NanoEdge AI Studio V3 - Anomaly Detection demo

Summary

The video introduces Nano Aji Studio, a tool that enables developers to integrate machine learning into C code without prior knowledge in the field. It focuses on creating an anomaly detection project using a 3-speed USB fan and the ST Win board. The step-by-step process covers project creation, data logging, signal analysis, library benchmarking, and deployment. The system adapts to changing conditions and can detect abnormal behaviors in real time. With embedded learning, the Nano AI libraries provide efficient, adaptable solutions for various use cases.

Takeaways

• 💻 Nano Aji Studio is a search engine for machine learning libraries, enabling developers to embed smart features into C code.
• 📊 The studio offers four types of projects: anomaly detection, one-class classification, n-class classification, and extrapolation.
• 🛠️ A project example involves using the ST Win board to monitor vibration patterns on a three-speed USB fan.
• 🔧 The Data Logger feature allows for easy configuration and logging of data from sensors like accelerometers.
• 📈 Anomaly detection is demonstrated by collecting data representing normal fan behavior and using it as a baseline for comparisons.
• 🚀 The studio benchmarks Nano AI libraries, optimizing pre-processing, machine learning models, and hyperparameters to find the best fit for a use case.
• 🎛️ Nano AI libraries are adaptable and can be trained incrementally in the microcontroller to detect anomalies in real-time.
• 🔄 The learning process can be dynamic, allowing for continuous adaptation to new conditions or regimes on the machine.
• 🧠 The main focus of Nano AI libraries is embedded learning, making them highly effective in detecting anomalies that haven't been seen before.
• 📦 After benchmarking, developers can deploy their custom Nano AI library and test it on real devices like the ST Win board.

Q & A

What is Nano Aji Studio?

Nano Aji Studio is a search engine for machine learning libraries that allows developers to easily embed smart features into C code. It provides a named AI Library optimal for a given use case with minimal data input.

What types of projects can be created with Nano Aji Studio?

Nano Aji Studio supports four types of projects: anomaly detection, one-class classification, n-class classification, and extrapolation.

How does the anomaly detection project work?

The anomaly detection project provides a dynamic model that learns patterns incrementally and infers directly within the target microcontroller, identifying deviations from learned patterns.

What is the purpose of the Data Logger feature in Nano Aji Studio?

The Data Logger feature allows users to configure their STM32 boards to log sensor data easily. It simplifies the process of collecting data for machine learning model training.

What is the process for creating a new anomaly detection project in the script?

The process involves defining general project settings, importing signals representing normal and abnormal behaviors, running a benchmark to find the best AI library for the use case, and then deploying the library to the target device.

How does the script demonstrate the use of an accelerometer with the STM32 board?

The script demonstrates using an accelerometer by placing an STM32 board on top of a USB fan to monitor vibration patterns, capturing raw data, and using it as input for the anomaly detection project.

What is the significance of the buffer size and sampling frequency in the data logging process?

The buffer size of 256 XYZ samples and a sampling frequency of 833 Hz ensure that each buffer represents approximately 300 milliseconds of data, which is sufficient to capture the essence of the fan's vibration patterns.

How does the script simulate abnormal behavior for the anomaly detection project?

The script simulates abnormal behavior by changing the orientation of the fan by 90 degrees to introduce unusual vibration patterns, which are then used as input for the anomaly detection model.

What performance indicators are used to evaluate the anomaly detection library?

The performance indicators used are balanced accuracy, which measures the library's ability to correctly classify signals as normal or abnormal, and confidence, which reflects the mathematical distance between normal and abnormal signals.

How is the final library tested before deployment?

The final library is tested using an emulator that allows for easy testing without needing to download, link, or compile anything. It involves initializing the library, running an initial learning cycle, and then performing inference to check for anomalies.

What adaptability does the Nano AI library offer for anomaly detection?

The Nano AI library offers adaptability through embedded learning, allowing the library to learn new patterns and adapt to changes in the operational regime of the device, making it suitable for dynamic environments.

Outlines

00:00

🔍 Introduction to Nano AI Studio and Project Overview

Nano AI Studio is a powerful tool designed for developers to easily embed machine learning features into C code. It outputs a small AI library optimized for specific use cases. This video demonstrates creating an anomaly detection project using a USB fan and the ST WIN board to monitor vibration patterns. Nano AI Studio offers four project types: anomaly detection, one-class classification, n-class classification, and extrapolation. The tutorial begins with a quick overview of the platform and its features, including use case inspirations and how the studio performs on various datasets.

