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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
Monday Jan 17, 2022
Monday Jan 17, 2022
Another year has come and gone, and still, almost every IIoT use case in manufacturing requires some sort of compute capability near the source of the data in order to solve some of the toughest challenges in Manufacturing Digital Transformation.
But yet, the currently dominant model for Industrial IoT is the Cloud-Based Platform-As-A-Service.
The issue is, while Edge Computing architectures do provide immense power and capabilities such as system resilience through delegation of computational workloads to autonomous IIoT devices in Distributed Edge Computing, it brings with it implementation complexity in manufacturing enterprises.
So, to provide you with practical guidance on Edge Computing, Architectures, and the building blocks necessary for an Edge Computing implementation in manufacturing, I invited Dominik Pilat, who is the Vice President of Customer Support & Field CTO at Hivecell, and John Kalfayan who is the Vice President of Energy, also at Hivecell.
Hivecell is a complete Edge-As-A-Service solution that allows companies to process vast amounts of raw data from smart machines and IoT Devices in real-time, at the Edge. It is both a hardware and software solution that supports the most widely used platforms today such as Kubernetes and Apache Kafka.
Outline
✔️ Key Drivers for Deployment of Compute Capabilities at the Industrial Edge
✔️ Industrial IoT Edge Computing Technology Stack
✔️ Characteristics of Distributed Edge Computing Model for IIoT
✔️ Management and Monitoring of Edge Deployed Software
✔️ Data Governance in Industrial Edge Computing
✔️ Apache Kafka Deployment at The Edge for IIoT
✔️ How Edge Compute Enables AI at the Industrial Edge
✔️ Hardware for Running AI Applications at the Edge
✔️ Practical Use Case of Industrial Edge Computing and AI
✔️ Hivecell Edge As A Services Solution
I wish you all a prosperous 2022.
Thursday Nov 04, 2021
Thursday Nov 04, 2021
The biggest challenge in the transition to Industry4.0 lies in the horizontal and vertical integration of information flow within and across manufacturing organisations, and the digitalisation of the engineering processes involved.
Among the technologies and standards developed to enable this flow of information, is the compelling combination of AutomationML, OPC UA, and the Asset Administration Shell.
To discuss this combination, I talked with Dr. Miriam Schleipen, the Chief Research Officer at EKS InTec GmbH where she deals with semantic interoperability in automation ecosystems based on Digital Twins and their application in automation environments.
Miriam is head of the joint working group of OPC foundation and AutomationML e.V., leads the German Glossary Industrie 4.0, and participates in national and international standardization groups dealing with semantic interoperability for Industrie 4.0.
Outline:
✔️ Introduction to AutomationML and its role in Industry4.0
✔️ Why and How AutomationML Integrates with OPC UA
✔️ Fundamentals of The Asset Administration Shell
✔️ Defining an Information Model Inside an Asset Administration Shell
✔️ Interrelation of the Asset Administration Shell and the AutomationML
✔️ Software Tools for Describing Models in AutomationML
✔️ Standardisation of the Asset Administration Shell
✔️ Benefits and Uses Cases of AutomationML, AAS, and OPC UA Combination
✔️ Best Practices for Implementing Asset Administration Shell Ecosystems
✔️ Role played by AutomationML, AAS, and OPC UA in Digital Twin Implementation
✔️ Distributed Digital Twins for Smart Manufacturing
✔️ AutomationML Association
✔️ EKS InTec GmbH
Friday Oct 29, 2021
Friday Oct 29, 2021
Perhaps the most common silos of information in Process Industries are the skilled workers who possess rare knowledge on how to optimise processes and maintain production equipment using traditional methods.
The good news is, the future of manufacturing is one where plant assets and operations have the capability to autonomously learn and self adapt in order to optimise processes with minimal human intervention, thereby freeing the skilled human resource for more value-added tasks. And that future has already begun!
To learn more about how Artificial Intelligence is currently being applied to solve problems in Process Industries, I invited Simon Rogers for a chat.
Simon is a Digital Transformation Consultant at Yokogawa in South Korea, where he helps Process Industries move from Industrial Automation to Industrial Autonomy by applying the latest digital technologies including Cloud Computing, IIoT, and Artificial Intelligence. He was previously the Vice-President of Digital Solutions at Yokogawa Headquarters in Japan, among many other previous roles at companies such as Honeywell, ABB, and KBC.
You can check out our full conversation on the video linked below.
