Smart App Embedded with Artificial Intelligence Improves Operations in Real-time

The popular press dithers over Artificial Intelligence and the “young ladies” Siri and Alexa spy on your every word. However, there are real, practical applications of AI that can help us operate and maintain our manufacturing and industrial operations. Here is one from AVEVA, a company that seems to have dominated my news this summer.

It has launched AVEVA Insight OMI app infusing real-time artificial intelligence into an operator’s decision-making. This app presents real-time anomaly detection in a context-aware OMI visualization display.

The AVEVA Insight OMI app introduces AI capabilities into the AVEVA System Platform, formerly Wonderware, and leverages predictive early warning and automatic detection of unusual operational behavior. This provides users with early notification so they can quickly resolve issues before they become critical business problems such as unplanned downtime and production losses.

A simple management interface enables operations, maintenance and production teams to quickly train the AI engine to adapt to the enterprise’s specific implementation. An intuitive thumbs-up or thumbs-down confirmation ensures AI-driven notifications are relevant to the needs of the user and support overall enterprise objectives, with no programming or data science knowledge required. This closed-loop feedback improves the accuracy of the AI prediction engine over time and enables users to see what matters. As anomalous patterns are identified, they can be captured and presented by the app within an organization’s on-premise HMI/SCADA solution, delivering insights directly where operators need it.

“IIoT applications have driven a massive increase in the collection of real-time operations and manufacturing data. As a result, operators face alarm overload and often cannot effectively react to or distinguish between process-critical situations and false positive alarm conditions, resulting in the loss of operational time and resources. By harnessing the power of AI and advanced cloud analytics, AVEVA is enabling operators to take proactive action, before process and maintenance problems occur,” commented Rashesh Mody, Vice President, Monitoring and Control, AVEVA.

“In today’s climate of increased demand for innovative technology solutions, the launch of our new AVEVA Insight OMI app is a significant development because it serves as a single interface into operations by bridging the information technology and operational technology divide for increased agility and situational awareness. We are very excited to introduce a solution that will help our customers manage critical operations and improve decision support for maximum profitability in these fast-changing times,” Mody concludes.

Data Remains Key Challenge for Artificial Intelligence Projects

70 Percent of Companies Report C-Suite Involvement into AI Projects, with COVID-19 Driving Acceleration of AI Strategies – But Businesses Still Name Data as Key Challenge.

“Artificial Intelligence is neither,” according to my favorite quip. This field of computer science is perhaps the most misunderstood thanks to possibility thinkers like Ray Kurzweil and the many dystopian movies. Nevertheless, AI in its realistic form powers much modern technology. And not only for home artificial listening devices. Business and industry use it often, as well.

Appen Limited, the leading provider of training data for organizations that build effective AI systems at scale, has announced its annual State of AI Report for 2020. The report highlights increasing C-suite involvement and investment in enterprise AI projects as well as data being a key challenge as AI models get more frequent updates in production. The report also reveals the recent acceleration of AI strategies in the wake of the COVID-19 pandemic.

According to the report, nearly 75 percent of businesses now consider AI critical to their success, and AI continues to grow in importance across companies of various sizes and industries. Yet, almost 50 percent of those who responded to the 2020 State of AI survey feel their company is behind on their AI journey, suggesting a critical gap exists between the strategic need and the ability to execute.

“Many organizations have adopted the use of the internet at the core of their processes, and AI is on a similar journey from fringes to core value offering. Increasing investment in AI projects and greater involvement by the C-suite, along with accelerating enterprise adoption in the wake of COVID-19, are clear indicators that AI is core to business success,” said Appen CEO Mark Brayan. “However, most companies are still in the early stages and facing challenges, especially around training data.”

Key Takeaways from the 2020 State of AI Report

The C-Suite is now far more heavily invested and involved in the development of AI projects

Executive visibility and involvement in AI have increased over 30 percent year-over-year, with 71 percent of organizations reporting C-suite involvement in AI projects. What’s more, the percentage of companies investing over $5 million has effectively doubled compared to last year. With this level of executive involvement and increased budgets, ethics, governance, and risk management initiatives have become important topics for technologists building AI.