05:00

⚙️ Initial Project Setup and Data Collection

In this step, the project is set up to monitor normal fan behavior at speed one using an accelerometer. The focus is on selecting the ST WIN sensor tile board and configuring it for data logging. The tutorial explains how to import data using three different methods (file, SD card, or serial). For this demo, the serial input method is used to collect normal fan vibration data. The tutorial emphasizes that for more complex applications, data representing all possible states (e.g., different fan speeds) could be logged and concatenated.

10:00

🔄 Collecting Abnormal Data and Preparing for Training

The tutorial moves on to simulate abnormal fan behavior by changing the fan's orientation. This is done to introduce abnormal vibration patterns, which are collected and imported as signal examples. Importantly, these abnormal signals are not used for training but rather to provide context for the studio's search during library creation. The tutorial highlights the adaptive nature of Nano AI libraries, which can detect unseen anomalies during real-world use.

15:02

🏁 Running Benchmarks to Find the Best Library

A benchmark is run to find the best Nano AI library for anomaly detection. The process involves testing many possible library configurations, combining signal pre-processing, machine learning models, and hyperparameters. Key performance indicators include balanced accuracy, confidence, and resource usage (RAM and Flash memory). The tutorial explains how to interpret the benchmark results, aiming for high similarity for normal signals and low similarity for abnormal ones, while optimizing for memory efficiency.

🤖 Testing the Anomaly Detection Library

The newly created library is tested using the ST WIN board and fan setup. After an initial learning phase, the library successfully detects normal and abnormal fan behavior in real time, including subtle anomalies like obstructed airflow. The tutorial highlights the library's adaptability through embedded learning, allowing it to adjust to new norms dynamically. Developers can easily implement more complex learning strategies, such as learning multiple regimes or enabling user-triggered learning.

🚀 Deploying the Nano AI Library

The final step involves deploying the Nano AI library onto a device. The tutorial demonstrates how to compile and integrate the library into an STM32CubeIDE project. Simple pseudocode is provided to illustrate how the library is used for initialization, learning, and detection in an infinite loop. The tutorial concludes by showcasing how easy it is for developers to deploy smart features into their devices without needing extensive knowledge of machine learning or data science.

Keywords

💡Nano Aji Studio

Nano Aji Studio is a search engine for machine learning libraries designed to simplify the process of embedding AI capabilities into C code. It allows developers to create projects that can learn and infer patterns directly within microcontrollers. In the video, it is used to create an anomaly detection project from scratch, demonstrating how to integrate smart features into devices without prior knowledge in machine learning.

💡Anomaly Detection

Anomaly detection is a machine learning application that identifies patterns in data that do not conform to expected behavior. In the context of the video, it is used to monitor vibration patterns of a USB fan to detect any abnormal operation. The project created in Nano Aji Studio learns normal patterns and then infers whether new data points are normal or anomalous.

💡STD WIN Board

The STD WIN Board is a development board used in the video to monitor vibration patterns. It is equipped with various sensors, including an accelerometer, which is used to collect data for the anomaly detection project. The board is connected to a PC to configure the Data Logger and to collect data that will be used to train the AI model.

💡Data Logger

A Data Logger in this context is a feature that allows for the collection of sensor data, which can be used for logging and analyzing over time. In the video, it is mentioned as a feature of the STD WIN Board, which can be configured to log data from selected sensors. The data is then downloaded to an SD card in a specific format for use in training the AI model.

💡Microcontroller

A microcontroller is a small computer on a single integrated circuit that is used to control a specific operation in an embedded system. In the video, the microcontroller is the target environment where the AI library will be embedded after being trained, allowing the device to perform anomaly detection on the fly.

💡Accelerometer

An accelerometer is a type of sensor that measures acceleration and can be used to detect vibrations. In the video, the accelerometer on the STD WIN Board is used to collect vibration data from the USB fan, which will be analyzed by the anomaly detection AI library to identify any abnormal patterns.

💡Inference

Inference in machine learning refers to the process of making predictions or decisions based on a trained model. In the video, inference is used to determine whether new vibration data from the fan is normal or abnormal after the AI library has been trained on initial data.

💡Buffer Size

Buffer size refers to the amount of data that can be held in a buffer, which is a temporary data storage area. In the video, a buffer size of 200 is mentioned, meaning each data set consists of 256 XYZ samples collected from the accelerometer. This size determines the amount of data processed at one time by the AI library.

💡Sampling Frequency

The sampling frequency is the rate at which data is collected per second, measured in Hertz (Hz). In the video, the accelerometer has a sampling frequency of 833 Hz, meaning it collects data 833 times per second. This frequency determines the resolution of the data collected for analysis.

