that will impact New IoT Technology Trends Production Automation

Transcription

1 New IoT Technology Trends that will impact Production Automation Alexander Körner, Software Solution Architect Watson IoT Electronics Industry Lab, Munich IBM Deutschland 19. Juni 2018

2 Your key Take-Aways out of my session today (*)... There are industrial IoT challenges in Production Automation......which can be addressed with technologies like edge analytics and mesh computing AI will move to the Production Edge with a supporting Cloud......while slightly moving away from a centralistic cloud approach Mesh Computing allows to keep sensitive Production Data on-site......while enhancing AI on the edge and providing additional benefits (*)...if you can only stay a minute or so

3 Where I work and with whom I work with...

4 The IoT is driving disruption of the physical world Accelerating advances in technology Are transforming every part of business Cognitive Analytics Cloud Computing Pervasive Connectivity Product Lifecycle Management Embedded sensors Boosting operational performance and lowering costs Driving engagement and customer experience Creating new products and business models

5 Today: A typical Industrial IoT / Industrie 4.0 Scenario Industrial Shop Floor Sensors Cloud Raw Sensor Data via industry specific automation protocols (e.g. PROFINET, BACnet, OPC UA, Modbus, TSN etc.) Aggregated / Filtered Sensor Data via standard communication protocols and formats (e.g. MQTT/JSON) into the Cloud... Industrial IoT-/Edge-/Fog-Gateway(s) with specific industry communication stacks and local Analytic capabilties (in motion / at rest analytics)

6 Tomorrow: AI enabled Industrial IoT at the Edge supported through the Cloud and Mesh Computing!

7 Use Case: IoT Edge AI Computing Private Eyes and Ears for Predictive Maintenance and Quality Control Visual Edge 3. Edge gateway checks quality using Watson-trained Models. No Operational Data is sent to Cloud 1. Train Watson IoT how good and bad products look and sound like. 2. Watson sends models to Edge Gateways Watson IoT Audio Edge Assembly Line for Manufacturing / Quality Testing

8 Use Case: Mesh based Analytics with IBM Queryplex Keep your Production Automation data where they occur (on the edge) and then... Query anything, anywhere Query many diverse data sources across cloud, on-prem and the edge with advanced analytics using the most popular languages and tools Query many sources as one with extreme simplicity Connect millions of devices and databases into a single self balancing constellation. Avoid the complexity of centralized copies. Queryplex s Computational Mesh Benefit from a massive speedup 10 to 100,000 times acceleration using the power of every device. Query Query Result Based on edge analytics nodes production sites coordinator (*) SPS, MES, Edge Gateways/Devices etc.

9 ...and finally an (incomplete) personal IoT outlook Edge/Fog/IoT Gateway Computing Will Continue to Gain Traction It is not feasible that all IoT devices will be communicating directly with the cloud. The amount of data, network availability and latency, as well as security are some of the primary drivers for pushing IoT compute toward the edges of the network. Digital Twins Will Become a Common Thing in Production Automation Most of the major IoT vendors claim support for digital twins. It will be interesting to see if the IoT industry will agree to a common definition for digital twins and even some common standards. AI on the Edge will greatly benefit by ML enhanced Edge Devices More and more consumer and industrial devices will provide out-of-the box machine learning (ML) hardware support. Mesh computing will allow to more effectively utilize those resources.

10 Thank you!

11 Terminology Edge Computing... "...is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors and the central data center by performing analytics and knowledge generation at or near the source of the data. Fog Computing... "...or fog networking, also known as fogging, is an architecture that uses one or more collaborative multitude of end-user clients or near-user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over the internet backbone), control, configuration, measurement and management." Mesh Computing (or a Mesh Network) is a local network topology in which the infrastructure nodes (i.e. bridges, switches and other infrastructure devices) connect directly, dynamically and non-hierarchically to as many other nodes as possible and cooperate with one another to efficiently route data from/to clients. This lack of dependency on one node allows for every node to participate in the relay of information. Mesh networks dynamically self-organize and self-configure, which can reduce installation overhead. The ability to selfconfigure enables dynamic distribution of workloads, particularly in the event that a few nodes should fail. This in turn contributes to fault-tolerance and reduced maintenance costs. Source: Wikipedia

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