Schlagwort: smart industry

  • Sustainable transformation of agriculture with the Internet of Things

    Sustainable transformation of agriculture with the Internet of Things

    Reading Time: 3 minutes

    With the urgency to prevent environmental degradation, reduce waste and increase profitability, farmers around the globe are increasingly opting for more efficient crop management solutions supported by optimization and controlling technologies derived from the Industrial Internet of Things (IIoT). 

    Intelligent information and communication technologies (IICT) (machine Learning (ML), AI, IoT, cloud-based analytics, actuators, and sensors) are being implemented to achieve higher control of spatial and temporal variabilities with the aid of satellite remote sensing. The use and application of this set of related technologies are known as “Smart Agriculture.”  

    In SA, real-time and continuous monitoring of weather, crop growth, plant physical/chemical variables, and other critical environmental factors allow the optimization of yield production, reduction of labor, and improvement of farming products. Practices such as irrigation management, resource management, production, or fertilization operations are being facilitated by integrating IoT systems capable of providing information about multiple crop factors. In this way, while quality and quantity of production are boosted, the negative aspects of unsustainable and costly agriculture practices are also prevented with advanced interconnected actuators and sensors.

    Arduino Smart Agriculture

    Why Smart Agriculture?

    The major focus in this relatively new field is crop optimization through higher productivity and significant control over environmental variations. Smart agriculture provides a convenient way to integrate farming management by having in-hand mobile devices that receive data collected from Unmanned Aerial Vehicles (UAV), satellites, or wireless sensors that operate directly at the plant level and are connected, for example, to cloud-based systems.

    In general, SA can potentially:

    • Reduce water consumption,
    • Implement a better plant nursing process with optimized nutrient levels,
    • Decrease risk of yield loss,
    • Assurance of higher revenue,
    • Better yield quality,
    • Decrease overall production of waste,
    • Simplification of labor,
    • Enhance environmental protection.

    From small farming to urban gardening

    The IoT can provide solutions for small farmers ranging from resource management to climate adaptability. However, urban gardeners or small producers are also benefiting from innovations brought through the evolution of IoT. Figure 4 shows a typical low budget and high precision system designed to improve irrigation in urban gardens. 

    The system is relatively simple, but it offers the potential easiness of building open-source solutions without significant technical constraints in different setups where adaptation to environmental conditions is required. Basic electromagnetic sensors, power supply, a water pump, relays, and the irrigation system are hardware interconnected and managed via cloud-based monitoring. A control unit receives the data that the user later accesses via the internet. 

    Arduino small farming to urban gardening

    Technology democratization can boost the competitiveness of small producers. 

    Despite the tremendous potential of SA, technical issues are just one aspect of the whole story. The deployment of high-tech solutions that are less costly, accessible, reliable, and durable has not yet reached maximum potential. The limited internet coverage in rural areas, especially in emerging economies, slows down the deployment of SA technologies.  It is why the democratization of IICT, including the internet, is not a discussion of privilege. It is crucial to support the sustainable transformation of agriculture in which small farmers and rural communities can also benefit from technological development. 

    To increase the adaptation of IIoT solutions, Arduino Pro has recently launched ARDUINO EDGE CONTROL (AEC). With its ease to adapt to solar-based power supply, AEC offers the power of AI with state-of-the-art connectivity modems. To learn more about how you can use the Edge Control, check out how to get started.

    This is an edited version of an article originally published on Wevolver. For references used in this article, check the full piece at Wevolver.

    This is an edited version of an article originally published on Wevolver. Please check the original article for references used within this post.

    Website: LINK

  • Engineer’s guide to Industrial IoT in Industry 4.0

    Engineer’s guide to Industrial IoT in Industry 4.0

    Reading Time: 5 minutes

    This is an edited version of a longer piece first published on Wevolver.

    In recent years, industrial enterprises are accelerating their digital transformation and preparing themselves for the fourth industrial revolution (Industry 4.0). This digitization of production processes enables industrial organizations to implement agile and responsive manufacturing workflows, which rely on flexible Information Technology (IT) systems rather than on conventional Operational Technology (OT). This flexibility facilitates a shift from conventional Made-to-Stock (MTS) manufacturing to novel customizable production models like Made-to-Order (MTO), Configure-to-Order (CTO) and Engineering to Order (ETO). 

    The implementation of Industry 4.0 compliant production systems hinges on the deployment of Cyber-Physical Systems (CPS) in the manufacturing shop floor. In essence, CPS systems comprise one or several internet-connected devices integrated with other production systems in industrial environments. This is the main reason why Industry 4.0 is also referred to as Industrial Internet of Things (IIoT).  IIoT includes the subset of IoT (Internet of Things) systems and applications that are deployed in industrial environments such as the manufacturing, energy, agriculture, and automotive sectors. According to recent market studies, the lion’s share of IoT’s market value will stem from IIoT applications rather than from consumer segments.          