COVID-19 is not slowing AI Investment

Continuing investment in AI shows that businesses are choosing to spend in times of turbulence. Two-thirds of companies do not expect any negative impact on their AI strategies. Nearly 50 percent of companies have accelerated their AI strategies, 20 percent doing so “significantly,” betting their AI projects will have a positive impact on their organization’s resiliency, efficiency, and innovation.

“COVID-19 has changed everything about the way companies are operating today, but not everyone has adapted in the same way,” added Wilson Pang, CTO at Appen. “The State of AI report shows despite turbulent times, more than two-thirds of respondents do not expect any negative impact from COVID-19 on their AI strategies. Those that are prioritizing AI see the power of digital transformations as a way to improve their resiliency and long-term performance.”

Data remains the key AI challenge

Training data is the key to successful AI, with 3 out of 4 companies updating their models at least quarterly. However, 40 percent of those updating quarterly feel that a lack of data or data management is a challenge.

“Many businesses are still early on their AI journey and they are finding that their data needs span beyond in-house resources when looking for high-quality, annotated training data that drives AI success,” added Pang. “Industry leaders are turning more and more to third-party providers like Appen to help them deploy their AI projects.”

AI Research For Tomorrow’s Production

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.

A Look At IoT Trends for 2020 and More

A Look At IoT Trends for 2020 and More

Top Tens and Top Twenties of the past or future year have never been my favorites. However, one can perceive trends and strain out little nuggets of gold by scanning several. Especially industrial taken broadly along with Internet of Things (IoT) and other current digital trends. I just had an interesting chat with Sean Riley, Global Director of Manufacturing and Transportation for Software AG, who released his Top Ten for 2020.

Following are his ideas interspersed with a few of my comments.

Cost Management Becomes Exceptional

As uncertainty enters the global manufacturing outlook, enterprises will become myopically focused on cost reductions. This will drive organizations to find more efficient methods of providing IT support, leveraging supplier ecosystems and simplifying value chains. [GM-much of my early work was in cost management/reduction; this is a never-ending challenge in manufacturing; however, tools continue to evolve giving us more and better solutions.]

A Blurred Line Between Products & Services

Manufacturers continue their product innovation quest and more manufacturers will begin focusing on how to deliver products as a service. The Manufacturers that have already created smart products and have elevated service levels will now begin to work out the financing considerations needed to shift from a sales based to a usage based revenue model. [GM-This is a trend most likely still in its infancy, or maybe toddler-hood; we see new examples sprouting monthly.]

Moving To Redefine Cost Models To Match Future Revenue Streams

Anticipating the shift to continual revenue streams, manufacturers will seek to shift costs to be incurred in a similar manner. This will be initially seen as a continued push to subscription based IT applications. While much progress has already been made, a larger focus will occur. [GM-I like his idea here of balancing capital versus expense budgets, continually finding the best fund source for shifting costs.]

IT Focuses on Rapid Support for Growth

The lines between business and IT users become blurred as no-code applications allow for business users to create integration services. IT professionals will leverage DevOps & Agile methodologies alongside of microservices and containers to rapidly develop applications that are able to generate incremental growth as requested by business users. This will be critical to the near term success for manufacturers, especially with economic headwinds that seem to be growing stronger. [GM-I didn’t ask about DevOps, but this idea is springing into the industrial space; cloud and software-as-a-service provide scalability both up and down for IT to balance costs and services.]

Industrial Self-Service Analytics Become Mission Critical

Industrie 4.0 / Smart Manufacturing initiatives continue to receive greater amounts of investment but in the near term, manufacturers will focus on unleashing the power of the data they already have. Historians, LIMS, CMMS’ have valuable data going to and in them and enabling production engineers to leverage that data rapidly is critical. Industrial Self-Service Analytics that allow production and maintenance professionals to leverage predictive analytics without IT assistance will sought as a powerful differentiating factor. [GM-we are beginning to see some cool no-programming tools to help managers get data access more quickly.]

