Software : Cloud Computing : General
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster.
How Corning Built End-to-end ML on Databricks Lakehouse Platform
Specifically for quality inspection, we take high-resolution images to look for irregularities in the cells, which can be predictive of leaks and defective parts. The challenge, however, is the prevalence of false positives due to the debris in the manufacturing environment showing up in pictures.
To address this, we manually brush and blow the filters before imaging. We discovered that by notifying operators of which specific parts to clean, we could significantly reduce the total time required for the process, and machine learning came in handy. We used ML to predict whether a filter is clean or dirty based on low-resolution images taken while the operator is setting up the filter inside the imaging device. Based on the prediction, the operator would get the signal to clean the part or not, thus reducing false positives on the final high-res images, helping us move faster through the production process and providing high-quality filters.
Building Industrial Digital Twins on AWS Using MQTT Sparkplug
Even better, a Sparkplug solution is built around an event-based and publish-subscribe architectural model that uses Report-By-Exception for communication. Meaning that your Digital Twin instances get updated with information only when a change in the dynamic properties is detected. Firstly, this saves computational and network resources such as CPU, memory, power and bandwidth. Secondly, this results in a highly responsive system whereby anomalies picked up by the analytics system can be adjusted in real-time.
Further, due to the underlying MQTT infrastructure, a Sparkplug based Digital Twin solution can scale to support millions of physical assets, which means that you can keep adding more assets with no disruptions. What’s more, MQTT Sparkplug’s definition of an MQTT Session State Management ensures that your Digital twin Solution is always aware of the status of all your physical assets at any given time.
How KAMAX connected their industrial machines to AWS in hours instead of weeks
Every manufacturing customer these days is talking about Industry 4.0, digital transformation, or AI/ML, but these can be daunting topics for manufacturers. Historically, connecting industrial assets to the cloud has been a large and complicated undertaking. Older assets increase the complexity, leaving many manufacturers with legacy equipment stalled at the starting gate. KAMAX, a player for cold forming parts in the sector of steel processing, shows that it is not only possible to transform, but can be easy when working with the right partners. KAMAX wanted a fully managed shop floor solution to acquire data from industrial equipment, process the data and make it available fast, to improve their operational efficiency. KAMAX employed their subsidiary and digital incubator, nexineer digital, Amazon Web Services (AWS) and CloudRail to help. This Industrial IoT collaboration increased manufacturing efficiency and effectiveness within their plants by automating and optimizing traditionally manual tasks, increasing production capacity, and optimizing tool changeover times (planned downtimes) of machines. This solution helped KAMAX realize quantifiable time savings of 2.5% – 3.5%.
California’s AI-Powered Wildfire Prevention Efforts Contend With Data Challenge
Southern California Edison, San Diego Gas & Electric Co. and PG&E Corp. say they see promise in AI algorithms that use images captured by drones and other means to detect anomalies in infrastructure that could lead to wildfires. However, they say it will likely take years to gather enough data to deploy the algorithms at scale across their infrastructure, where they would augment ongoing manual inspections.
San Diego Gas & Electric said it has 75 working models designed to detect specific conditions or damages on company assets or third-party equipment. Gabe Mika, senior group product manager, said each is trained on anywhere from 100 to 5,000 images. SDG&E has leveraged several of Amazon Web Services’ machine-learning and computer vision tools to help build the models, the company said.
Koch Ag & Energy High Value Digitalization Deployments Leverages AWS
This application uses existing plant sensors, Monitron sensors, Amazon Lookout and SeeQ software to implement predictive maintenance on more complex equipment. The goal accomplished was successfully implementing predictive maintenance requires data from thousands of sensors to gain a clear understanding of unique operating conditions and applying machine learning models to achieve highly accurate predictions. In the past modeling equipment behavior and diagnosis issues requiring significant investment in time money inhabiting scaling this capability across all assets.
AWS Announces AWS IoT TwinMaker
Industrial companies collect and process vast troves of data about their equipment and facilities from sources like equipment sensors, video cameras, and business applications (e.g. enterprise resource planning systems or project management systems). Many customers want to combine these data sources to create a virtual representation of their physical systems (called a digital twin) to help them simulate and optimize operational performance. But building and managing digital twins is hard even for the most technically advanced organizations. To build digital twins, customers must manually connect different types of data from diverse sources (e.g. time-series sensor data from equipment, video feeds from cameras, maintenance records from business applications, etc.). Then customers have to create a knowledge graph that provides common access to all the connected data and maps the relationships between the data sources to the physical environment. To complete the digital twin, customers have to build a 3D virtual representation of their physical systems (e.g. buildings, factories, equipment, production lines, etc.) and overlay the real-world data on to the 3D visualization. Once they have a virtual representation of their real-world systems with real-time data, customers can build applications for plant operators and maintenance engineers that can leverage machine learning and analytics to extract business insights about the real-time operational performance of their physical systems. Because of the work required, the vast majority of organizations are unable to use digital twins to improve their operations.
Apollo Tyres Moves to AWS to Build Smart, Connected Factories
Apollo Tyres needed to upgrade its infrastructure to develop new ways of engaging with fleet operators, tyre dealers, and consumers, while delivering tyres and services efficiently at competitive prices. The company’s first step was to create a data lake on AWS, which centrally stores Apollo Tyres’ structured and unstructured data at scale. This data lake provides the foundation for an integrated data platform, which enables Apollo Tyres’ engineers around the world to collaborate in developing cloud-native applications and improve enterprise-wide decision making. The integrated data platform enables Apollo Tyres to innovate new products and services, including energy-efficient tires and remote warranty fulfillment.
