No less a thinker and scientist than Albert Einstein extolled the value of curiosity. A lifelong pursuit of learning is the mark of being human. This month kicks off what would have been conference season complete with airline and hotel reservations. However, we are now sitting quietly in our offices checking out new things. This week and next we have opportunities to learn about
How about what’s new with robotics and peripherals? I was a little late getting to this due to pressing matters. But you can pick up what you missed today and listen in the rest of the week to the RIA Robotics Week Virtual Conference and Trade Show.
How about machine technology and automation? Next week’s feature is the IMTS Network Streaming Sept 14-18.
In the midst of the IMTS week comes the Inductive Automation Ignition Community Conference
Sept 15. I am missing my annual trip to Folsom, California, although given the bad weather enhancing forest fires, perhaps this is a good year to miss. You can pick up all the action here.
While I scan a number of sources for news each day (never TV news, though), I have found a new source that I really like called Morning Brew. It’s a short read and gets you up to date quickly. Links for more in-depth reading if you care to.
A story from the 4th Century Desert Fathers. I thought of this after a post on LinkedIn from Rick Bullotta who had grown tired of all the negativity.
A certain brother came to Abbot Silvanus at Mount Sinai, and seeing the hermits at work, he exclaimed, “Why do you work for the bread that perishes? We read that Mary chose the better part – namely, to sit at the feet of her Lord.”
Then the abbot said to his disciple Zachary, “Give the brother a book, and put him in an empty cell, and let him read.” At the ninth hour the brother who was reading began to wonder why the abbot had not called him to eat. Sometime later he went directly to the abbot and said, “Did the brethren not eat today, father?”
“Oh yes,” said the abbot. “They have just finished their meal.” “Well,” said the brother, “Why did you not call me?” “Because you are a spiritual man,” answered the abbot. “You do not need the food that perishes. The rest of us have to work. But you have chosen the better part; you have read all day and can surely get along without food.”
I don’t know why. Perhaps it was an attack of acesis—the noonday demon. I sinned and browsed Twitter yesterday afternoon. Almost every post was some sort of opinion based on, well, nothing. The only thing I learned were the prejudices of a large number of people.
It is so easy to criticize. It is so hard to do.
There are actually two better ways. One is to study, learn, contemplate. But that must be balanced by going and doing. These are our spiritual practices.
Nowhere at no time did someone teach that the goal of life is to sit on our ever-expanding, er, posteriors and voice unfounded opinions.
Perhaps in this time of trial, we can tell stories of people doing good. There are people all around the world working to serve the rest of us–health care workers, people who work in groceries (we gotta eat), people in manufacturing and production keeping the lights on and gas flowing and replenishing necessary supplies. There are tons of people being creative in their time away from busy-ness.
While at the Hannover Messe Preview last week in Germany, I talked with the representatives of a German consortium with the interesting name of “it’s OWL”. Following are some thoughts from the various organizations that compose the consortium.
Intelligent production and new business models
Artificial Intelligence is of crucial importance for the competitiveness of industry. In the Leading-Edge Cluster it’s OWL six research institutes cooperate with more than 100 companies to develop practical solutions for small and medium-sized businesses. At the OWL joint stand (Hall 7, A12) over 40 exhibitors will demonstrate applications in the areas of machine diagnostics, predictive maintenance, process optimization, and robotics.
Prof. Dr. Roman Dumitrescu (Managing Director it’s OWL Clustermanagement GmbH and Director Fraunhofer IEM) explains: “Our research institutes are international leaders in the fields of machine learning, cognitive assistance systems and systems engineering. At our four universities and two Fraunhofer Institutes, 350 researchers are working on over 100 projects to make Artificial Intelligence usable for applications in industrial value creation. With it’s OWL, we bring this expert knowledge into practice. In 2020, we will launch three new strategic initiatives worth 50 million € to unlock the potential for AI in production, product development and the working world for small and medium-sized enterprises.”
In the initiative ‘AI Marketplace’ 20, research institutes and companies are developing a digital platform for Artificial Intelligence in product development. Providers, users, and experts can network and develop solutions on this platform. In the competence centre ‘AI in the working world of industrial SMEs’, 25 partners from industry and science make their knowledge of work structuring in the context of AI available to companies.
