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aj3944/power-equipment-fault-detector
power-equipment-fault-detector is a machine learning model from aj3944. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model detects and classifies high-voltage electrical transmission line faults and power equipment defects with 94.9% Top-1 Accuracy, trained on the Power Equipment Image Dataset using an NVIDIA RTX 5090 GPU.
Downloads · 30 days
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Updated Sep 3, 2026
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From the Hugging Face model README
This model detects and classifies high-voltage electrical transmission line faults and power equipment defects with 94.9% Top-1 Accuracy, trained on the Power Equipment Image Dataset using an NVIDIA RTX 5090 GPU.
| Class Code | Defect Name | Severity | Failure Physics & Corrective Action |
|---|---|---|---|
| czjyz_ps | Porcelain Post Insulator Breakage | CRITICAL | Loss of creepage distance; urgent emergency replacement to prevent arc flashover. |
| czjyz_sl | Porcelain Post Insulator Flashover | CRITICAL | Conductive carbonization track; high-pressure cleaning or replacement. |
| czjyz_lw | Porcelain Post Insulator Crack | HIGH | Mechanical fatigue fracture under wind vibration; schedule maintenance replacement. |
| dx_jypps | Conductor Wire Insulation Damage | HIGH | Core exposure; install repair armor sleeve over conductor. |
Download or view the included notebook power_equipment_fault_detection.ipynb to inspect dataset loading, training curves, and visualization.
Experience live predictions via the hosted Space: Power Equipment Fault Inspector