I remember my introduction to industrial “fieldbus” technology that came even before I became an editor with Control Engineering. That was in the 90s. Another indication of the maturity of the automation and control market lies in this announcement from the EtherCat Technology Group.
I love press releases. The writers are not bashful about proclaiming their virtues. I’m sure there are a couple of others who would contest some of the proclamations. But, this network did bring Ethernet early on and works well with motion control more so than some others.
Beckhoff Automation, a pioneer in automation solutions, proudly commemorates the 20th anniversary of EtherCAT, the groundbreaking industrial Ethernet ystem that transformed the world of industrial networking. Since its introduction at Hannover Messe in 2003, EtherCAT has emerged as the leader in speed, flexibility and openness, solidifying its position in the global market through its reliability and continuous technical advancements. Today, EtherCAT remains an open IEC standard and empowers companies across industries to leverage its key technological advantages.
EtherCAT combines the standard Ethernet protocol with the reliability, performance and stability required for industrial communication, delivering significant advantages:
Fast communication and nanosecond synchronization via distributed clocks
Reduce CPU loads by up to 30%
Flexible topology: tree, star, drop, line, circle, etc. – or any combination of these is possible
Automatic addressing of nodes
Comprehensive and highly localized diagnostics
Noise immunity and high bandwidth
Integrated safety
No switches are necessary, masters only need an Ethernet port, and sub-devices can use a variety of ESC chips – all of which keep expenses near or less than legacy fieldbus costs.
What’s the old saying, “You can’t control what you can’t measure”? Here is news about a partnership to better measure environmental footprint.
KPMG LLP and Context Labs today announced an alliance to help companies better measure, quantify, and reduce their environmental footprints using distributed ledger technology and advanced climate data and analytics, enabled with machine learning and AI. The alliance will provide strategic support to companies working to reduce environmental impact, analyze their efforts to decarbonize, and provide transparent and traceable data, as investors, regulators, and other stakeholders increasingly demand ESG disclosures.
The alliance combines Context Labs’ technology with KPMG’s environmental monitoring, analytics and reporting for customers across market sectors. The collaboration between Context Labs and KPMG U.S. is already demonstrating results with Williams, an energy company handling 30% of the natural gas in the United States.
Williams is leveraging Context Labs’ Decarbonization-as-a-Service platform to track and measure emissions across the full energy value chain, executing the energy industry’s first end-to-end methane intensity certification, supported by KPMG. The platform provides transparent data and insights on the carbon intensity and climate performance attributes for energy sector customers.
I don’t travel like I used to. There is no pressure from sales to see and be seen in the faint hope of selling advertising.
However, I will be at Siemens Realize in Las Vegas June 12-15 and Honeywell User Group in Orlando June 19-21. I am curious to find out how people are using all of this technology, what they see with future applications, and perspectives on future job roles and functions.
You can schedule a time by pinging me at [email protected] or just stop me if you see me around. The pictures on the website are a bit dated, but I still pretty much look like them.
What workforce problem has populated every industrial trade press issue for the past 10-15 years? Baby Boomers are retiring taking decades of knowledge with them. How can the new workforce of engineers and technicians be provided the skillset quickly?
Actually, new people have come into the industry continually for years. Recall the conferences you’ve attended (except during the pandemic) over the past several years. The demographic has been trending younger and diverse.
However, the search for better ways to train new workers is, and will be, never ending. We’ve traveled the simulation path. Better virtual reality technology may improve that experience.
Another solution comes from a new company formed in Detroit with former Siemens executives. (The press release says, “with a vision to use AI to accelerate skills training for shop-floor and other highly technical skilled trades workers.”) DeepHow utilize generative AI to enable teams to capture their know-how using a smartphone, turning expertise into step-by-step how-to videos that can be translated into multiple languages in a matter of minutes, to create a unique and extremely valuable library of organizational knowledge that can be effectively transferred to every worker.
This looks like one of many cool uses of technology that may not translate as well into practical everyday use as might be desired. But…it may, if it is easy to use, find widespread usage among industrial engineers.
The recent news concerns funding of the startup. DeepHow has closed $14M in Series A funding. Owl Ventures, a specialist in education technology investing, led the round, with participation from another new investor: LG Technology Ventures. Existing investors Sierra Ventures, Qualcomm Ventures, Osage Venture Partners, and Foothill Ventures also participated in this round.
DeepHow plans to invest in go-to-market initiatives to accelerate its business growth, and to ramp up its investments in product, engineering, and generative AI innovation. In response to customer demand, DeepHow has already fast-tracked development of powerful product enhancements that leverage the power of generative AI to accelerate SOP video creation, multimodal workflow generation, AI chat assistance, and strategic knowledge mapping.
While I see companies that are predominantly American rushing to capture open technology initiatives and make them as proprietary as possible, here is another predominantly German initiative pushing for using standards to move manufacturing technology forward.
This news came to me indirectly from the PI (Profibus and Profinet standards organization). Check it out. Do you find this potentially useful?
What is MTP?
By now, we all know OPC UA is really good at supporting the use-cases for not only horizontal integration like machine to machine, but also vertical integration like device to cloud. Now, most recently, OPC UA is being applied to those industries considered to be process control or hybrid industries with factory automation.
Rather than have every single I/O point controlled by one large distributed control system (DCS), MTP seeks to modularize the process into more manageable pieces. The point is to construct a plant with modular equipment to ease integration and allow for better flexibility should changes be required. With the help of a Process Orchestration Layer (POL), MTP-enabled equipment can “Plug & Operate” reducing the amount of time to commission a process or make changes to that process… pretty cutting-edge stuff.
The POL is the superordinate software into which an MTP file is imported. When an MTP file is imported into the POL, offline service engineering (orchestration) is performed along with communication configuration (OPC UA). Note: if recipe/batch engineering is applicable, MTP utilizes the ISA 88 standard here. The next step is an Orchestration Test (“Plug”) and then to begin (“Operate”). It is truly “Plug & Operate”.
Why should you care?
MTP files describe Equipment Assemblies. These are individual automated units providing the functionality to realize a step in a process. They have their own mechanical equipment, sensors, actuators, and controller. A great example would be skid integration. Here, an end-user can quickly integrate skids into their plant DCS to reduce engineering effort. The MTP file describing the skid is employed to shorten the time-to-market. According to ZVEI the benefits from first pilot projects can be summarized as follows:
