Latest software technology brings the world of Automated Machine Learning and Artificial Intelligence to the masses

Thought Leadership published by Anglia, under Artificial Intelligence / Machine learning, Cloud, Components, Internet of Things (IoT), PR

Nick Stone, Field Application Engineer at Anglia, introduces the NanoEdge™ AI Studio product from STMicroelectronics which enables developers to create an optimal ML library for their STM32 Microcontroller based projects, using a minimal amount of data in just a few steps.

Imagine the transformation if the next system you design could autonomously learn about its environment, predict anomalies, and understand and report causes of failure. This is already becoming a reality with thousands of pumps, motors and home appliances! In the quest for smarter equipment and infrastructure, Machine Learning (ML) and Artificial Intelligence (AI) integration are now being employed to help boost the reliability, innovation and capabilities of equipment. Machine Learning gives software applications the ability to predict outcomes more accurately without being explicitly programmed to do so using data and is a type of Artificial Intelligence.

The evolution of Smart devices
As technology has moved into the connected IoT era many more powerful features have been made possible by the intelligent and powerful processing power available in the Cloud, this includes ML and AI implementations.

As these new powerful features have been realised they are now often entirely depended on for the efficient functioning of equipment, an example of this is smart home devices, we know how frustrating it can be when we are unable to turn on a light by voice activation due to no internet connection. For many applications this is just a minor inconvenience, however for mission critical equipment connectivity or latency issues can be a big problem, therefore the need to bring some of this intelligent processing including ML and AI back onto the equipment itself, via intermediate gateways or edge devices which are not reliant on Cloud connectivity, has increased.

Implementing Machine Learning and Artificial Intelligence
Before we go any further, it is worth drawing the distinction between ML and AI. A full implementation of AI allows a machine to address tasks that would normally require human intelligence. ML is a form of artificial intelligence that addresses tasks by learning from reference or collected data and then makes predictions or acts based on this data.

Whilst ML and AI have now worked their way into everyday vernacular, implementing them on a technical level in a real-world application is challenging for embedded developers, even those with some prior experience of AI. The investment, complexity and development time required can often be barriers to AI adoption. Fortunately, STMicroelectronics have recognised developers need a new generation of tools to allow the mass implementation of ML and AI in applications and have released NanoEdge™ AI Studio, a new Automated Machine Learning tool that brings true innovation easily to end-users allowing the embedding of cutting-edge ML and AI algorithms directly into the host system microcontroller.

A major advantage of NanoEdge™ AI Studio is that it requires no specific data science skills. Any software developer using the Studio can create optimal ML libraries and start embedding smart features into the source code from its user-friendly environment with absolutely no AI skills. The Studio can generate four types of libraries: anomaly detection, outlier detection, classification, and regression libraries.

An Anomaly Detection (AD) library can be generated using a minimal amount of data examples which show normal and abnormal behaviours to train the system by example. Once created the library can be loaded into the target systems microcontroller to train and interpret directly on the device. The library learns the equipment behaviour from data acquired locally and dynamically adapts to each equipment behaviour. Once trained, the library inference compares data coming from equipment over time against the locally created models allowing it to accurately identify and report anomalies.

Outlier Detection (1C) can be used to detect any abnormality with the one-class classification method. This is especially useful when no examples of abnormal behaviours can be provided. In this instance the normal expected signal is imported into the Studio and then developers can easily create an optimized outlier detection ML library.

A Classification (nC) library is used to classify a collection of data, each representing different types of equipment defects (such as bearing problems, cavitation problems or others), or distinct types of events in the equipment environment. The developer imports the reference signals for each type of defect into the Studio and, in just a few steps, they can create a classification ML library that gathers all this knowledge into a single library. When this library is subsequently run on the microcontroller, the classifier analyses the live data and indicates the percentage of similarity against the static reference knowledge. The classification library is an immensely powerful tool, in a smart industrial setting it can not only provide quick fault detection but can also give an indication of the type of fault detected reducing diagnosis and repair time.

And finally, the Regression (E) algorithm can be used to extrapolate data and predict future data patterns. In this instance the developer imports reference signals and target values in the desktop Studio tool and in a few steps can generate a smart library to, for example, improve energy management or to forecast the remaining lifetime of equipment.

These ML libraries are powerful tools in their own right and can also be combined and chained: anomaly or outlier detection to detect a problem on the equipment, classification to identify the source of the problem, and regression to extrapolate information and provide real insight to the maintenance team. Typically designs will start with one library and then move onto using more libraries adding more value and capability on subsequent versions, see figure 1.

Fig.1 – Adding more value

All this learning and inference are done directly inside the microcontroller by means of the NanoEdge™ AI self-learning library, which streamlines the AI process and significantly reduces development effort, cost and time to market.

For a typical use case example in industrial equipment, ML can provide the key to avoiding breakdowns by helping to predict when and where a failure might occur. It allows preventative maintenance and service schedules to be optimised so they can be conducted when convenient and targeted correctly at the equipment or subsystem which is most in need.

Making sense of Sensors
Of course, to implement ML in an application, the equipment will need appropriate sensors to detect the required environmental conditions. These input sensor signals can range from vibration to pressure, sound, magnetic and time of flight just to name a few. By utilising STMicroelectronics wide range of MEMS and sensor technologies, which includes smart sensors with integrated ML, developers can cover a full spectrum of applications from low-power devices for IoT and battery-operated applications to high-end devices for accurate navigation and positioning, Industry 4.0, augmented virtual reality components and consumer devices. The NanoEdge™ AI Studio provides developers with maximum flexibility by allowing multiple sensor inputs to be combined, either in a single library, or using multiple libraries concurrently.

Getting started
To enable wider adoption of ML Technology, STMicroelectronics have released a demonstration version of the NanoEdge™ AI Studio which is available completely free for three months for developers to experiment with. The PC-based push-button development studio is compatible with Windows® or Linux® Ubuntu® operating systems and supports several STM32 Nucleo boards and Qr codeDescription automatically generatedDiscovery Kits such as the STEVAL-STWINKT1B SensorTile wireless industrial node development kit, this kit also supports the datalogging feature embedded in the NanoEdge™ AI Studio. The software can be downloaded for free by registering on the STMicroelectronics website, click or scan the QR code to register and download now. 


Once initial investigation using the demonstration version of NanoEdge™ AI Studio has been completed users then have the option to purchase an annual Single user development license or Team development license which works with all STM32 microcontrollers. To help build prototypes or proofs of concept faster, with limited risk and investment and maximum chance of success, developers are also able to purchase an Edge AI Sprint Package, this is a bundle that includes training sessions, a NanoEdge™ AI Studio license, and technical support.


The libraries generated with NanoEdge™ AI Studio production licenses can run on any STM32 microcontrollers during development and are subject to licensed conditions for production.

Design support
Anglia offers support for customer designs with free evaluation kits, demonstration boards and samples of STMicroelectronics products via the EZYsample service which is available to all registered Anglia Live account customers.

Anglia’s engineering team are also on hand to support designers with their extensive experience of ML and AI commonly being used in Condition based Monitoring (CbM) and Predictive Maintenance (PdM) based designs and can offer advice and support at component, software and system level. This expertise is available to assist customers with all aspects of their product design, providing hands on support and access to additional comprehensive STMicroelectronics resources including tutorial videos, technical application notes and reference designs.


Visit to see the full range of STMicroelectronics products available from Anglia.

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