Nuvoton recently launched the NuMicro M091 Series of microcontrollers, these are 32-bit MCUs based on the Arm Cortex-M0 core, featuring 4 sets of operational amplifiers with 8 MHz gain bandwidth (GBW), 4 sets of 12-bit DAC, up to 16 channels of 2 MSPS 12-bit SAR ADC, a temperature sensor, and extensive I/O options. The MCU supports the NuMaker evaluation board and various third-party IDEs making this an ideal device for industrial sensing, smart sensors, and precision instrumentation applications. Previously we have seen Nuvoton release MA35H0 and MA35D1 both MPUs are based on Cortex-A35 cores, feel free to check those out if you are interested in the topic. Nuvoton NuMicro M091 MCU specifications: Processor ARM Cortex-M0 core Maximum clock speed: 72 MHz Memory Flash – Up to 64 KB SRAM – 8 KB LDROM – 2 KB (for user program loader) SPROM – 512 Bytes (for security protection) Analog Features 4x [...]
The NRFICE FPGA is a Bluetooth FPGA board designed for edge computing and IoT applications. It is built upon a combination of the dual-core nRF5340 Bluetooth SoC and the Lattice ICE40UP5K FPGA. The ICE40 UltraPlus is a low-power, high-performance FPGA for edge computing and artificial intelligence projects and the nRF5340 is a Bluetooth 5.3 SoC that supports Bluetooth Low Energy (BLE), Bluetooth Mesh, Thread, NFC, and Zigbee. Through the Nordic nRF5340, NRFICE can load a project directly into the iCE40 FPGA, bypassing the usual extensive toolchain setup. This enables a new class of FPGA development, where bitstreams can be hosted in the cloud, selected by a user on their phone, and loaded wirelessly to the board. It features a built-in J-Link OB for easy debugging and programming without the need for emulator dongles and is similar to the previously covered Segger emPower evaluation board in this regard. This board supports [...]
On the surface, a programmable logic controller (PLC) might seem like nothing more than a generic microcontroller, perhaps outfitted to operate in industrial settings with things like high temperatures or harsh vibrations. While this is true to some extent, PLCs also have an international standard for their architecture and programming languages. This standard is maintained by the International Electrotechnical Commission, making it so that any device built under these specifications will be recognizable to control engineers and maintenance personnel worldwide. And, if you use this standard when working with certain Arduinos, this common platform can become a standard-compliant PLC as well.
The IDE itself supports programming ladder diagrams, functional block diagrams, and other programming systems covered under the IEC 61131-3 standard. Not only that, it allows the combination of these types of PLC programming with Arduino sketches. The system offers many of the perks of PLC programming alongside the familiar Arduino platform, and supports a number of protocols as well including CANOpen, Modbus RTU, and Modbus TCP. It can also be used for monitoring a PLC system, essentially adding IoT capabilities to existing systems, enabling continuous monitoring, debugging, and program updates.
While not every Arduino is a great platform to build a PLC around, there are a few available for those looking for a system a little less proprietary and a little more user-friendly than typical PLC systems tend to be. There’s a reason that PLCs are built around an international standard and generally have certain hardware in mind to run it, though, and this comparison of a Raspberry Pi with an off-the-shelf PLC goes into detail about why certain components aren’t good choices for PLCs.
Wevolver’s previous article about the Arduino Pro ecosystem outlined how embedded sensors play a key role in transforming machines and automation devices to Cyber Physical Production Systems (CPPS). Using CPPS systems, manufacturers and automation solution providers capture data from the shop floor and use it for optimizations in areas like production schedules, process control, and quality management. These optimizations leverage advanced data Internet of Things (IoT) analytics over manufacturing datasets, which is the reason why data are the new oil.