Learning machine diagnostics and ‘SmartBox’ for process optimization
The Institute for Industrial Information Technology at the OWL University of Applied Sciences and Arts will present new results for intelligent machine diagnostics at the trade fair. Using a three-phase motor, it will be illustrated how learning algorithms and information fusion can be used to reliably identify, predict, and visualize states of technical systems. Patterns and information hidden in time series signals are learned and presented to the user in an understandable way. Inaccuracies and uncertainties in individual sensors are solved by conflict-reducing information fusion. For example, motors can be used as sensors. Within a network of sensors and other data sources in production plants, motors can measure the “state of health” and analyze the causes of malfunctions via AI. This reduces scrap and saves up to 20 percent in materials.
The ‘SmartBox’ of the Fraunhofer Institute IOSB-INA is a universally applicable solution that identifies anomalies in processes in various production environments on the basis of PROFI-NET data. The solution requires no configuration and learns the process behavior.
With retrofitting solutions of the Fraunhofer Institute, companies can prepare machines and systems in their inventory for Industrie 4.0 applications without major investment expenditure. The spectrum ranges from mobile production data acquisition systems in suitcase format for studies of potential to permanently installable retrofit solutions. Intelligent sensor systems, cloud connections and machine learning methods build the basis for data analysis. This way, processes can be optimised and more transparency, control, planning, safety, and flexibility in production can be achieved.
Cognitive robotics and self-healing in autonomous systems
The Institute of Cognition and Robotics (CoR-Lab) presents a cognitive robotics system for highly flexible industrial production. The potential of model-driven software and system development for cognitive robotics is demonstrated by using the example of automated terminal assembly in switch cabinet construction. For this purpose, machine learning methods for environ- mental perception and object recognition, automated planning algorithms and model-based motion control are integrated into a robotic system. The cell operator is thereby enabled to perform different assembly tasks using reusable and combinable task blocks.
The research project “AI for Autonomous Systems” of the Software Innovation Campus Paderborn aims at achieving self-healing properties of autonomous technical systems based on the principles of natural immune systems. For this purpose, anomalies must be detected at runtime and the underlying causes must be independently diagnosed. Based on the localization it is necessary to plan and implement behavioral adjustments to restore the function. In addition, the security of the systems must be guaranteed at all times and system reliability must be increased. This requires a combination of methods of artificial intelligence, machine learning and biologically inspired algorithms.
Predictive maintenance and digital twin
Within the framework of the ‘BOOST 4.0’ project, the largest European initiative for Big Data in industry, it’s OWL is working with 50 partners from 16 countries on various application scenarios for Big Data in production. it’s OWL focuses on predictive maintenance: thanks to the systematic collection and evaluation of machine data from a hydraulic press and a material conveyor system, it is possible to identify patterns in the production process in a pilot company. The Fraunhofer IEM has provided the technological and methodological basis. And successfully so: over the past two years the prediction of machine failures has been significantly improved in this specific application by means of machine learning methods. The Mean Time To Repair (MTTR) has already been reduced by more than 30 percent. The Mean Time Between Failures (MTBF) is now six times longer than before. A model of the predictive production line can be seen at the stand.
The digital twin is an important prerequisite for increasing the potential for efficiency and productivity in all phases of the machine life cycle. Companies and research institutes are working on the technical infrastructure for digital twins in an it’s OWL project. Digital descriptions and sub-models of machines, products and equipment as well as their interaction over the entire life cycle are now accessible thanks to interoperability. Requirements from the fields of energy and production technology as well as existing Industrie 4.0 standards and IT systems are taken into account. This is expected to result in potential savings of over 50 percent. At the joint stand, Lenze and Phoenix Contact will use typical machine modules to demonstrate how digital twins can be used to exchange information between components, machines, visualisations and digital services across manufacturers. Interoperability proves for the first time how the combination of data can be used to create useful information with added value for different user groups. For example, machine operators and maintenance staff can detect anomalies and receive instructions for troubleshooting.
Connect and get started – production optimization made easy
The cooperation in the Leading-Edge Cluster gives rise to new business ideas that are developed into successful start-ups. For example, Prodaso—a spin-off from Bielefeld University of Applied Sciences—has developed a simple and quickly implementable solution for the acquisition and visualization of machine and production data. The hardware can be connected to a machine in a few minutes via plug-and-play. The machine data is displayed directly in the cloud.
Prodaso has succeeded in solving a central challenge: Until now, networking machines from different manufacturers have been complex and costly. The Prodaso system can be retrofitted to all existing systems, independent of manufacturer and interface. In addition, the start- up also provides automated analysis and optimization tools. This enables companies to detect irregularities and deviations in the process flow at an early stage and to initiate appropriate measures. The company, founded in 2019, has already connected approximately 100 machines at companies in the manufacturing industry.