Industrie 4.0 / Smart Manufacturing Initiatives Continue to Draw Investment

It’s no surprise that Manufacturers will continue to invest in Industrie 4.0 as the promises are great however, the scaled returns have not been realized and won’t be realized in the near term. The difficult of implementing these initiatives has surpassed manufacturers expectations for several reasons. First, traditional OT companies were trusted to deliver exceptional, open platforms and that wasn’t delivered. Secondly, collaboration efforts between IT & OT professionals proved to be more convoluted and difficult than expected. [GM-I’m thinking these ideas became overblown and complex, and that is not a good thing; to swallow the whole enchilada causes stomach pain.]

Artificial Intelligence Enters the Mix

AI won’t allow for users to sit back and relax while AI handles all of their tasks for them but it will make an appearance in back office tasks. Freight payment auditing, invoice payment and, in some select areas, chatbots will be the initial main stream uses of AI and will be seen as not becoming an anomaly but be understood to be more mainstream this year. [GM-I think still an idea looking for a problem; however some AI ideas are finding homes a little at a time.]

3D Printing Find New Uses

While this technology has steadily crept into production lines, the push towards usage based product pricing will have the technology move into after market services. Slow moving parts will be the first target for this technology which will help to free up much needed working capital to support financial transformation. [GM-watch for better machines holding tighter tolerances making the technology more useful.]

5G & Edge Analytics Enable New Possibilities

As Industrie 4.0 is continued to be pursued, Manufacturers will implement new initiatives that could not previously be realized without the high speed data transmission promises of 5G or the ability to conduct advanced analytics at the edge where production occurs. This will also provide manufacturers with new methods to securely implement Smart Manufacturing initiatives and in new locations that were not previously feasible due to connectivity issues. [GM-5G is still pretty much a dream, but there is great potential for some day.]

Security Still Remains a Critical Focus

With the increasing rate of IoT sensors, IT-OT convergence, the usage of API’s and the interconnectivity of ecosystems ensuring data security remains a top priority for manufacturers. As more data becomes more available, the need to increase levels of security becomes ever greater. [GM-ah, yes, security–a never-ending problem.]

A Look At IoT Trends for 2020 and More

Taking a Digital Journey

Keynoters have a tough time with originality these Digital Days with everyone emphasizing Digital Transformation. Steve Lomholt-Thomson, chief revenue officer of AVEVA, took us on a Digital Journey this morning. Setting the tone of the three days of AVEVA World Congress (North America edition).

Three technology trends to watch: an IoT boom; cloud/empowered edge; and, AI / ML. The theme is digital. The Digital Organization discovers its Digital DNA, figures out how to build that Digital DNA through people who challenge the status quo; and then figures out how to track talent flow.

Which all starts us on our Digital Journey. On this journey, we unify end-to-end data, connect data silos taking an wholistic view of the business, and then visualize our assets and supply chain. I believe implied in all this is the company’s product AVEVA System Platform. The company touted six customer stories with at least five of them (and probably the sixth) all leveraging System Platform.

Oh, and the only time the “W” word was used referred to past tense.

Other areas of the company were highlighted:

Focus on assets–asset performance management including how to use machine learning (ML) and artificial intelligence (AI) for predictive analytics (predictive maintenance.

How to combine it all into a Digital Twin–bringing the design lifecycle and physical lifecycle into congruence.

Recently hired head of North America business, Christine Harding, interviewed customers from Campbell’s (soup/snacks), Quantum Solutions (integration project at St. Louis/Lambert airport), and Suncor (Canadian oil sands).

I have the rest of today and then tomorrow to take deeper dives into many of these topics. If there is anything you want me to ask, send a note.