💡Signal Pre-processing

Signal pre-processing is the initial treatment of raw data to make it suitable for further analysis. In the video, it is mentioned as an optional step in the AI library composition, where data can be normalized or filtered to remove unwanted frequency components before being used for training or inference.

💡Hyperparameters

Hyperparameters are configuration settings of a machine learning model that are set before the model is trained. In the video, hyperparameters are part of the AI library composition, where they are optimized to best solve the anomaly detection problem by adjusting settings like the decision boundary for classifying signals as normal or abnormal.

Highlights

Nano Aji studio is introduced as a search engine for machine learning libraries, simplifying the integration of AI into C code.

The tool requires minimal data input and outputs an AI Library tailored to specific use cases.

A new anomaly detection project is demonstrated using the STD wind board to monitor a USB fan's vibration patterns.

The homepage of Nano Aji studio is showcased, highlighting the creation of new projects and available project types.

The inspiration section on the studio's homepage provides use case ideas and links to the use case explorer.

Four types of Nano AI projects are explained: anomaly detection, one class classification, n-class classification, and extrapolation.

The Data Logger feature is described, allowing for easy configuration and data logging from sensors.

A simple three-speed USB fan is used as a model to simulate an industrial vibrating machine for the project.

The process of creating a new anomaly detection project in the studio is detailed, starting with general settings.

Signals representing normal fan behavior are imported from the serial port, using a specific buffer size and sampling frequency.

Abnormal behavior signals are collected by altering the fan's orientation to introduce different vibration patterns.

The Benchmark step is explained, where the studio searches for the best AI library based on the provided normal and abnormal signals.

The Nano AI library is composed of a pre-processing algorithm, a machine learning model, and a set of hyperparameters.

The importance of providing context with abnormal signals during the Benchmark step is emphasized for the automatic search process.

The performance indicators for anomaly detection libraries, such as balanced accuracy and confidence, are discussed.

The process of testing the library using an emulator without downloading or compiling is demonstrated.

The final deployment step is shown, where the Nano AI library is compiled and ready to be integrated into the target device.

The adaptability of the Nano AI library due to embedded learning is highlighted, allowing for dynamic updates to what is considered normal behavior.

The Nano AI documentation on the ST Wiki is mentioned as a resource for further information about the studio and libraries.

Transcripts

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Nano Aji studio is a search engine for

