Enterprise AI for Predicting HVAC Chiller Failures
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Equipment & Machinery
Use Cases
- Predictive Quality Analytics
Services
- Data Science Services
The Customer
Not Disclosed
About The Customer
- Global equipment manufacturer and services company
- 100,000 employees
- $30 billion in revenue
The Challenge
- Load and cluster sensor data for use in a predictive model
- Train a machine learning model to predict chiller failure
- Demonstrate speed of development and deployment by completing project in < 1 week
The Solution
- Configured C3 AI Reliability in 4 days
- Loaded 3 years of sensor data for 165 HVAC chillers (40-50 sensor feeds per chiller)
- Developed 163 analytics as inputs for failure prediction algorithm
- Trained and tuned a machine learning model to predict chiller failures with 73% precision and 71% recall
Operational Impact
Quantitative Benefit
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