Deployment Options for IoT Analytics: From Cloud Analytics to TinyML
IoT analytics entail statistical data processing and employ Machine Learning (ML) functions, including Deep Learning (DL) techniques i.e., ML based on deep neural networks. Many manufacturing enterprises deploy IoT analytics in the cloud. Cloud IoT analytics use the vast amounts of cloud data to train accurate DL models. Accuracy is important for many industrial use cases like Remaining Useful Life calculation in predictive maintenance. Nevertheless, it is also possible to execute analytics at the edge of the network. Edge analytics are deployed within embedded devices or edge computing clusters at the factory’s Local Area Network (LAN). They are appropriate for real-time use cases that demand low latency such as real-time detection of defects. Edge analytics are more power-efficient than cloud analytics. Moreover, they offer increased data protection as data stays within the LAN.
During the last couple of years, industrial organizations use TinyML to execute ML models within CPU and memory-constrained devices. TinyML is faster, real-time, more power-efficient, and more privacy-friendly than any other form of edge analytics. Therefore, it provides benefits for many Industry 4.0 use cases.
TinyML is the faster, real-time, most power-efficient, and most privacy friendly form of edge analytics. Image credit: Carbon Robotics.
Building TinyML Applications
The process of developing and deploying TinyML applications entails:
Getting or Producing a Dataset, which is used for training the TinyML model. In this direction, data from sensors or production logs can be used.
Train an ML or DL Model, using standard tools and libraries like Jupyter Notebooks and Python packages like TensorFlow and NumPy. The work entails Exploratory Data Analysis steps towards understanding the data, identifying proper ML models, and preparing the data for training them.
Evaluate the Model’s Performance, using the trained model predictions and calculating various error metrics Depending on the achieved performance, the TinyML engineer may have to improve the model and avoid overfitting on the data. Different models must be tested to find the best one.
Make the Model Appropriate to Run on an Embedded Device, using tools like TensorFlow Lite which provides a “converter” library that turns a model into a space-efficient format. TensorFlow Lite provides also an “interpreter” library that runs the converted model using the most efficient operations for a given device. In this step, a C/C++ sketch is produced to enable on device deployment.
On-device Inference and Binary Development, which involves the C/C++ and embedded systems development part and produces a binary application for on-device inference.
Deploying the Binary to a Microcontroller, which makes the microcontroller able to analyse data and derive real-time insights.
Building a Google Assistant using tinyML. Image credit: Arduino.
Leveraging AutoML for Faster Development with Arduino Pro
Nowadays, Automatic Machine Learning (AutoML) tools are used to develop TinyML on various boards, including Arduino boards. Emerging platforms such as Edge Impulse, Qeexo and SensiML, among others, provide AutoML tools and developers’ resources for embedded ML development. Arduino is collaborating with such platforms as part of their strategy to make complex technologies open and simple to use by anyone.
Within these platforms, users collect real-world sensor data, train ML models on the cloud, and ultimately deploy the model back to an Arduino device. It is also possible to integrate ML models with Arduino sketches based on simple function calls. AutoML pipelines ease the tasks of (re)developing and (re)deploying models to meet complex requirements.
The collaboration between Arduino and ML platforms enables thousands of developers to build applications that embed intelligence in smart devices such as applications that recognize spoken keywords, gestures, and animals. Implementing applications that control IoT devices via natural language or gestures is relatively straightforward for developers who are familiar with Arduino boards.
Arduino has recently introduced its new Arduino Pro ecosystem of industrial-grade products and services, which support the full development, production and operation lifecycle from Hardware and Firmware to Low Code, Clouds, and Mobile Apps. The Pro ecosystem empowers thousands of developers to jump into Industry 4.0 development and to employ advanced edge analytics.