Outline:
✔️ Importance of AI in Continuous Process Industries
✔️ Technologies Enabling Digital Transformation in the Process Industries
✔️ Benefits of Data-Driven Process Optimisation vs Traditional Methods
✔️ Using Natural language Processing for Industrial Data Management
✔️ Improving Safety and Reliability in Industrial Operations using Semantic AI
✔️ Applicability of Machine Learning in Process Simulation
✔️ Application of Digital Twins in the Process Industries
✔️ Common Use Cases of AI Application in the Process Industry
✔️ Current Challenges of AI Application in Process Industries
✔️ Future Potential of AI Application in Process Industries
✔️ The Shift from Industrial Automation to Industrial Autonomy
✔️ Yokogawa Electric Corporation - Process Automation and Digital Solutions
Wednesday Oct 20, 2021
Wednesday Oct 20, 2021
Based on the undeniable success of Advanced Data Analytics in Internet Companies, there's no doubt that manufacturers could reap massive benefits from adopting this "Data First' approach.
And for manufacturers, having this intelligent layer at the edge, closer to industrial data sources has proven to be more fitting and valuable.
To gain an understanding of the application of Edge Analytics for intelligent automation, I had a conversation with Martin Thunman. Martin is the CEO and Co-Founder of Crosser, a platform that was built with the realisation that low code edge analytics, automation, and integration software will play a critical role in accelerating the digital transformation journey of Industrial and asset-rich organizations.
You can check out the full conversation on the video linked below.
Outline:
✔️ Challenges in Industrial Legacy System integration with Industry4.0 technologies
✔️ Requirements for next-generation industrial system integration solutions
✔️ Edge Analytics and Opportunities it provides for Industrial Automation
✔️ Functional Composition of an Industrial Edge Analytics Solution
✔️ ISA95 vs Any-to-Any Hub Architecture
✔️ Data Modelling Best Practices for Edge Analytics Solution
✔️ Introduction to Edge Machine Learning Ops
✔️ Required Hardware and OS Capabilities for Edge Analytics
✔️ Crosser - Connectivity to Legacy Industrial Control Systems and Enterprise Applications
✔️ Crosser - Data Management and Orchestration
✔️ Common Uses Cases of Edge Analytics in Manufacturing
✔️ The future of Low Code Platforms in Industrial Automation
✔️ About Crosser
#iot #iiot #industry40 #EdgeAnalytics
Monday Oct 04, 2021
Tuesday Sep 21, 2021
Tuesday Sep 21, 2021
By nature, industrial facilities consist of physical assets and processes that evolve through time. Therefore, each data point generated by such systems is essentially a snapshot of events at that particular point in time.
By extension, this data wants to be stored in a way that reflects the sequential order of events, so that it can be rapidly queried and analysed, among many other reasons.
But yet, this isn't a capability that is inherently baked into the more common Relational and NoSQL databases. Hence the rise in popularity of Time-Series Databases for industrial Telemetry Data storage over the past few years.
At the forefront of this revolution is InfluxDB, an Open-Source Time-Series Database platform developed by InfluxData.
To understand how Time-Series Databases work, and InfluxDB in particular, I had a chat with Brian Gilmore who is the Product Manager for IoT at InfluxData.
Check out our full conversation in the video linked below.
Outline:
✔️ Characteristics of IIoT Data
✔️ Why Time-Series Databases Matter for IIoT
✔️ Common IIoT Use Cases for Time Series Database
✔️ How to Plan an IIoT Data Architecture
✔️ InfluxDB Time-Series DB Platform
✔️ InfluxDB - Open Source vs Cloud vs Enterprise
✔️ InfluxDB Time-Series DB Migration
✔️ InfluxDB Deployment Options
✔️ Acquiring Industrial Telemetry Data into InfluxDB
✔️ Industrial Telemetry Data Enrichment in InfluxDB
✔️ InfluxDB Integration with Analytics & Visualisation Platforms
✔️ Factory-Floor to InfluxDB Data Pipeline
Wednesday Sep 15, 2021
Wednesday Sep 15, 2021
The success of a fully realised Industry4.0 lies in the democratisation of intelligence and the capacity for Industrial "Things" to autonomously act based on the knowledge they have.
Effectively, turning each and every factory into a computer that is made up of modular processes within, in the form of Cyber-Physical systems.
And central to that success, is the ease with which Industrial things like pumps and sensors can be embedded with Machine Learning functionality.
To learn more about Embedded ML, I had a chat with Zin Thein Kyaw who is a Sr Success Engineer at Edge Impulse, a company on a mission to enable the ultimate development experience for machine learning on embedded devices for sensors, audio, and computer vision, at scale.
You can check out our conversation at the link below
Outline:
✔️ Integrating ML into industrial machines and sensors
✔️ Benefits of ML at the Edge of IIoT Network
✔️ Current applications of Embedded ML in industrial assets
✔️ Choosing an Embedded Processor for ML
✔️ Workflow for developing and deploying Embedded ML models
✔️ Integration of Edge Impulse with Tensorflow and Resource Optimisation
✔️ Industrial Data Collection and Data Availability
✔️ Application of Deep Learning in Industrial Systems
✔️ The Future of Embedded ML
✔️ The Edge Impulse Ecosystem & Developer Resources
Saturday Jul 03, 2021
Saturday Jul 03, 2021
As Industrial IoT matures, most of the components in the IIoT stack have become commoditised. Things like hardware, OSes, drivers, protocols, databases e.t.c
But yet, many organisations still develop custom interfaces for these components, instead of adopting standards. And in cases where there is adoption, there lacks an industry-wide consistent approach to standardisation.