Many engineers and programmers like open source projects combined with open APIs. Some open source catches on and quietly becomes widely used. Others languish. The Linux Foundation’s Edge project, especially EdgeX Foundry, keeps quietly growing. What are the odds that this becomes a widely used Internet of Things tool?
Today’s news in brief:
- EdgeX’s fifth release offers more scalable solutions to move data from devices to cloud, enterprise and on-premises applications
- The first LF Edge project to achieve Stage 3 ratification, EdgeX hits widespread adoption and production-level maturity
- EdgeX and LF Edge onsite at IoT Solutions World Congress with demos from Dell Technologies, Home Edge, IOTech and Project EVE
EdgeX Foundry, a project under the LF Edge umbrella organization within the Linux Foundation that aims to establish an open, interoperable framework for IoT edge computing independent of connectivity protocol, hardware, operating system, applications or cloud, announced the availability of its “Fuji” release. This release offers additional security and testing features on top of the production-ready “Edinburgh” release launched this spring.
“EdgeX Foundry has experienced significant momentum in developing an open IoT platform for edge-related applications and shows no signs of slowing down,” said Arpit Joshipura, general manager, Networking, Edge and IoT, the Linux Foundation. “As the only Stage 3 project under LF Edge, EdgeX Foundry is a clear example of how open collaboration is the key to an active community dedicated to creating an interoperable open source framework across IoT, Enterprise, Cloud and Telco Edge.”
Launched in April 2017, and now part of the LF Edge umbrella, EdgeX Foundry is an open source, loosely-coupled microservices framework that provides the choice to plug and play from a growing ecosystem of available third-party offerings or to augment proprietary innovations. With a focus on the IoT Edge, EdgeX simplifies the process to design, develop and deploy solutions across industrial, enterprise, and consumer applications. As a Stage 3 project under LF Edge, EdgeX is a self-sustaining cycle of development, maintenance, and long-term support. As an example of the rapidly accelerating use of the code, EdgeX hit a milestone of 1 million platform container downloads, which almost half of these took place in the last few months.
“The 1M container download isn’t our only milestone,” said Keith Steele, EdgeX Foundry chair of the Technical Steering Committee and LF Edge Governing Board member. “The development team has expanded with more than 150 active contributors globally and the partner ecosystem of complementary products and services continues to increase. As a result, we’re seeing more end-user case studies that range from energy and utilities, building automation, industrial process control and factory automation, smart cities, retail stores and distribution and health monitoring.”
The Fuji Release
As the fifth release in the EdgeX Foundry roadmap, Fuji offers significant enhancements to the Edinburgh 1.0 release, which launched in July, including:
- New and improved security features to include PKI infrastructure for token/key generation.
- Application services that now offer full replacement capability to the older export services provided with previous EdgeX releases. These application services offer more scalable and easier to use solutions to get data from the EdgeX framework to cloud, enterprise and on-premises applications.
- Example application services are provided with this release to allow users to quickly move data from EdgeX to the Azure and AWS IoT platforms.
- A new applications function Software Development Kit (SDK) also provides the EdgeX user community with the ability to create new and customized solutions on top of EdgeX – for example, allowing EdgeX to move edge data to legacy and non-standard environments.
- Unit test coverage is considerably increased (in some services by more than 200 percent) across EdgeX core and supporting microservices.
- New device service connectors to BLE, BACNet, IP camera, OPC UA, GPS, and REST device services.
- Choices for commercially-supported EdgeX device connectors are also starting to blossom with offerings for CANopen, PROFINET, Zigbee, and EtherCat available through EdgeX community members.
Inaugural EdgeX Open
The EdgeX Foundry community recently kicked off a series of hackathons, titled the EdgeX Open. More than 70 attendees participated in the first event on October 7- 8, 2019, in Chicago. Hosted by LF Edge and the Retail Industry Leader Association (RILA), and sponsored by Canonical, Dell Technologies, Deep Vision, Intel, IOTech, IoTium and Zededa, the event featured five teams that competed in retail use case categories. More details on the event, including the winning use case from Volteo, are available in this blog post.
The next hackathon will coincide with the Geneva release, targeted for Spring 2020. It will be centered on the Manufacturing vertical and held in a location in Europe.
This is still more followup from Emerson Global Users Exchange relative to sessions on Projects Pilot Purgatory. I thought I had already written this, but just discovered it languishing in my drafts folder. While in Nashville, I ran into Jonas Berge, senior director, applied technology for Plantweb at Emerson Automation. He has been a source for technology updates for years. We followed up a brief conversation with a flurry of emails where he updated me on some presentations.