Big opportunity at every scale
The Arduino ecosystem provides excellent support for TinyML, including boards that ease TinyML development, as well as relevant tools and documentation. For instance, the Arduino Nano 33 BLE Sense board is one of the most popular boards for TinyML. It comes with a well-known form factor and various embedded sensors. The latter include a 9-axis inertial sensor that makes the board ideal for wearable devices, as well as for humidity and temperature sensors. As another example, Arduino’s Portenta H7 board includes two asymmetric cores, which enables simultaneously runs of high level code such as protocol stacks, machine learning or even interpreted languages (e.g., MicroPython or JavaScript). Furthermore, the Arduino IDE (Integrated Development Environment) provides the means for customizing embedded ML pipelines and deploying them in Arduino boards.
In a Nutshell
ML and AI models need not always to run over powerful clouds and related High Performance Computing services. It is also possible to execute neural networks over tiny memory-limited devices like microcontrollers, which opens unprecedented opportunities for pervasive intelligence. The Arduino ecosystem offers developers the resources they need to ride the wave of Industry 4.0 and TinyML. Arduino boards and the IDE lower the barriers for thousands of developers to engage with IoT analytics for industrial intelligence.
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.
The Arduino Pro lineup continues to grow with the introduction of the new Arduino Edge Control. This is a remote monitoring and control solution optimized for outdoor environments. Easy deployment makes it suitable for smart agriculture, precision farming, and other intelligent control applications in remote locations.
Featuring built-in Bluetooth, Arduino MKR boards can expand connectivity with 2G/3G/CatM1/NB-IoT modems, LoRa®, Sigfox and WiFi. With solar power capabilities you can place it anywhere while leveraging AI on the edge. Once installed in the field, it can then be managed remotely using Arduino IoT Cloud (or other services).
Real-Time Monitoring with Edge Control Sensors
You can also connect sensors, provide real-time monitoring, and drive actuators — commonly used in agriculture — thereby reducing production-related risks.
Particularly aimed at smart agriculture, the sensors can collect real-time data. Weather conditions, soil quality, crop growth and any other data you need. Once sent to Arduino IoT Cloud, the data value chain becomes valuable analytics that support business processes at various levels. For example, crop yield, equipment efficiency, staff performance and so forth. The Edge Control can improve crop quality, reduce effort and minimize error by automating processes like irrigation, fertilization or pest control.
Remote Access and Maintenance
With its robust design, the Edge Control is a fitting solution for applications in any outdoor setting. For example, using it on construction sites or in real estate to automate access control. Similarly, swimming pool maintenance and cleaning companies could monitor and control the condition of pool water from remote locations. As usual, we expect the Arduino community to come up with countless ingenious ways to implement this new technology.
To learn more about how you can use the Edge Control, check out how to get started.
The Edge Control is now available for €169/US$199 on the Arduino Store.
Industrial hardware needs to be reliable, tough, and interoperable. For this reason, there are a series of standards used for command & control connections between equipment. One of the more widespread standards is ModBus, an open protocol using a master-slave architecture, usually delivered over RS-485 serial. It’s readily found being used with PLCs, HMIs, VFDs, and all manner of other industrial equipment that comes with a TLA (three letter acronym).
[Absolutelyautomation] decided to leverage ModBus to control garden variety digital cameras, of the type found cluttering up drawers now that smartphones have come so far. This involves getting old-school, by simply soldering wires to the buttons of the camera, and using an Arduino Nano to control the camera while talking to the ModBus network.
This system could prove handy for integrating a camera into an industrial production process to monitor for faults or defective parts. The article demonstrates simple control of the camera with off-the-shelf commercial PLC hardware. Generally, industrial cameras are very expensive, so this hack may be useful where there isn’t the budget for a proper solution. Will it stand up to industrial conditions for 10 years without missing a beat? No, but it could definitely save the day in the short term for a throwaway price. One shortfall is that the camera as installed will only save pictures to its local memory card. There’s a lot to be said for serving the images right to the engineer’s desk over a network.
Industruino’s mission is to offer industrial automation components that have the simplicity of Arduino at its core. It’s created by Loic & Ainura, two product designers originally from Belgium and now based in Shenzhen, with a mission to help people make their own products, by creating an accessible platform.