    The typical structure of IIoT applications is specified in standards-based architectures for industrial systems such as the Reference Architecture of the Industrial Internet Consortium. It comprises a stack of components that includes sensors and IoT devices, IoT middleware platforms, IoT gateways, edge/cloud infrastructures, and analytics applications. 

    The Power of Embedded Sensors in the Manufacturing Value Chain

    IT systems, enterprise applications (e.g., ERP and Manufacturing Execution System (MES)), and industrial networks for production automation have been around for decades. The real game-changer in Industry 4.0 is the expanded use of embedded sensors in the value chain. Embedded sensors transform manufacturing assets into cyber-physical systems and enable many optimizations that were hardly possible a few years ago. Overall, embedded sensors and other IIoT technologies empower increased efficiencies by transforming raw digital data to factory floor insights and automation actions. 

    Some of the perceived benefits of IIoT and embedded sensors deployments in production operation include:

    • Flexible Production Lines
    • Predictive Maintenance 
    • Quality Management
    • Supply Chain Management
    • Zero Defects Manufacturing
    • Digital Twins

    Data analysis options: Edge, Cloud, or combination?

    Most IIoT applications include data analytics functionalities such as sensor data analysis based on machine learning techniques. Therefore, they typically collect and process information within cloud computing infrastructures. The latter facilitates access to the required data storage and computing resources. Nevertheless, IIoT deployments in the cloud fall short when it comes to addressing low latency use cases, such as applications involving real-time actuation and control. In such cases, there is a need to execute operations close to the field (i.e., the shopfloor) that cannot tolerate delays for transferring and processing data in the cloud. 

    To address real-time, low-latency applications, industrial organizations are deploying IIoT applications based on the edge computing paradigm. The latter involves data collection and processing close to the field, within infrastructures like edge clusters (i.e., local cloud infrastructures), IoT gateways, and edge devices. A recent report by Gartner predicts that by 2023 over 50% of enterprise data will be processed at the edge

    Edge computing deployments are best suited for real-time control applications while helping to economize on bandwidth and storage resources. Specifically, data processing within edge devices facilitates the filtering of IoT data streams and enables enterprises to selectively transmit to the cloud “data points of interest” only. Furthermore, edge computing provides better data protection than cloud computing, as data remains within local edge devices rather than being transmitted to cloud data centers outside the manufacturing enterprise. Moreover, edge analytics functions like AI algorithms on edge devices are much more power-efficient than cloud-based analytics. 

    In practice, industrial enterprises employ both cloud computing and edge computing for their IIoT use cases. Specifically, they tend to deploy real-time functions at the edge and data-savvy industrial automation functions on the cloud. There is always an interplay between cloud and edge functions towards achieving the best balance between analytics accuracy, computational efficiency, and optimal use of bandwidth and storage resources. Thus, IIoT applications are usually deployed in the scope of a cloud-edge environment.

    Nowadays, there are many ways to implement edge computing and its interactions with cloud infrastructures. Likewise, there are also many options for employing machine learning at the edge of an industrial network, such as federated machine learning techniques or even deployment of machine learning functions in embedded devices. The latter involves a convergence of embedded programming with machine learning, characterized as embedded machine learning or TinyML

    State of the art cloud/edge computing paradigms support varying requirements of IIoT use cases in terms of latency, security, power efficiency, and the number of data points needed for training ML algorithms. Future articles in this series will shed light on the technical architecture and the deployment configurations of some of the above-listed cloud/edge paradigms. 

    The Scaling of IIoT and the Path towards industry 4.0

    Industry 4.0 has been around for over five years, yet we are still quite far from realizing the full potential of embedded sensors and the Industrial IoT. Many enterprises have started their deployment journey by setting up data collection infrastructures and deploying CPS systems and IoT devices on their shop floor. There are also several deployments of operational use cases in areas like asset management, predictive maintenance, and quality control. Nevertheless, many use cases are still in their infancy or limited to pilot deployments in pilot production lines or lab environments. Therefore, there is a need for evolving and scaling up existing deployments to enable industrial enterprises to adopt and fully leverage the fourth industrial revolution.

    The scaling up of Industry 4.0 use cases hinges on addressing the following challenges technical and organizational challenges:

    • Legacy compliance for brownfield deployments
    • Alleviating data fragmentation in industrial environments.
    • Addressing the IoT, BigData, and AI skills gap.
    • Ensuring access to pilot lines and experimentation infrastructures
    • Easing IIoT integration end-to-end i.e., from the embedded device to the manufacturing application
    • Realizing a cultural shift towards Industry 4.0. 

