AI Building Fault Detection

Automated fault detection and diagnostics for building energy systems

The Problem

“Detect Hidden Building Energy Faults in Real Time”

Organizations face these key challenges:

Faults persist for weeks/months because detection depends on complaints or sporadic manual trend review

Rule-based alarms are noisy and lack context, overwhelming operators and leading to alarm fatigue

Energy and maintenance teams cannot quantify fault impact (kWh/therms/peak kW) to prioritize limited technician time and capital

Impact When Solved

5–15% whole-building energy reduction with continuous fault detection and prioritized fixes30–70% reduction in time to detect and diagnose common HVAC/control faults versus manual review2–8% peak kW reduction and improved compliance with energy targets, M&V, and decarbonization reporting

The Shift

Real-World Use Cases

Predictive Maintenance Framework for Wind Turbine Blade Erosion

This is like putting a smart ‘health monitor’ on wind turbine blades so you can tell when their edges are wearing down long before they fail, and schedule service at the best time instead of waiting for breakdowns.

Time-SeriesEmerging Standard

8.5

AI Condition Monitoring for Wind Turbines

This AI framework monitors wind turbines to detect any problems early, helping to prevent energy losses.

anomaly detectionemerging

8.5