Today they are officially joining the Arduino AtHeart program with Industruino Proto, a Leonardo compatible industrial controller housed in a DIN-rail enclosure, with screw connector terminals to robustly connect to sensors and actuators.
Industruino allows makers and professionals to take a breadboarded solution and make it into an enclosed finished looking product, ready for permanent installation. Watch Loics’ introduction:
With Industruino everyone can combine the strengths of Arduino with the specific requirements of industry:
We are now at the dawn of a new industrial revolution, one in which the key elements will be automation, robotics and interconnected devices. In this revolution the Arduino platform is growing to be a real contender.
We are very excited to become part of the At Heart family! It is our way to show that we are very much interlinked with the Arduino community. We are looking forward to further develop the use of Arduino in industrial applications whilst contributing back to the Arduino platform.
When you open the enclosure you will find a prototyping area to add your own components, and re-routable jumper connections, letting you connect any point to either the microcontroller’s pins or the external screw connectors. The onboard graphic LCD and membrane button panel facilitate quick UI development to visualise and input your application’s data.
Arduinos! They’re a great tool that make the world of microcontrollers pretty easy, and in [cptlolalot]’s case, they also give us an alternative to buying expensive, proprietary parts. [cptlolalot] needed a gauge for an expensive vacuum pump, and rather than buying an expensive part, built a circuit around an Arduino to monitor the vacuum.
This project goes a little beyond simple Arduino programming though. A 12V to 5V power supply drives the device, which is laid out on a blank PCB. The display fits snugly over the circuit which reduces the footprint of the project, and the entire thing is housed in a custom-printed case with a custom-printed pushbutton. The device gets power and data over the RJ45 connection so no external power is needed. If you want to take a look at the code, it’s linked on [cptlolalot]’s reddit thread.
This project shows how much easier it can be to grab an Arduino off the shelf to solve a problem that would otherwise be very expensive. We’ve been seeing Arduinos in industrial applications at an increasing rate as well, which is promising not just because it’s cheap but because it’s a familiar platform that will make repairs and hacks in the future much easier for everyone.
Let us be the first to say: Not a hack! Nonetheless this is an interesting read about how the Arduino movement has made hobby microcontroller boards attractive for industrial applications.
This is a digital printing machine which looks like it is used for industrial packaging. [Paul Furley] worked for the company which produces it, developing the software for the control interface. He recounts the story of how he helped guide the company away from choosing a microcontroller, and toward using an Arduino board. Actually, using three Arduino boards. We can already hear the flame war boiling up in the comments section. But before you rage, read the article and see if you don’t agree with [Paul's] reasoning.
The most compelling argument to us is that choosing Arduino is absolutely future proof. If the company goes out of business there are hundreds of clone devices already available. As the Arduino platform evolves it will keep pin compatibility in order to support the older shields. And if they choose a different microcontroller the Arduino IDE will still compile the same sketch for the new hardware.
One thing that pops into our minds is write protection. The machine uses a big PCB to which the three Arduinos mount. That can be produced anywhere without threat of having the source code leak as the PCB doesn’t include chips that need to be programmed. Arduino uses AVR chips that have write protection fuses which can be burnt in-house after they flash the control firmware.
Planet Arduino is, or at the moment is wishing to become, an aggregation of public weblogs from around the world written by people who develop, play, think on Arduino platform and his son. The opinions expressed in those weblogs and hence this aggregation are those of the original authors. Entries on this page are owned by their authors. We do not edit, endorse or vouch for the contents of individual posts. For more information about Arduino please visit www.arduino.cc
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This project goes a little beyond simple Arduino programming though. A 12V to 5V power supply drives the device, which is laid out on a blank PCB. The display fits snugly over the circuit which reduces the footprint of the project, and the entire thing is housed in a custom-printed case with a custom-printed pushbutton. The device gets power and data over the RJ45 connection so no external power is needed. If you want to take a look at the code, 
