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Each episode of Industry40.tv Podcast will treat you to an in-depth interview with leading AI practitioners, exploring the Application of Artificial Intelligence in Manufacturing and offering practical guidance for successful implementation.
Episodes
![Ep 31 Architecting IIoT Solutions Using Unified Namespace - [ David Schultz, G5 Consulting ]](https://pbcdn1.podbean.com/imglogo/image-logo/9018806/podcast_cover1400x1400_2xi99v_300x300.png){kind=logo}
Monday Dec 12, 2022
Monday Dec 12, 2022
The Unified Namespace has countless advantages over traditional architectural approaches when it comes to IIoT implementation in manufacturing.
Some of these are:
👉 More efficient communication and data sharing between different devices and systems.
👉 Improved scalability due to a unified interface that is consistent across the entire enterprise network.
But how do you go actually go about building a Unified Namespace Architecture?
To gain an understanding of this, I invited David Schultz for a podcast session.
David is the President of G5 Consulting where he works with manufacturers to help them develop and execute strategies for their digital transformation and asset management initiatives.
Below is the outline of our conversation.
✅ What is The Unified Namespace?
✅ MQTT/Sparkplug for Unified Namespace
✅ Tools and Strategies for Building the Unified Namespace
✅ Data Normalisation and Contextualisation Techniques for UNS
✅ Role and Significance of ISA 95 Schema in UNS
✅ Using Multiple MQTT Brokers for a Unified namespace
✅ Role played by Historian, MES, and ERP in UNS Architecture
✅ Use Cases and Technologies for Data lake Integration with UNS
✅ Approaches for Extending UNS with transactional Capabilities
Sunday Oct 23, 2022
Sunday Oct 23, 2022
When you look around a factory, you are likely to see objects interacting with other objects. And there are specific things that each object can do.
It, therefore, makes sense that, in order to build autonomously reconfiguring factories, each object needs to be able to describe its capabilities to other objects so that they can interact with no human intervention.
OPC UA Information Modelling is an effort to enable the description of complex industrial systems through a standardised and object-oriented interface.
To gain a deeper understanding of how it works, I invited Jouni Aro for a podcast conversation.
Jouni is the Chief Technology Officer at Prosys OPC Ltd, a leading provider of OPC and OPC UA technology with over 20 years of experience in the field. He's been the main architect for Prosys OPC UA SDKs and is an active member of the Technical Advisory Council and several working groups of the OPC Foundation.
Below is the outline of our conversation
✅ What is OPC UA Information Modelling
✅ Benefits of Information Modelling for Industry 4.0
✅ OPC UA Unified Object Model for Description of Complex Systems
✅ Tools for Building and Managing OPC UA information models
✅ OPC UA Companion Specifications, Custom Information Models
✅ OPC Cloud Library, Online Information Model Repository
✅ Workflow for integrating OPC UA Information Model into Products
✅ OPC UA Information Modelling in Industrial System Integration
✅ OPC UA Information Modelling and ISA95
✅ OPC UA Information Modelling for Vertical Integration
✅ OPC UA Information Modelling for Horizontal Integration
✅ Is OPC UA Information Modelling Future-Proof?
✅ Information Modelling for OPC UA PubSub Over MQTT
Thursday Oct 13, 2022
Thursday Oct 13, 2022
Gaining competitive advantage is the main driver of innovation in nature as much as it is in technology. And the manufacturing ecosystem is no different.
In manufacturing, this manifests in the deployment of production automation systems on the shop floor, and enterprise planning systems on the top floor.
But yet, there's a grey area in between that has, for the most part, remained underutilised or wrongly implemented altogether.
This is where a Manufacturing Execution System (MES) would live.
To understand how manufacturers can gain a competitive advantage by leveraging a Modern MES Architecture for data-driven manufacturing, I invited Kevin Jones for a podcast conversation.
Kevin is the CEO and Lead Strategist at Ectobox, Inc., a Manufacturing Intelligence Solutions company and
Industry 4.0 systems integrator based in Pittsburgh, PA.
Below is the outline of our conversation
✅ Drivers for Data-Driven Manufacturing
✅ Introducing MES for Manufacturing
✅ Functions of Manufacturing Execution Systems
✅ Selecting and Implementing an MES System
✅ Modern Manufacturing Execution System Architecture
✅ Problems with Current Manufacturing Data Systems
✅ Unified Namespace for Manufacturing Execution System
✅ Brownfield Integration into Unified Namespace Architecture
✅ The Future of MES in Manufacturing
Thursday Oct 06, 2022
Thursday Oct 06, 2022
I invited Maciek Wasiak for a podcast conversation on Predictive Analytics in Manufacturing and he delivered a masterclass.
