C3 IoT > Case Studies > Enterprise AI for Predicting HVAC Chiller Failures

Enterprise AI for Predicting HVAC Chiller Failures

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 Enterprise AI for Predicting HVAC Chiller Failures - IoT ONE Case Study
The Challenge

The building systems division of a Fortune 500 manufacturer provides equipment and services that provide comfort to customers while optimizing building energy expenditures. Historically, the building systems conducted chiller maintenance reactively which led to business disruptions and downtimes and costly emergency repairs that ultimately impacted customer satisfaction.

The manufacturer needed a reliable solution that rapidly integrated all relevant equipment and facility data sources and allow it to reduce downtime and costly, unscheduled maintenance for its commercial Heating, Venting & Cooling (HVAC) chiller systems.

The Customer

Fortune 500 Manufacturing

About The Customer

Fortune 500 manufacturing company with operations around the world. The business is active in both acquisitions and divestitures, making the IT infrastructure very dynamic.

  1. Global equipment manufacturer and services company
  2. 100,000 employees
  3. $30 billion in revenue
The Solution

The manufacturer deployed C3 AI Reliability for 165 of its chillers to address these objectives. The customer selected C3 AI for its proven ability to rapidly integrate sensor data, normalize and cluster disparate readings, and run machine learning algorithms to identify deteriorating conditions before failures occur.

In 4 days, C3 AI and the customer loaded, normalized, and mapped 3 years of sensor data for all 165 chillers, created custom analytics on these data, and configured a machine-learning algorithm to predict chiller failure events. C3 AI Reliability exceeded the identified accuracy and precision targets.

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