As explained earlier, Beetle ESP32-C3 does not have a secondary storage option for audio sample collection via the onboard I2S microphone. Therefore, I decided to capitalize on the phone microphone and internal storage via the Android application while...
After setting FireBeetle 2 ESP32-S3 and Beetle ESP32-C3 on the Arduino IDE, I programmed FireBeetle 2 ESP32-S3 to communicate with Beetle ESP32-C3 to obtain the commands transferred by the Android application via serial communication, capture raw image...
In any Bluetooth® Low Energy (also referred to as Bluetooth® LE or BLE) connection, devices can have one of these two roles: the central and the peripheral. A peripheral device (also called a client) advertises or broadcasts information about...
As explained earlier, I built a basic frequency-controlled apparatus (the X-axis timing belt system) to demonstrate mechanical deviations since I did not have the resources to conduct experiments in an industrial plant. Then, I utilized the specialized...
Since Edge Impulse does not support audio samples in the 3GP format, I needed to convert my audio samples to the officially supported WAV format for audio classification. Even though there are various methods to convert audio files, including online...
After collecting training and testing audio samples, I uploaded them to my project on Edge Impulse. #️⃣ First of all, sign up for Edge Impulse and create a new project. #️⃣ Navigate to the Data acquisition page...
After uploading and labeling my training and testing samples successfully, I designed an impulse and trained the model to detect sound-based mechanical anomalies. An impulse is a custom neural network model in Edge Impulse. I created my impulse by employing...
After building and training my neural network model, I tested its accuracy and validity by utilizing testing samples. The evaluated accuracy of the model is 100>#/em###. #️⃣ To validate the trained model, go to the Model testing page...
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....
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...
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...
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...
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...
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...
[1] Anomaly Detection in Industrial Machinery using IoT Devices and Machine Learning: a Systematic Mapping, 14 Nov 2023, https://arxiv.org/pdf/2307.15807.pdf. [2] Martha Rodríguez, Diana P. Tobón, Danny Múnera, Anomaly classification...