Edge Impulse Experts / AI-driven HVAC Fault Diagnosis (Thermal) Public
Target: Cortex-M4F 80MHz Clone this project

Test data

Set confidence thresholds
Model variants
Test 'Unoptimized (float32)' model Test 'Quantized (int8)' model
Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.
Sample name Expected outcome Anomaly Accuracy Result
sample__2024_06_01_16_57_48 anomaly 29.92 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_04_38 anomaly 32.87 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_14_53 anomaly 30.21 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_50_58 anomaly 30.14 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_01_13 anomaly 31.74 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_37_18 anomaly 28.96 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_11_28 anomaly 28.28 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_30_28 anomaly 30.99 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_54_23 anomaly 31.25 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_44_08 anomaly 30.61 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_40_43 anomaly 28.61 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_47_33 anomaly 30.82 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_33_53 anomaly 34.92 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_18_18 anomaly 30.17 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_17_08_03 anomaly 29.43 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_09_58 anomaly 33.06 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_16_48 anomaly 33.00 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_20_13 anomaly 38.96 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_39_13 anomaly 30.88 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_03_08 anomaly 29.94 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_56_18 anomaly 31.85 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_46_03 anomaly 31.82 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_49_28 anomaly 32.46 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_13_23 anomaly 34.92 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_25_33 anomaly 33.36 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_22_08 anomaly 30.17 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_52_53 anomaly 29.40 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_28_58 anomaly 28.77 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_59_43 anomaly 34.31 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_42_38 anomaly 26.76 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_32_23 anomaly 27.46 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_06_33 anomaly 33.81 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_23_38 anomaly 27.32 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_16_27_03 anomaly 30.44 100% anomaly
Show classification Show in data acquisition
sample__2024_06_01_15_35_48 anomaly 28.43 100% anomaly
Show classification Show in data acquisition

Model testing output

Results

Model version:
Accuracy
100.00%

Confusion matrix

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Feature explorer

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