Maciek is the CEO and Founder of Xpanse AI, a company that develops technology that rapidly accelerates Data Science delivery by replacing manual data science with AI-driven processing
Here's the outline of our conversation:
✅ Xpanse AI
✅ Introduction to Predictive Analytics
✅ Real-World Data Science Use Cases in Manufacturing
✅ Semiconductor Fabrication Predictive Analytics Solution Demo
✅ Traditional vs Automated Predictive Analytics
✅ Predictive Analytics Workflow Based on AI and ML
✅ Identifying and qualifying plant-floor data sources for Predictive Analytics
✅ Managing plant-floor data variety for Predictive Modelling
✅ Predictive Modelling Techniques
✅ Meeting plant-floor real-time requirements with ML Processes
✅ Role played by domain-level expertise in Predictive Analytics
✅ Role Played by Industrial System Integrators in Predictive Analytics implementation
✅ Working with AI and ML platforms for non data scientists
Thursday Sep 29, 2022
Thursday Sep 29, 2022
Whenever you're faced with information overload on any subject matter, as is the case with many manufacturers considering Smart Manufacturing, it's important to take a step back and understand its first principles.
Without which it would be difficult and costly to realise the vision of Smart Manufacturing.
To help bring the First Principles of Smart Manufacturing to light, I invited Conrad Leiva for a podcast conversation. Conrad is the VP of Ecosystem and Workforce Development at the Clean Energy Smart Manufacturing Innovation Institute, CESMII.
CESMII is a US Government funded institute with a mission to democratise Smart Manufacturing and make its methodologies and technologies more affordable and accessible to SME manufacturers.
Here's the outline of our conversation
✅ Clean Energy Smart Manufacturing Innovation Institute (CESMII)
✅ A Brief History of Smart Manufacturing
✅ Smart Manufacturing Definition and Description of Key Terms
✅ The Seven Principles of Smart Manufacturing
✅ Flat and Real-Time Principle
✅ Scalability Principle, Enabled by Cloud and Edge
✅ Interoperable and Open Principle, Information Modelling
✅ The Role of ISA 95 in Smart Manufacturing
✅ Proactive and Semi-Autonomous Principle
✅ Sustainable and Energy Efficient
Thursday Sep 22, 2022
Thursday Sep 22, 2022
More than anything else, the foundational power of personnel in fieldwork merely lies in the fact that we can see.
By extension, it makes sense that the biggest impact on industrial digital transformation will come from embedding vision in intelligent connected components.
More so when embedded vision and AI software are widely deployed in mobile and battery-powered field equipment.
To learn more about building this capability into industrial products, I invited Taylor Cooper for a chat on the podcast.
Taylor is the CEO and Principal Engineer at MistyWest where he's recently led the company in developing an Embedded Vision System on Module (MistySOM), based on the Renesas RZ/V2L processor, that enables the embedding of vision-based AI capabilities in field equipment.
Below is the outline of our conversation.
✅ Misty West, Embedded Vision & IIoT
✅ Latest Trends in Industrial IoT, and Chip Shortage
✅ Google IoT Core Retirement, IoT Boom and Bust
✅ MQTT in IIoT and Computer Vision
✅ Delivering AI Capabilities for IIoT with Renesas RZ/V2L Based System on Module
✅ Potential Applications of Low Power System on Module in Connected Intelligence,
✅ Workflow for developing Embedded Vision for Connected Products
✅ AI versus Rules-Based Image Processing in Embedded vision
✅ Selecting Embedded Vision Middleware
✅ Selecting wireless connectivity for Embedded Vision Applications
Thursday Sep 22, 2022
Thursday Sep 22, 2022
Here's the thing. Containerisation is not only an IT technology, it is an advanced IT technology. And yet, it already looms on the horizon for Operations Technology.
And, while the technology opens up massive opportunities for optimisation and efficiency in the OT network, it demands a fundamental rethink of industrial software distribution and management.
To find out what this actually means for vendors, engineers, and system integrators in the industrial space, I invited Neil Cresswell for a conversation.
Neil is the CEO and Co-Founder of a company called Portainer.io, the most popular Docker Container Management platform that abstracts the complexities of container management with a feature-rich and easy-to-use Graphical User Interface.
Below is the outline of our conversation.
✅ What are containers?
