Schlagwort: Industrial IoT

  • Simplifying IoT for smarter manufacturing: Join the chat with Arduino, AWS, and Atlas Machine

    Simplifying IoT for smarter manufacturing: Join the chat with Arduino, AWS, and Atlas Machine

    Reading Time: 2 minutes

    We all know that the future of manufacturing lies in IoT — yet the path to adoption can sometimes feel daunting. But what if you could simplify the process and start seeing results quickly? That’s exactly what we’re going to explore in our upcoming Arduino Cloud Café webinar on December 10 at 5PM CET / 11AM EST.

    This session is a unique opportunity to hear from experts at Arduino, AWS, and Atlas Machine as they dive into how industrial IoT is transforming manufacturing operations. Whether you’re just starting to explore IoT or looking for ways to optimize your existing systems, this webinar is for you.

    What to expect

    In this session, we’ll be sharing actionable tips and insights to help you easily integrate IoT into your operations:

    • Learn how to collect data quickly — without months of delays.
    • Understand how to retrofit your legacy equipment and get real-time visibility into your operations.
    • Discover how to integrate the data from Arduino devices with the rest of your business systems on AWS for smarter decision-making.

    We’ll also be sharing real-world success stories, including how Atlas Machine & Supply leveraged Arduino (Opta and Arduino Cloud) and AWS solutions for predictive maintenance and remote monitoring across their global fleet of industrial equipment.

    And don’t forget, we’ll have a live Q&A session at the end, where you can ask our experts anything. Feel free to submit your questions throughout the webinar, and we’ll do our best to address as many as possible.

    Meet the speakers

    We’re excited to be joined by a fantastic lineup of speakers who are experts in their fields:

    • Richie Gimmel, CEO at Atlas Machine & Supply
    • Danny Kent, IoT Development Director at Atlas Machine & Supply
    • Andrea Richetta, Principal Product Evangelist at Arduino
    • Gabriel Verreault, Senior Manufacturing Partner Solutions Architect at AWS

    Why you should join

    If you’ve been looking for a way to simplify IoT adoption in your manufacturing operations, this is your chance to learn from industry leaders who are making it happen. Whether you’re trying to modernize old equipment or integrate IoT into your larger business strategy, you’ll walk away with valuable insights and tips you can start using right away.

    Save your spot today! Don’t miss out on this chance to hear from the experts and get your questions answered. We can’t wait to see you there!

    Register now

    The post Simplifying IoT for smarter manufacturing: Join the chat with Arduino, AWS, and Atlas Machine appeared first on Arduino Blog.

    Website: LINK

  • Welcome Opta, our first-ever micro PLC with Industrial IoT capabilities

    Welcome Opta, our first-ever micro PLC with Industrial IoT capabilities

    Reading Time: 2 minutes

    We can finally tell you all about the new product we had announced for SPS! Today, we are excited to expand the Arduino Pro range with Opta, our first micro PLC with Industrial IoT capabilities.

    We designed it specifically with PLC engineers in mind: supporting standard languages including LD (Ladder Logic Diagram) and FBD (Function Block Diagram), it allows professional users to scale up industrial and building automation projects leveraging our signature open-source, easy-to-use, anti-vendor-lock-in approach.

    We partnered with none other than Finder — a leader in the field of mission-critical electromechanical and electronic components with 65+ years of excellence and expertise to its name — to create the most durable, reliable, secure and high-performance component possible, while still maintaining our signature flexibility and ease of deployment in production.

    Through its onboard connectivity and machine learning capabilities, Opta opens up new opportunities for industrial automation within everyone’s reach: design, iterate and implement increasingly ambitious ideas for higher-quality manufacturing, increased efficiency, better productivity and profitability. If you agree with us that innovation is here to help us face every possible challenge, we look forward to seeing the solutions you create to work better with Opta!

    Here is a taste of its tech specs:

    • High-performance computing. Its powerful STM32H747XI dual-core Cortex®-M7 +M4 MCU enables users to perform real-time operations and implement predictive maintenance capabilities.
    • Secure and durable by design. Opta supports OTA firmware updates and ensures data security from the hardware to the Cloud through M2M communication, thanks to the onboard secure element and X.509 Standard compliance.
    • Faster turnaround. Get to work quickly with Arduino’s ease of use, flexibility and extensive sketches, tutorials and software libraries.
    • Three configurations to suit any user’s needs. Opta is available in three variants with incremental features: Opta Lite with onboard Ethernet and USB-C programming ports, Opta RS485 which also adds RS485 half duplex connectivity interface, and Opta WiFi, the most versatile option, featuring also Wi-Fi/Bluetooth® Low Energy connectivity.

    Ready to opt in to industrial automation? Whether you are interested in real-time control, monitoring or predictive maintenance applications, find out more about Opta on our website’s dedicated page.

    The post Welcome Opta, our first-ever micro PLC with Industrial IoT capabilities appeared first on Arduino Blog.

    Website: LINK

  • Industrial IoT anomaly detection on microcontrollers

    Industrial IoT anomaly detection on microcontrollers

    Reading Time: 2 minutes

    Arduino Team — July 22nd, 2022

    Consumer IoT (Internet of Things) devices provide convenience and the consequences of a failure are minimal. But industrial IoT (IIoT) devices monitor complex and expensive machinery. When that machinery fails, it can cost serious money. For that reason, it is important that technicians get alerts as soon as an abnormality in operation occurs. That’s why Tomasz Szydlo at AGH University of Science and Technology in Poland researched IIoT anomaly detection techniques for low-cost microcontrollers.

    When you only have a single sensor value to monitor, it is easy to detect an anomaly. For example, it is easy for your car to identify when engine temperature exceeds an acceptable range and then turn on a warning light. But this becomes a serious challenge when a complex machine has many sensors with values that vary depending on conditions and jobs — like a car engine becoming hot because of hard acceleration or high ambient temperatures, as opposed to a cooling problem. 

    In complex scenarios, it is difficult to hard code acceptable ranges to account for every situation. Fortunately, that is exactly the kind of problem that machine learning excels at solving. Machine learning models don’t understand the values they see, but they are very good at recognizing patterns and when values deviate from those patterns. Such a deviation indicates an anomaly that should raise a flag so a technician can look for an issue. 

    Szydlo’s research focuses on running machine learning models on IIoT hardware for this kind of anomaly detection. In his tests, he used an Arduino Nano 33 BLE board as an IIoT accelerometer monitor for a simple USB fan. He employed FogML to create a machine learning model efficient enough to run on the relatively limited hardware of the Nano’s nRF52840 microcontroller.

    The full results are available in Szydlo’s paper, but his experiments were a success. This affordable hardware was able to detect anomalies with the fan speed. This is a simple application, but as Szydlo notes, it is possible to expand the concept to handle more complex machinery.

    Image: arXiv:2206.14265 [cs.LG]

    You can follow any responses to this entry through the RSS 2.0 feed.
    You can leave a response, or trackback from your own site.

    Website: LINK