One important topic centered on IoT projects—actually applicable to other types of projects as well. He told me the secret sauce is to start small. “A World Economic Forum white paper on the fourth industrial revolution in collaboration with McKinsey suggests that to avoid getting stuck in prolonged “pilot purgatory” plants shall start small with multiple projects – just like we spoke about at EGUE and just like Denka and Chevron Oronite and others have done,” he told me.
“I personally believe the problem is when plants get advice to take a ‘big bang’ approach starting by spending years and millions on an additional ‘single software platform’ or data lake and hiring a data science team even before the first use case is tackled,” said Berge. “My blog post explains this approach to avoiding pilot purgatory in greater detail.”
I recommend visiting Berge’s blog for more detail, but I’ll provide some teaser ideas here.
First he recommends
- Think Big
- Start Small
- Scale Fast
Plants must scale digital transformation across the entire site to fully enjoy the safety benefits like fewer incidents, faster incident response time, reduced instances of non-compliance, as well as reliability benefits such as greater availability, reduced maintenance cost, extend equipment life, greater integrity (fewer instances of loss of containment), shorter turnarounds, and longer between turnarounds. The same holds true for energy benefits like lower energy consumption, cost, and reduced emissions and carbon footprint, as well as production benefits like reduced off-spec product (higher quality/yield), greater throughput, greater flexibility (feedstock use, and products/grades), reduced operations cost, and shorter lead-time.
The organization can only absorb so much change at any one time. If too many changes are introduced in one go, the digitalization will stall:
- Too many technologies at once
- Too many data aggregation layers
- Too many custom applications
- Too many new roles
- Too many vendors
Multiple Phased Projects
McKinsey research shows plants successfully scaling digital transformation instead run smaller digitalization projects; multiple small projects across the functional areas. This matches what I have personally seen in projects I have worked on.
From what I can tell it is plants that attempt a big bang approach with many digital technologies at once that struggle to scale. There are forces that encourage companies to try to achieve sweeping changes to go digital, which can lead to counterproductive overreaching.
The Boston Consulting Group (BCG) suggests a disciplined phased approach rather than attempting to boil the ocean. I have seen plants focus on a technology that can digitally transform and help multiple functional areas with common infrastructure. A good example is wireless sensor networks. Deploying wireless sensor networks in turn enables many small projects that help many departments digitally transform the way they work. The infrastructure for one technology can be deployed relatively quickly after which many small projects are executed in phases.
Small projects are low-risk. A small trial of a solution in one plant unit finishes fast. After a quick success, then scale it to the full plant area, and then scale to the entire plant. Then the team can move on to start the next pilot project. This way plants move from PoC to full-scale plant-wide implementation at speed. For large organization with multiple plants, innovations often emerge at an individual plant, then gets replicated at other sites, rolled out nation-wide and globally.
Use Existing Platform
I have also seen big bang approach where plant pours a lot of money and resources into an additional “single software platform” layer for data aggregation before the first use-case even gets started. This new data aggregation platform layer is meant to be added above the ERP with the intention to collect data from the ERP and plant historian before making it available to analytics through proprietary API requiring custom programming.
Instead, successful plants start small projects using the existing data aggregation platform; the plant historian. The historian can be scaled with additional tags as needed. This way a project can be implemented within two weeks, with the pilot running an additional three months, at low-risk.
I personally like to add you must also think of the bigger vision. A plant cannot run multiple small projects in isolation resulting in siloed solutions. Plants successful with digital transformation early on establish a vision of what the end goal looks like. Based on this they can select the technologies and architecture to build the infrastructure that supports this end goal.
NAMUR Open Architecture (NOA)
The system architecture for the digital operational infrastructure (DOI) is important. The wrong architecture leads to delays and inability to scale. NAMUR (User Association of Automation Technology in Process Industries) has defined the NAMUR Open Architecture (NOA) to enable Industry 4.0. I have found that plants that have deployed digital operational infrastructure (DOI) modelled on the same principles as NOA are able to pilot and scale very fast. Flying StartThe I&C department in plants can accelerate digital transformation to achieve operational excellence and top quartile performance by remembering Think Big, Start Small, Scale Fast. These translate into a few simple design principles:
- Phased approach
- Architecture modeled on the NAMUR Open Architecture
- Ready-made apps
- East-to-use software
- Digital ecosystem