Edge Impulse Experts / AI-driven HVAC Fault Diagnosis (Audio)
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
This Audio MFE neural network model detects anomalous sound originating from the HVAC system cooling fans:
- normal
- defective
After building my neural network model, I deployed my model as a fully optimized and customizable Arduino library and uploaded it to XIAO ESP32C6. Therefore, the device is capable of detecting anomalous sound emanating from the cooling fans by running the neural network model onboard without any additional procedures or latency.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| MFE training data | NPY file | 180 windows | |
| MFE training labels | NPY file | 180 windows | |
| MFE testing data | NPY file | 36 windows | |
| MFE testing labels | NPY file | 36 windows | |
| Classifier model | TensorFlow Lite (float32) | 12 KB | |
| Classifier model | TensorFlow Lite (int8 quantized) | 7 KB | |
| Classifier model | TensorFlow SavedModel | 17 KB | |
| Classifier model | Keras h5 model | 11 KB | |
| Classifier model | Model evaluation metrics (JSON file) | - |
Clone project
You are viewing a public Edge Impulse project. Clone this project to add data or make changes.
Run this model
Scan QR code or launch in browser
Summary
Data collected
2m 57sProject info
| Project ID | 418121 |
| Project version | 1 |
| License | Apache 2.0 |
