Senrode

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  Here's a quick glance to our dashboard of Senrode

Inspiration

Industrial equipment failures cost $50B annually, yet 70% of inspections rely on subjective visual checks. When a tire factory lost $180K from an undetected bearing failure, we realized the problem: no objective way to measure degradation. We built Senrode to turn smartphones into precision wear detection systems.

What it does

Senrode analyzes before/after images of any industrial part to detect and predict failures—no training data required. Upload two photos, get instant results:

• Visual damage heatmaps with severity scores
• Failure classification (corrosion, cracks, wear, deformation)
• Time-to-failure predictions based on degradation rate
• ISO-compliant inspection reports
• Batch processing for 100+ parts simultaneously
• Offline operation for remote sites
• CMMS integration for automated work orders

How we built it

Architecture: Physics-based computer vision, not deep learning. OpenCV for image alignment, SSIM for change quantification, perceptual hashing for degradation detection.

Classification: Domain knowledge encoded as rules—rust = red-brown clusters, cracks = new edge formations, wear = texture entropy loss.

Stack: React frontend, Flask API, TensorFlow Lite for mobile deployment, SQLite for asset history.

Key innovation: Zero-shot detection works on any part without training datasets.

Challenges we ran into

Image alignment: Different angles/lighting broke registration. Solved with multi-stage pipeline: SIFT feature matching → perspective transform → ECC intensity alignment.

Mobile performance: Initial 45-second processing time crushed to 3 seconds through aggressive optimization and lazy loading.

Validation: No ground truth for "when will this fail?" Partnered with automotive shop, backtested predictions against 6 months of actual failure logs.

Accomplishments that we're proud of

• Zero-shot generalization: Works on gears, concrete, circuits—anything—without retraining
• True offline capability: Full analysis on $200 phone with no internet
• Real validation: 8 maintenance managers reviewed it; one tire factory committed to 200-asset pilot
• Business proof: Cost calculator shows single prevented failure pays for 2 years of service

What we learned

• Domain expertise beats algorithm complexity—physics-based rules outperformed generic ML
• Users want ROI, not AI—lead with outcomes, not technology
• Visual explanations build trust—showing which pixels triggered alerts drove adoption
• Integration is the product—API connectors matter more than 2% accuracy gains

What's next for Senrode

3 months: Launch 200-asset pilot with tire manufacturer, build SAP/Maximo connectors, add thermal imaging support.

6 months: Expand to infrastructure (bridges, pipelines, pavement), add predictive maintenance scheduling.

12 months: Multi-sensor fusion (visual + thermal + vibration), industry-specific modules, satellite imagery for large-scale monitoring.

Vision: Become the visual intelligence layer for every physical asset globally—if it degrades, Senrode watches it.

Built With

• flaskapi
• react
• sqlite
• tensorflowlite