✅ Benefits of containerisation at the Industrial Edge
✅ Challenges of adopting containers for Industrial Edge Compute
✅ Key differences between containerisation at the edge and in the cloud
✅ Containerization Approaches and Microservices for IIoT
✅ What is Portainer and How it Works
✅ Use Cases in Industrial IoT
✅ Portainer Products
✅ Reference Architectures for Managing software in OT
✅ Best Practices Containerisation at the Industrial Edge
✅ How a Containerised PLC functions
✅ Impact of Containerisation on Industrial System Integration
✅ The Future of Industrial Software Distribution
Thursday Sep 08, 2022
Thursday Sep 08, 2022
While there may be disagreements on what data modeling, encoding, and transportation technologies to adopt for Industrial IoT, there's one aspect that's evidently being agreed upon across the board.
The fact that Publish-Subscribe architectures are, by far, more suitable for this new world of hyper-connectivity.
One implementation of such an architecture for IIoT is OPC UA PubSub, which defines a mapping of Binary and JSON encoding over an MQTT, AMQP, and UDP-based PubSub network.
To understand how OPC UA PubSub can be applied in Real-World Industrial scenarios, I had a conversation with Praveen Kumar Singh, who is the Chief OPC Solutions Architect at Utthunga, a Product Engineering and Industrial Solutions company that engineers industrial-grade digital products and solutions for industrial OEMs, Industries, ISVs, and Service Providers.
Below is the outline of our conversation.
✅ What is OPC UA PubSub?
✅ Real World Applications of OPC UA PubSub
✅ Embedded OPC UA PubSub Applications
✅ Configuration Mechanism of OPC UA PubSub Components
✅ How Discovery works in OPC UA PubSub networks
✅ How Information Modelling Works in OPC UA PubSub
✅ Consuming OPC UA PubSub using Third Party Applications
✅ OPC UA PubSub over TSN explained
✅ Use Cases for OPC UA Client-Server combined with PubSub
✅ Sensor to Cloud Using OPC UA PubSub
✅ Security mechanisms in OPC UA PubSub communication
✅ uOPC PubSub Bridge overview
✅ About Utthunga
Sunday Sep 04, 2022
Sunday Sep 04, 2022
Advanced as they may be, modern analytics systems fall short of enabling the complete digital transformation of manufacturing enterprises.
For example, instead of only detecting symptoms of impending machine failure, what would be more valuable would be to determine the actual cause of failure.
Causal Machine Learning, a recent advance in ML holds the problem to solve this problem.
To understand how it can be applied in Digital Twins to enable complete digital transformation for manufacturers, I had a conversation with Dr. PG Madhavan.
PG has deep expertise in Data Science and extensive experience in advanced analytics development, both in industry and academia.
Below is the outline of our conversation:
✅ Enthusiasm about Digital Twins Today
✅ Why Predictive Maintenance is not the Killer App for IIoT
✅ What is the central purpose of a Digital Twin?
✅ Challenges in Integrating Digital Technologies for DT Realisation
✅ Role of Industrial IoT in Digital Twins
✅ Machine Learning Methods in Digital Twins
✅ Application of Root Cause Analytics Method in DTs
✅ Application of Causality in Industrial IoT Data
✅ Key Steps to Digital Transformation in Manufacturing
✅ Manufacturing Digital Transformation through Digital Twins
✅ PyWhy, an open-source repository of AWS & Microsoft joint work in Causality for machine learning.
✅ Systems Analytics Solutions
Thursday Aug 25, 2022
Thursday Aug 25, 2022
Done right, the digital transformation of manufacturing enterprises has less to do with plugging in smart objects to collect data from the factory floor.
Rather, it has a lot to do with laying down a reliable, secure, and scalable data processing infrastructure in such a manner that it allows you to automate your entire manufacturing business process.
And for engineers and architects tasked with building IIoT solutions, it involves picking the right tools for each part of your data processing pipeline, from the edge of the network to systems at the highest level of your enterprise.
To understand why and how to achieve that using open source tools, I had a conversation with Jeremy Theocharis who is the Co-Founder and CTO of United Manufacturing Hub, a company centered around an Open Source Project that combines state-of-the-art IT/OT tools to help engineers build Industrial IoT solutions.
Here's the outline of our conversation which you can watch by clicking on the link below:
Outline:
Challenges in IT/OT Integration Introduction to United Manufacturing Hub (UMH) Why Open Source Matters Criteria for picking the core technologies for the UMH stack. Modernising Industrial Systems Architecture Using Microservices Why MQTT and Kafka for the IIoT Data Pipeline The role played by OPC UA in the UMH stack Unified Namespace Architectural Approach Factors influencing the design of the UMH DataModel Multisite Data Integration Using UMH Machine Vision Use Cases on the UMH Platform Historian vs Open-Source databases