To understand the importance of standardisation for IIoT and how vendors and end-users should engage standards, I had a conversation with Claude Baudoin.
Claude is the co-author of a recently published whitepaper on Global Industry Standards for IIoT by the Industrial Internet Consortium (IIC). He is the owner of cébé IT & Knowledge Management LLC, advisor to the OMG, IIC, and senior consultant at the Cutter Consortium.
Here's the outline of our conversation in the video linked below:
✔️ Importance of Standardisation for IIoT
✔️ Phases of a Standard Life Cycle
✔️ Standards Engagement Strategy
✔️ Identifying areas for standardisation
✔️ Adapting Open Standards to an Industrial Architecture
✔️ IIoT Connectivity Standards
✔️ Standards related to IIoT Security
✔️ Barriers to Agreeing on Standards and How to avoid building new silos
✔️ Building IIoT Solutions Vs Buying Off-The-Shelf Solutions
✔️ IIC in IIoT Standardisation
✔️ cébé
Wednesday Jun 02, 2021
Wednesday Jun 02, 2021
At the present moment, it is quite clear that the future of industrial automation will be driven by software. More so, that of IIoT.
And, due to the merits that have allowed it to dominate in the IT space, Open Source software is likely to lead the industrial software revolution. Regardless of the conservative nature of the industry.
To discuss the use of Open Source in building IIoT solutions, I had a conversation with Frédéric Desbiens.
Frédéric is the Program Manager for IoT and Edge Computing at the Eclipse Foundation, managing close to 50 Open Source projects under Eclipse IoT.
Here's the outline of the discussion.
✔️ Key Challenges for Implementing IIoT
✔️ Why Open Source Matters for IIoT Implementation
✔️ Key Components of an Industrial IoT Solution
✔️ Open Source Stack for IIoT Gateways
✔️ Open Standards for IIoT Data Aggregation
✔️ Why Semantic Interoperability Matters for IIoT
✔️ Real Value of MQTT Sparkplug to Implementers
✔️ Real Value of MQTT Sparkplug to End-Users
✔️ Is MQTT Sparkplug a Lock-In?
✔️ Open Source Digital Twin Frameworks and how they work
✔️ Open Source Software for IIoT Security
✔️ Role of Eclipse Foundation and Eclipse IoT
Tuesday Apr 06, 2021
Tuesday Apr 06, 2021
Nowadays, with so many IIoT concepts in the air, you can't help but breathe it in.
But sometimes it's helpful to take a step back and put all of this in context to understand how we got here, as that might help shed light on what IIoT is and isn't about.
To gain a fundamental understanding of OT-IT integration, I had a conversation with Benson Hougland.
Benson is VP of Product Strategy at Opto 22, a company that has been at the forefront of OT-IT integration for close to 30 years. From being a founding member of OPC to introducing the first Ethernet-based I/O Unit in the nineties, and more recently, introducing the first Edge Programmable Industrial Controller.
Below is an outline of our discussion in the linked video.
✔️ Why Should Manufacturers Care About IIoT?
✔️ Evolution of the IIoT Technology Stack
✔️ Open Technologies in IIoT
✔️ Principal Functions of an IIoT Edge Device
✔️ The Role of SCADA in an IIoT World
✔️ Brownfield and Greenfield Considerations for IIoT
✔️ Best Practices for IIoT Security
✔️ Critical Skills for IIoT System Integration
✔️ Integration of IIoT Solutions into Business Processes
✔️ Opto22
The MTConnect Standard offers such a semantic vocabulary for manufacturing equipment like Machine Tool Controllers, Robotic Arms, CNC Machines, etc. And more importantly, it integrates with other communication standards.
To understand how MTConnect works and its role in Industrial IoT, I had a conversation with Russell Waddell who is the Managing Director at the MTConnect Institute and is responsible for day-to-day business operations and standards development activity.
You can check out our conversation in the video linked below, and here's the outline:
✔️ Semantic Interoperability and its Benefits for IIoT
✔️ Introduction to MTConnect
✔️ Basics of MTConnect Information Model,
✔️ Components Required to Build an MTConnect System
✔️ Data Transportation Mechanism in MTConnect
✔️ Developer Support, Tools, and Frameworks for MTConnect
✔️ What Differentiates MTConnect from other communication standards
✔️ MTConnect Integration with OPC UA
✔️ MTConnect Use Cases
✔️ Security considerations in MTConnect
✔️ About The Association for Manufacturing Technology and MTConnect Institute