I have four news items today. A couple are AI related and a couple more along the Edge. All concern developing products with the latest tech. ABB, Micropsi, IOTech, and ThinkIQ.
ABB to deliver artificial intelligence modeling for data center energy optimization in Singapore.
ABB has signed up to a pilot study with ST Telemedia Global Data Centres (STT GDC) to explore how artificial intelligence (AI), machine learning (ML), and advanced analytics can optimize energy use and reduce a facility’s carbon footprint.
Singapore-headquartered STT GDC, which is one of the fastest growing global data center operators, is leveraging the digital transformation expertise of technology leader ABB as it bids to become net carbon-neutral by 2030.
ABB is conducting the pilot in two phases, beginning with initial data exploration, modeling, and validation, studying historical data to establish how digital solutions would impact existing operations and energy use. Once proven, it will be followed by AI control logic testing in a live data center environment. STT GDC aims to achieve at least 10 percent in energy savings from its cooling systems, which is the largest consumption of electrical power in a data center after IT equipment.
The ABB team is currently developing AI-based optimization models for the entire data center cooling plant, including the upstream chiller and distribution systems. The AI project is also unlocking new opportunities for efficiency improvement at a granular level within the data center. STT GDC will be able to use AI-generated insights, leveraging cutting-edge ABB Ability™ Genix for industrial analytics and AI, to track and analyze data generated by monitoring systems in the data center, and better facilitate dynamic cooling optimization.
Micropsi Industries’ AI-driven Control System Speeds Complex and Precise Robot Training and Deployment
Industrial and collaborative robots learn to perform camera-guided movements more quickly with the latest version of Micropsi Industries’ MIRAI robot control system. Using artificial intelligence (AI), MIRAI enables robots to flexibly react to variances in their tasks in real time by learning from humans. Variances in position, shape, surface properties or lighting conditions are a common challenge for robotic automation of machine tending, assembly or test applications. With MIRAI’s new “positioning skills” feature, giving examples of quality movements to the robot has become much easier, and the robot will generalize and understand what to do much more quickly.
With the new feature, MIRAI customers will notice quicker set-up times, down from 2-3 days per skill to about three hours. In addition, robot speeds have increased, which also enables shorter cycle times.
Companies wanting to use a robot to perform precise and complex skills—such as gripping and inserting a bendable or soft component, like a cable, into differently arranged sockets—would primarily use the MIRAI controller at the first and last decisive centimeters of a manufacturing step.
With MIRAI, preparing robots to perform tasks that include variances requires a human worker to guide the robot arm several times through typically occurring scenarios to show the robot to its destination, such as sockets in which freely hanging cables need to be inserted. A machine learning process then derives a motion intuition for the robot from the given examples. For a robot that is not required to follow specific paths to perform its task, MIRAI users can deploy the new positioning skills to teach the robot to find the destination even faster because a human worker needs only to show MIRAI the surroundings of the target with the camera. The robot then independently searches for the shortest path to the object.
IOTech launches Edge Builder to manage edge systems at scale
IOTech, the edge software company, announced the launch and availability of Edge Builder, its end-to-end management solution for edge systems. Edge Builder provides a comprehensive, flexible and open solution that simplifies and automates the management of edge systems at scale.
To ensure that Edge Builder addresses the market opportunity, IOTech has been working with a number of key partners and potential customers during the development phase of the product.
Designed to meet the specific needs of edge systems, Edge Builder provides light touch provisioning and complete lifecycle management for both edge nodes and their applications. Currently it supports the deployment and management of containerized applications at the edge and in the future will also support the deployment of native binary applications.
Edge systems are managed from a centralized Edge Builder controller that can be hosted either on-premise or in the cloud. Platform independence for both the managed nodes and the cloud environment on which the controller is deployed ensures flexibility and choice for Edge Builder users.
Edge Management at Scale -Solving the Big Problem in the IoT Room
ThinkIQ Enhances SaaS Platform with Stronger Connectivity, Analytics and Visualization
ThinkIQ, a pioneer of digital manufacturing transformation SaaS, announced major enhancements to its SaaS Manufacturing platform. The new offering strengthens the company’s leading Transformational Intelligence Platform and provides more powerful and simplified modeling technology to allow for faster time to solution, better analytics and visualization, and higher performance data processing.
Many transformational intelligence platforms are either pure developments tools or are restricted to the feature set that is delivered with the platform. ThinkIQ’s latest enhancements deliver the best of both worlds with strong model integration combined with an extensible development platform that bridges the gap between traditional, on-premise OT technologies and strong could-enabled analytics.
These capabilities can be applied to any manufacturing and supply chain application and are particularly well-suited for hybrid, continuous and batch processes.
ThinkIQ’s SaaS Manufacturing cloud-based platform simplifies the creation of web-based applications and leverages the strengths of the Internet of Things, Big Data, Data Science, Semantic Modeling and Machine Learning. The platform collects data across the operation (existing and IIoT sensors) to provide actionable real time insights (e.g., identify correlations and root causes, traceability and yield issues, etc.). It creates a new level of capability beyond what independent disconnected operating environments can provide today.
To learn more about ThinkIQ, visit our website.
