October 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.
✔️ 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
September 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.
✔️ 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
September 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
✔️ 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
July 3, 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
June 2, 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
April 6, 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
March 15, 2021
While it may be convenient to follow simple steps to get connectivity working for your IIoT solution, sometimes you are better off having an understanding of the elements that make up the broad spectrum of connectivity technologies.
To understand the foundations upon which IoT protocols are built, I had a conversation with Dominik Obermaier. Dominik is the Co-Founder & CTO of HiveMQ, a company that provides an MQTT broker and a client-based messaging platform to over 130 customers including many Fortune 500 companies for mission-critical use cases like connected cars, logistics, Industry 4.0, and connected #IoT products.
Dominik is also a member of the OASIS Technical Committee responsible for developing the MQTT specification, and he's also involved in the standardisation of Sparkplug.
Here's the outline of the discussion linked below:
✔️ IoT Connectivity Architectures
✔️ Data Encoding Mechanisms
✔️ COAP Protocol
✔️ AMQP Protocol
✔️ XMPP protocol
✔️ Fundamentals of MQTT Protocol
✔️ Plug and Play Interoperability Using Sparkplug B
✔️ Adoption of Sparkplug B in Manufacturing
✔️ #MQTT Broker Deployment Options
✔️ Kubernetes for High Availability MQTT Broker Deployments
✔️ Backend IoT Architecture for MQTT
✔️ #IIoT Security
✔️ MQTT Use Case in Smart Manufacturing
March 8, 2021
Developed at LinkedIn in 2010, Apache Kafka - a stream processing engine, now powers web-scale Internet companies such as Netflix, Uber, Twitter, Airbnb, and more. Profoundly impacting real-time user experience.
Of equal impact, is its application in creating a continuous streaming pipeline of manufacturing data, from the factory-floor to data centers. Fundamentally changing the structural organisation of manufacturing systems.
To gain an understanding of Kafka in IIoT, I had a conversation with Kai Waehner. Kai is Field CTO and Global Technology Advisor at Confluent, a company that was founded by Kafka creators and is behind the open-source project.
Here are the contents of our discussion
✔️ Stream processing in Manufacturing
✔️ Apache Kafka and Its Role in IIoT
✔️ Kafka vs MQTT
✔️ Architecture Patterns for Kafka Deployments
✔️ Connectivity to Industrial Control Systems
✔️ Data Ingestion to enterprise Applications
✔️ Examples of Real-Time Streaming Analytics
✔️ Using Kafka as a Data Historian
✔️ Re-Engineering ERP Suites with Kafka
✔️ Using Kafka to Drive Machine Learning
✔️ Hybrid Kafka Deployments
✔️ Kafka as a Platform for the Digital Twin
✔️ Kafka's Role in Augmented Reality
✔️ Kafka Use Cases in Manufacturing
March 8, 2021
So here's the thing, data from industrial sources is inherently messy. For example, a typical PLC system manages thousands of tags from both physical instruments and internal calculations, but this data is often unstructured, not linked to a unifying data model, and uses naming conventions that are vague to the outside world.
This makes data from such sources not readily usable in analytics applications and has led to the emergence of a new field of industrial data preprocessing for IIoT, called Industrial DataOps.
To discuss more on industrial DataOps, I had a conversation with John Harrington who is the Co-Founder of HighByte, a company pioneering this field with their HighByte Intelligence Hub.
Here some of the topics that we discussed.
✔️ What is Industrial IoT DataOps
✔️ Limitations of the Purdue Model/ISA-95
✔️ The importance of Data Quality
✔️ Data Standardisation, Normalisation and Contextualisation
✔️ Best Practices for integrating industrial data silos
✔️ Principles of IIoT Data Modelling
✔️ How DataOps enforces privacy and security
✔️ HighByte Intelligence Hub
John has previously worked as the VP of Business Strategy at PTC, and he's also served as the VP of Product Management at Kepware Technologies.