machine learning libraries it provides

any developer with a simple way to start

embedding smart features into any c code

this tool takes as input minimal amounts

of data and as output provides a naned

AI Library which is optimal for a given

use case today we will get familiar with

the studio by creating a new anomaly

detection project from scratch we will

use the STD wind board to monitor some

vibration patterns on a three-speed USB

fan here's the studios homepage where

you can create new projects out of the

four available

types below is the list of all available

projects on the right side the

inspiration section lists many use case

ideas which link directly to st's use

case

Explorer there you will find out how nji

Studio performs on these data sets which

are all available to download for

free there are four types of Nano AI

projects anomaly detection which

provides a dynamic model that learns

patterns incrementally and infer both

directly within the target

microcontroller one class classification

which is used to detect outliers within

data and is especially useful when no

examples of abnormal behaviors can be

provided on the system

n-class classification which enables

automatic identification of a machine

State among many different possible

States and extrapolation which uses

mathematical regression models in order

to estimate a Target value using other

known

features Additionally the Data Logger

feature provides a quick and easy way to

configure your St win data loger simply

connect your STD win and STD link to

your PC as indicated and click load

binary then click configure Data Logger

and select the sensors that you wish to

use for data logging as well as their

configuration finally click download

configuration to get the device

config.js file which needs to be copied

to the root of your SD wins SD

card you're now ready to log some data

onto your s wins SD card in that format

D of course this is just one way out of

many to log some data for instance today

we will not use this feature but instead

a more standard serial Data Logger

procedure let's get started with the

creation of our smart vibration

sensor here we have a very simple

three-speed USB fan that will be used as

a basic model to simulate an industrial

vibrating

machine we also have the St winds sensor

tile board the reference is St eval St

win KT

1B this board features many different

sensor types but today we'll only use

the

accelerometer we will just place the St

win on top of the fan and secure it

using some blue

tack at the moment on the St win we have

some very basic data logging code

there's no intelligence yet the logger

reads raw accelerometer data as XY Z

instantaneous samples and then formats

it into a buffer which is nothing more

than a collection of many XYZ samples

which we also call a signal example or a

learning

example and finally it outputs this

buffer or signal example directly to the

serial port or

USB this serial stream of buffers can be

read through the terminal and of course

we will use it as an input to provide

data to the

studio here we're using a buffer size of

200 156 which means that each line is

composed of 256 XYZ

samples the sampling frequency used on

the accelerometer is 833 Herz it means

that each buffer represents a temporal

signal of approximately 300 milliseconds

which seems to be sufficient to capture

the essence of the Fan's vibration

patterns let's create a new anomaly

detection

project in the first step we Define the

general setting of our project we'll

call it fan

vibration then we select the St win in

the list of available

boards the Nano AI libraries are of

course compatible with any stm32 board

with a cortex M

microcontroller and we choose our sensor

a 3axis

accelerometer the Nano AI libraries are

completely sensor agnostic meaning that

we can use any sensor type or even a

combination of several

sensors for instance we could have used

a combination of accelerometer 3 AIS

gyroscope 3 AIS and temperature sensor

which is only one variable for a

combined total of seven axes or seven

variables for this we would have

selected a generic sensor with seven

axes we could also decide to restrict

the maximum amounts of RAM and Flash

memories that our library will use if we

have specific constraints in our

application but today it's not the case

so we'll just leave it as it is and even

increase the maximum amount of ram to 32

kiloby because we don't want to limit

the search space in any way during

Benchmark then we move on to step number

two where we will import signals

representing the normal behaviors of the

fan to add signals we can import from

three different sources from file in

text or CSV format

from an SD wins data loggers SD card in

that format as explained briefly

earlier in which case we select a sensor

we choose a buffer

size and we import the

data or finally we can choose serial

from USB which is what we'll do

today we plug the S2 in select the right

Port the B rate and and turn the fan on

at speed number

one we'll consider that the normal

behavior of the fan is when it is

running at speed number one then we can

start collecting signals we'll stop

after we've got approximately 30

buffers here for the Simplicity of the

demo we only collect data representing

one single mode of operation or regime

on Ouran which is speed number one but

of course we could gather data

representing all the different states

that we' like to consider as the norm on

our machine it could be for instance

signals representing all speeds not only

one but also number two and three in

that case we would just concatenate all

signal examples representing all the

normal regimes in one single nominal

signal

file then we click continue and make

sure that our data looks fine and click

import the signal screen summarizes the

contents of the data we've just logged

on the right hand side we have previews

of the imported data with both temporal

and frequency plots or ffts for all

sensor axes so here we have the X Y and

Z accelerometer

axes it is possible to use a manual

filter if we'd like to remove some of

the unwanted frequency components within

our

signal just click filter settings

activate the

filter then input the sampling frequency

us used here we are at 833 Herz and

choose the low and high cut off

frequency values for example we may want

to remove all frequencies above 200

Hertz like

so and we can see how our changes are

reflected on the fft

plots but today we don't want to filter

frequencies so we'll just deactivate the

filter then we move on to the third step

where we import signals representing

abnormal behaviors on our system we

follow the exact same procedure as

before but this time we'll change the

behavior of a fan so that it functions

in a way that is

unusual for Simplicity we will simulate

this abnormal behavior by changing the

orientation of the fan by 90° to

introduce abnormal vibration patterns we

turn the fan on and collect

approximately 30 signal

examples the signal examples we provide

at this point are not used to train the

library it is very important they are

only used to provide some context for

the automatic search that will happen in

the next step because all the learning

will happen later on in the

microcontroller after the library has

been embedded on the target machine the

examples of anomalies provided here

don't need to be exhaustive for a real

use case for instance they could be

collected on a faulty machine or in

conditions that are known to be

abnormal or or depending on the case

they could even be created

artificially when testing our library we

will see that in fact we are able to

detect anomalies that were never seen