    Arduino Pro and Industry 4.0

    Driven by these challenges, Arduino has recently created its Arduino Pro solution for Professional Applications. It is an all-in-one IoT platform, which combines:     

    • Hardware boards for industrial control, robots, and edge AI applications.
    • End-to-End secure connectivity solutions for deploying cloud-based applications. 
    • Advanced development environments that enable low code application development
    • Ease of Implementation and significant community support

    Conclusion

    This article has introduced the Industrial Internet of Things, including its main use cases and business value potential for industrial enterprises. It has shed light on how embedded sensors, cloud/edge computing, and Artificial Intelligence provide a sound basis for optimizing production operations in directions that can improve production time, quality, and cost, while at the same time boosting employees’ safety and customers’ satisfaction. 

    Read the full version of this article, including references used here, at Wevovler.com.

    Website: LINK

  • Portenta Machine Control: Add a powerful brain to your machines

    Portenta Machine Control: Add a powerful brain to your machines

    Reading Time: 3 minutes

    Arduino Pro is introducing a powerful new member of the Portenta product family, the Portenta Machine Control. It’s a fully-centralized, low-power, industrial control unit able to drive equipment and machinery. Plus, you can program it using the Arduino framework or other embedded development platforms.

    [youtube https://www.youtube.com/watch?v=2wrBy8qNT60?feature=oembed&w=500&h=281]

    Thanks to its computing power, the Portenta Machine Control enables a wide range of predictive maintenance and AI use cases. It enables the collection of real-time data from the factory floor, while supporting remote control of equipment, including from the cloud.

    Key benefits include:

    • Shorter time-to-market
    • Enhance existing products
    • Add connectivity for monitoring, as well as control
    • Each I/O pin can be configured, so you can tailor it to your needs
    • Make equipment smarter, as well as AI-ready
    • Provide security and robustness from the ground up
    • Open new business model opportunities (such as servitization)
    • Interact with your equipment with advanced human-machine interfaces (HMI)
    • Modular design for adaptation, expansion and upgrades

    Business as a Service

    The Portenta Machine Control allows companies to enable new business-as-a-service models. You can monitor customer usage of equipment for predictive maintenance while gathering valuable production data.

    The device enables industry standard soft-PLC control. Because of this, it’s able to connect to a range of external sensors and actuators. For example, the following options are all available.

    • Isolated digital I/O, 4-20mA compatible analog I/O
    • Three configurable temperature channels
    • Dedicated I2C connector. 

    Multiple choices are available for network connectivity, including USB, Ethernet and WiFi and BLE. Furthermore, it offers impressive compatibility through industry specific protocols such as RS485. All I/O are protected by resettable fuses, but on-board power management ensures maximum reliability in harsh environments.

    The Portenta Machine Control core runs an Arduino Portenta H7 microcontroller board. This is a highly reliable design operating at industrial temperature ranges (-40 °C to +85 °C). Firstly, it boasts a dual-core architecture that doesn’t require any external cooling. Secondly, thanks to this versatile processor, you can also connect external human-machine interfaces. These include displays, touch panels, keyboards, joysticks and mice to enable on-site configuration of state machines and direct manipulation of processes.

    The Portenta Machine Control’s design addresses a large variety of use cases. It’s possible to configure a selection of the I/O pins in software. Because of this, it stands out as a powerful computer to unify and optimize production where one single type of hardware can serve all your needs. 

    The Arduino Portenta Machine Control

    Additional Portenta Machine Control Features

    Furthermore, it offers these other outstanding features.

    • Industrial performance leveraging the power of Arduino Portenta boards
    • DIN rail compatible housing
    • Push-in terminals for fast connection
    • Compact size (170 x 90x 50 mm)
    • Reliable design, operating at industrial temperature rates (-40 °C to +85 °C) with a dual-core architecture and no external cooling
    • Embedded RTC (real time clock), for perfect synchronization of processes
    • Leverage embedded connectivity without any external equipment
    • CE, FCC and RoHS certified

    The Portenta Machine Control can be used in multiple industries, across a wide range of machine types. For example, labelling machines, form and seal machines, cartoning machines, gluing machines, electric ovens, industrial washers and dryers, mixers and more.

    As a result, adding the Portenta Machine Control to your existing processes mean you become the owner of your own solutions in the market of machines.

    The Portenta Machine Control is now available for €279/$335.

    Take a look here for more information and complete technical specs.

    Website: LINK