AI & Machine Learning and Diagnostics

AI & Machine Learning Diagnostics enhance traditional thermography with automated fault detection and advanced pattern recognition. Our models analyze thermal images, RGB photos, and metadata to classify defects, quantify risks, and predict failure trends.
By combining datasets and intelligent algorithms, we deliver faster, more consistent, and highly accurate diagnostic results that support proactive maintenance and data-driven decision-making.

What We Analyze

Our AI models evaluate thermal signatures, visual irregularities, electrical patterns, shading, module degradation, hotspots, and spatial correlations across the entire PV field.

Models & accuracy

Machine learning detects subtle anomalies that the human eye may miss—especially in large solar parks. It increases precision, reduces inspection time, and ensures consistent quality across repeated inspections.

Why ML Improves Inspection

Thermographic Solar Inspection provides a precise, non-invasive method to evaluate the health and performance of solar PV installations. Using high-resolution thermal imaging captured by drones, we identify:

hotspots
faulty modules
string failures
other hidden anomalies
before they escalate into costly downtime.
This service helps asset owners maintain optimal efficiency, extend equipment lifespan, and reduce operational losses through early detection and clear diagnostic insights.

Example outputs

We use object detection, segmentation, anomaly detection, and classification models trained on domain-specific datasets. Accuracy is continuously improved through feedback loops and real-world data integration.