temperature to make sure nothing is slipping out of sync. Prognostics go even further. Plant tools can now determine when maintenance needs to occur or parts need replacing long before they break down and kill production. These tools are like a health checkup to catch a plant's heart attack before it happens. (Source: Siemens.)
The real takeaway from reading this very spot-on automation trend wrap-up is that plant floor contral and automation systems are definitely following in the footsteps of mainstream enterprise business systems in terms of leveraging the same new technology hot buttons. Advances in graphics (i.e., gaming-like capabilities), cloud computing, smart software, and remote monitoring functionality are all poised to radically change how the plant floor is run and monitored and bring far greater efficiencies and transparency to plant floor operators.
Yes, and the benefits are measurable. That's always been critical in plant operation. The benefits always have to be measurable or the tools don't get deployed. In the case of recent technology the benefits come in reduced energy consumption, increased uptime, less unplanned maintenenace and less maintenance altogether, quicker changes, and improved communication from supply through the delivery of finsihed materials.
Rob, thanks for the overview. Looks like some pretty exciting trends and new developments to watch for. I'm especially interested in increasing simulation: it's good to see this powerful technology put to very practical uses in factories. I also liked the diagrams in the Connected Automation System and Cloud Computing in Automation slides. If a picture can say 1,000 words, a diagram can say 5,000.
Alongside this article, I'd like to recomment that readers check out my story, Top 5 Roadblocks to the Digital Factory of the Future. This is an important trend, the ability to rapidly adapt (repurpose) production lines, using graphical programming tools and networks PLCs to which software can be downloaded via network links. This is a big part of the ability to go rapidly from prototyping to production.
Yes, the plant floor is definitely following in the footsteps of mainstream enterprise business systems. The question is, how much will this trend accelerate when plants who aren't yet using Ethernet finally migrate to Ethernet-based automation systems? Rockwell Automation claims that only about 60% of the equipment they sell is Ethernet-based. What happens when the other 40% finally make that move?
Alex, thanks for the link to your article. What a fascinating trend! It makes me think of the production version of what IT has been working on for awhile, the "agile enterprise," or whatever they are calling it now, for BPM.
Good question, Chuck. I would think the difference between the 60 percent and the 40 percent of Rockwell implementations can be tracked along greenfield versus brownfield plants. Ethernet is likely going into most new plants as well as some upgrades at existing plants. But I'll bet a lot of the existing plants are not going to Ethernet with their upgrades.
Rob, that division sounds a lot like what I've heard about using Ethernet for machine vision networks: it's being deployed in new systems, and not so much in existing ones, because of the difficulty of re-engineering and re-configuring hardware and software, as well as training.
Rob, I don't know the geographic distribution of where new machine vision installations are going in vs older installed base. What I do know, though, is that there are still a lot of older analog camera systems in Asia, especially Japan, so that's not considered an area where GigE is likely to take hold, at least for awhile.
Machine vision and video streaming systems are used for a variety of purposes, and each has applications for which it is best suited. This denotes that there are differences between them, and these differences can be categorized as the type of lenses used, the resolution of imaging elements, and the underlying software used to interpret the data.
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