Edge Impulse Experts / AI-driven HVAC Fault Diagnosis (Thermal)
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About this project
This FOMO-AD visual anomaly detection model diagnoses thermal cooling malfunctions of HVAC system components based on thermal images:
- no anomaly
- anomaly
After building my visual anomaly detection model, I deployed my model as a fully optimized and customizable Linux (x86_64) application (.eim) and uploaded it to LattePanda Mu. Thus, the device is capable of diagnosing thermal cooling abnormalities based on the specifically produced thermal images by running the visual anomaly detection model without any additional procedures, reduced accuracy, or latency.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Image training data | NPY file | 35 windows | |
| Image training labels | NPY file | 35 windows | |
| Image testing data | NPY file | 35 windows | |
| Image testing labels | NPY file | 35 windows | |
| FOMO-AD model | TensorFlow Lite (float32) | 69 KB | |
| FOMO-AD model | TensorFlow Lite (int8 quantized) | 50 KB | |
| FOMO-AD model | TensorFlow Lite (float32) - Model head | 60 KB | |
| FOMO-AD model | Model evaluation metrics (JSON file) | 89 Bytes | |
| FOMO-AD model | TensorFlow SavedModel | 100 KB | |
| FOMO-AD model | Keras h5 model | 77 KB |
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Summary
Data collected
70 itemsProject info
| Project ID | 419123 |
| Project version | 1 |
| License | Apache 2.0 |