AWS, Google, Microsoft apply expertise in data, software to manufacturing
As manufacturing becomes digitized, Google’s methodologies that were developed for the consumer market are becoming relevant for industry, said Wee, who previously worked in the semiconductor industry as an industrial engineer. “We believe we’re at a point in time where these technologies—primarily the analytics and AI area—that have been very difficult to use for the typical industrial engineer are becoming so easy to use on the shop floor,” he said. “That’s where we believe our competitive differentiation lies.”
Meanwhile, Ford is also selectively favoring human brain power over software to analyze data and turning more and more to in-house coders than applications vendors. “The solution will be dependent upon the application,” Mikula said. “Sometimes it will be software, and sometimes it’ll be a data analyst who crunches the data sources. We would like to move to solutions that are more autonomous and driven by machine learning and artificial intelligence. The goal is to be less reliant on purchased SaaS.”
AWS IoT SiteWise Edge Is Now Generally Available for Processing Industrial Equipment Data on Premises
With AWS IoT SiteWise Edge, you can organize and process your equipment data in the on-premises SiteWise gateway using AWS IoT SiteWise asset models. You can then read the equipment data locally from the gateway using the same application programming interfaces (APIs) that you use with AWS IoT SiteWise in the cloud. For example, you can compute metrics such as Overall Equipment Effectiveness (OEE) locally for use in a production-line monitoring dashboard on the factory floor.
Seeq Accelerates Chemical Industry Success with AWS
Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, today announced agreements with two of the world’s premier chemical companies: Covestro and allnex. These companies have selected Seeq on Amazon Web Services (AWS) as their corporate solution, empowering their employees to improve production and business outcomes.
Amazon Lookout For Equipment – Predictive Maintenance Is Now Mature
Amazon Lookout for Equipment is designed for maintainers, not data scientists, and it comes from a place of knowledge. Incorporating expertise and insight gleaned from maintaining its own assets, Amazon aims to make it as easy as possible for users to get started and begin seeing value, addressing potential issues around usability and configurability.
In terms of technical abilities, it currently only covers simple assets like motors, conveyors, and servos – essentially, the kind of assets Amazon itself uses. It doesn’t yet monitor more sophisticated assets like robots or CNC machinery, although, in time, I do not doubt that these, too, will also be covered. As it stands, though, it will be competent for a lot of standard factory equipment.
How to build a predictive maintenance solution using Amazon SageMaker
Introducing Amazon SageMaker Reinforcement Learning Components for open-source Kubeflow pipelines
Woodside Energy uses AWS RoboMaker with Amazon SageMaker Kubeflow operators to train, tune, and deploy reinforcement learning agents to their robots to perform manipulation tasks that are repetitive or dangerous.
AWS Announces General Availability of Amazon Lookout for Vision
AWS announced the general availability of Amazon Lookout for Vision, a new service that analyzes images using computer vision and sophisticated machine learning capabilities to spot product or process defects and anomalies in manufactured products. By employing a machine learning technique called “few-shot learning,” Amazon Lookout for Vision is able to train a model for a customer using as few as 30 baseline images. Customers can get started quickly using Amazon Lookout for Vision to detect manufacturing and production defects (e.g. cracks, dents, incorrect color, irregular shape, etc.) in their products and prevent those costly errors from progressing down the operational line and from ever reaching customers. Together with Amazon Lookout for Equipment, Amazon Monitron, and AWS Panorama, Amazon Lookout for Vision provides industrial and manufacturing customers with the most comprehensive suite of cloud-to-edge industrial machine learning services available. With Amazon Lookout for Vision, there is no up-front commitment or minimum fee, and customers pay by the hour for their actual usage to train the model and detect anomalies or defects using the service.
AWS Predictive Quality Industrial Demo
Facilitating IoT provisioning at scale
Whether you’re looking to design a new device or retrofitting an existing device for the IoT, you will need to consider IoT provisioning which brings IoT devices online to cloud services. IoT provisioning design requires decisions to be made that impact user experience and security for both network commissioning and credential provisioning mechanisms which configure digital identities, cloud end-points, and network credentials so that devices can securely connect to the cloud.
AI Solution for Operational Excellence
Falkonry Clue is a plug-and-play solution for predictive production operations that identifies and addresses operational inefficiencies from operational data. It is designed to be used directly by operational practitioners, such as production engineers, equipment engineers or manufacturing engineers, without requiring the assistance of data scientists or software engineers.
Unchain the ShopFloor through Software-Defined Automation
But, what happens as soon as insight is generated and the status of the physical process needs to be changed to a better state? In manufacturing for discrete and process industries, the process is defined by fixed code routines and programmable parameters. It has its own world of control code languages and standards to define the behavior of controllers, robot arms, sensors and actuators of all kinds. This world has remained remarkably stable over the past 40-plus years. Control code resides on a controller and special tools, as well as highly skilled automation engineers, who define the behavior of a specific production system. Changing the state of an existing and running production system changes the programs and parameters required to physically access the automation equipment—OT equipment needs to be re-programmed, often on every single component locally. To give a concrete example, let’s assume we can determine from field data, using applied machine learning (also referenced as Industrial IoT), that a behavior of a robotic handling process needs to be adapted. In the existing world, production needs to stop. A skilled engineer needs to physically re-teach or flash the robot controller. The new movement needs to be tested individually and in context of the adjacent production components. Finally, production can start again. This process can take minutes to hours depending on the complexity of the production system.
Production systems will optimize themselves based on simulated and real experiment. Improvements will rapidly be propagated around the globe. Labor will optimize the learning, not the system. This could also differ over time or by external influence. In times where renewable energy was cheap, output could have been one of the core drivers for optimization, while the minimization of input factors could have been paramount in other circumstances.