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Step 11.1: Uploading images (samples) to Edge Impulse and labeling objects
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
After collecting training and testing image samples, I uploaded them to my project on Edge Impulse. Then, I labeled each target object on the image samples. #️⃣ First of all, sign up for Edge Impulse and create a new project....

Step 11.2: Training the FOMO model on the faulty component images
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
After labeling target objects on my training and testing samples successfully, I designed an impulse and trained the model on detecting specialized components representing faulty parts causing mechanical anomalies in a production line. An impulse is...

Step 11.3: Evaluating the model accuracy and deploying the model
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
After building and training my object detection model, I tested its accuracy and validity by utilizing testing image samples. The evaluated accuracy of the model is 66.67>#/em###. #️⃣ To validate the trained model, go to the Model...

Step 12: Setting up the neural network model on Beetle ESP32-C3
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
After building, training, and deploying my neural network model as an Arduino library on Edge Impulse, I needed to upload the generated Arduino library on Beetle ESP32-C3 to run the model directly so as to detect sound-based mechanical anomalies with...

Step 13: Setting up the object detection model on FireBeetle 2 ESP32-S3
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
After building, training, and deploying my object detection model as an Arduino library on Edge Impulse, I needed to upload the generated Arduino library to FireBeetle 2 ESP32-S3 to run the model directly so as to recognize the specialized components...

Step 14: Running neural network and object detection models simultaneously to inform the user of the root cause of the detected anomaly via SMS
Instructions from Multi-Model AI-Based Mechanical Anomaly Detector
My Edge Impulse neural network model predicts possibilities of labels (operation status classes) for the given audio (features) buffer as an array of 2 numbers. They represent the model's "confidence" that the given features buffer corresponds...

Previously made Hotplate Project
Instructions from Makeshift Reflow Hotplate
This was the previous Hotplate project, which expanded the heating area by connecting the cloth iron's element to a copper plate. A mechanical mechanism called a thermostat was used to control the heating element of the original version of the hotplate....

Create the database and a table to store the collected data.
Instructions from Industry 4.0: An economical SAAS MES part 1
For readers looking to test the project with a MySQL database without incurring any costs, there are several free MySQL database hosting services available. These services typically offer a limited amount of resources for free, but it will perfect for...

INSERT and UPDATE Data in the Database Using POST Method.
Instructions from Industry 4.0: An economical SAAS MES part 1
Currently, we have a database with an empty table 'ACTIVITY'. A crucial step in our project is to create a PHP page that utilizes the POST method to save data sent from our sensors. For this project, we will create two pages: The first (post_sensor.php)...

Part 7 - Full Tutorial Video
Instructions from NOOBIX IOT
Now as you can see in the tutorial video, each time I toggle the button on my website, the LED on the NOOBIX board is also turning on and off. But we can also connect it to high voltage. Connect a 220V load at the output. Then we add a cable at the input...

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