before because the embedded learning is

what makes Nano AI libraries especially

for anomaly detection particularly

adaptable let's import these abnormal

signals check our graphs and

proceed here we get to the four step

Benchmark we click run new Benchmark

select our couple of signals both Normal

and abnormal the number of cores we wish

to use on the CPU and

start at this point the studio will

start working hard to find the best

possible library for our use case given

the signals that we provided the

nanoedge library is composed of three

main blocks first there's an optional

signal pre-processing algorithm this

could be very simple such as offsetting

or normalizing or more complex like fft

or PCA and so

on second we have the choice of the

machine learning model in the pool of

available models we have Classics of

machine learning such as K&N svm neuron

networks and so on but we also have more

specific models developed inhouse and

patented by STD which were designed for

microcontrollers and are therefore

extremely well

optimized and third we have a set of

hyperparameters for the machine learning

model so that it can solve the problem

at hand as effectively and efficiently

as

possible so for each Library made of

these three blocks pre-processing

machine learning model and

hyperparameters there are many

possibilities which results in millions

of possible combinations and millions of

possible

libraries during Benchmark each

candidate library is tested using the

couples of signals imported previously

we learn the normal signals and then run

inference on both the Normal and

abnormal signals this whole process

happens many times for each Library

using random subsets of the data

provided to minimize any possible

bias the results are displayed here on

this graph on the xais there's the

signal number and the Y AIS is the

percentage of similarity which is the

raw output of the inference or detection

function of course would like all the

blue points with which corresponds to

the normal signals imported in step two

to be as close to 100% similarity as

possible and would like all the red

points the abnormal signals from step

three to be close to

0% the most important performance

indicator for the anomaly detection

candidate library is the balanced

accuracy which translates the ability of

the library to correctly attribute the

input signals to the correct class

either normal or

abnormal one 100% accuracy would mean

that all the blue points are above the

90% decision boundary the gray dash line

here and all red points are

below the second performance indicator

is confidence which translates how much

mathematical distance the library is

able to put between the Normal and

abnormal

signals 100% confidence would mean that

all the blue points are at exactly 100%

similarity while all the red points are

exactly at

zero finally the amount of RAM and Flash

memories are also optimized previously

in Project settings we put a constraint

on maximum RAM at 32 kiloby but here we

see that the best Library found so far

only takes much much

less The Benchmark process can take

anywhere from a few minutes to a few

hours here we've already obtained

excellent results so we'll stop The

Benchmark

manually now we have our best Nano AGI

Library

which is the only one kept by default

but we can also access the list of the

best candidate libraries found during

Benchmark by clicking

here we may select another one for

example one which has lower memory

Footprints despite slightly worse

accuracy or

confidence before testing our library

let's have a quick look at the functions

that it

provides for anomaly detection we have

the initialization function which needs

to be run first

first then we have the learn function

which is incremental Dynamic and can be

called at any time and finally the

inference function detect which outputs

a similarity percentage which measures

how much any given signal matches with

what was learned

before now we can test our library using

the

emulator each library has its own

emulator which allows for easy testing

without the need to download link or

compile anything we first initialize our

library and then learn some basic

knowledge we'll use our serial Data

Logger turn the fan on at speed number

one and start logging data we'll take

approximately 15

signals remember that anomaly detection

libraries come untrained out of the

Benchmark that's why we need to run an

initial learning cycle first

now that we have our reference knowledge

let's go to detection to see how the

library

behaves again we'll use serial

data and here the inference results

shows 100% similarity which was expected

since our fan is still running normally

now if we start tapping on the fan the

similarity drops

instantly same if we change the fans

orientation and back to normal if we

leave it

alone our library is behaving as

expected so we can move on to the final

step

deploy here we have a few developer

options available for the S win we only

need to check the first

one and then we can click the compile

button we will get a zip file

which contains the Nano Library which is

a static precompiled C language Library

here lib ne. a as well as the header

file Nano

A.H we need to import both of these into

our favorite

IDE here I'm using stm32 Cube

IDE and I will recompile my S wi to

transform it into a smart device and

we'll be ready to test it

here's a quick overview of the code that

we've prepared for our

device these are just Snippets of pseudo

code it is not full working

code first we initialize the

library then we run the initial learning

phase with only 15

iterations each time we get fresh

accelerometer data and pass it to the

learn

function after that we have the detect

function which runs in in an infinite

loop as soon as the learning is

over here we take the inference output

similarity and if it's higher than 90%

we say that everything is normal

otherwise under 90% we say anomaly of

course this is the simplest possible

logic and we could have coded something

much more

elaborate now that the S win is flashed

and the device ready let's see how it

works we turn the fan on reset the board

learning is in

progress and

finished everything is nominal and if we

change the orientation we start getting

anomalies and back to the original

states we're back to

nominal now let's introduce a more

subtle anomaly which was never seen

before we'll simulate that with a piece

of paper obstructing the airflow of the

fan

when we approach the paper the

similarity drops instantly and we get

anomalies as

expected note that here the piece of

paper is not even touching the

fan now the most important aspect of the

Nano library is its adaptability due to

the embedded learning for instance if we

suddenly change regimes and decide that

this is the new Norm we just have to

reset the board and learn this instead

as the new

norm and we get a nominal

result if we change back to the normal

orientation which we haven't learned yet

as expected the library returns

anomalies here we could imagine more

complex learning strategies for example

learning multiple regimes or enabling

the user to start a complimentary

learning phase with the Press of a

button for example

so hopefully today we've demonstrated

how easy it is using nooi Studio for any

developer to start embedding smart

features into their devices without any

prior knowledge in machine learning or

data science and all that in less than

an hour for any information about the

studio or the libraries please refer to

the Nano AI documentation which is

available on the St Wiki

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