C3 IoT Case Studies Enterprise AI for Predicting HVAC Chiller Failures: A Case Study

	Enterprise AI for Predicting HVAC Chiller Failures: A Case Study C3 IoT

Enterprise AI for Predicting HVAC Chiller Failures: A Case Study

C3 IoT

Technology Category	Analytics & Modeling - Machine Learning Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries	Education Equipment & Machinery
Applicable Functions	Maintenance
Use Cases	Building Automation & Control Predictive Maintenance
Services	Data Science Services System Integration
Challenge	The building systems division of a Fortune 500 manufacturer, which provides equipment and services for optimizing building energy expenditures, was facing a significant challenge. The division was conducting chiller maintenance reactively, leading to business disruptions, downtimes, and costly emergency repairs. This reactive approach was negatively impacting customer satisfaction. The manufacturer needed a solution that could rapidly integrate all relevant equipment and facility data sources. The goal was to reduce downtime and costly, unscheduled maintenance for its commercial Heating, Venting & Cooling (HVAC) chiller systems. Read More
About Customer	The customer in this case study is the building systems division of a Fortune 500 manufacturer. This division provides equipment and services that optimize building energy expenditures, ensuring comfort for customers. The company is a global equipment manufacturer and services company with 100,000 employees and $30 billion in revenue. The company's primary objective was to load and cluster sensor data for use in a predictive model, train a machine learning model to predict chiller failure, and demonstrate the speed of development and deployment by completing the project in less than a week. Read More
Customer Name	Fortune 500 Manufacturing Read More
Solution	To address these challenges, the manufacturer deployed C3 AI Reliability for 165 of its chillers. The customer chose C3 AI due to 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 just 4 days, C3 AI and the customer loaded, normalized, and mapped 3 years of sensor data for all 165 chillers. They 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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Platform as a Service (PaaS) - Application Development Platforms

Applicable Industries

Education

Equipment & Machinery

Applicable Functions

Maintenance

Use Cases

Building Automation & Control

Predictive Maintenance

Services

Data Science Services

System Integration

Challenge

The building systems division of a Fortune 500 manufacturer, which provides equipment and services for optimizing building energy expenditures, was facing a significant challenge. The division was conducting chiller maintenance reactively, leading to business disruptions, downtimes, and costly emergency repairs. This reactive approach was negatively impacting customer satisfaction. The manufacturer needed a solution that could rapidly integrate all relevant equipment and facility data sources. The goal was to reduce downtime and costly, unscheduled maintenance for its commercial Heating, Venting & Cooling (HVAC) chiller systems.

About Customer

The customer in this case study is the building systems division of a Fortune 500 manufacturer. This division provides equipment and services that optimize building energy expenditures, ensuring comfort for customers. The company is a global equipment manufacturer and services company with 100,000 employees and $30 billion in revenue. The company's primary objective was to load and cluster sensor data for use in a predictive model, train a machine learning model to predict chiller failure, and demonstrate the speed of development and deployment by completing the project in less than a week.

Customer Name

Fortune 500 Manufacturing

Solution

To address these challenges, the manufacturer deployed C3 AI Reliability for 165 of its chillers. The customer chose C3 AI due to 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 just 4 days, C3 AI and the customer loaded, normalized, and mapped 3 years of sensor data for all 165 chillers. They 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.

Impact #1	The deployment of C3 AI Reliability led to significant operational improvements. The manufacturer was able to rapidly integrate sensor data, normalize and cluster disparate readings, and run machine learning algorithms to identify deteriorating conditions before failures occur. This proactive approach to maintenance reduced business disruptions and downtimes, leading to improved customer satisfaction. The project was completed in less than a week, demonstrating the speed of development and deployment. The machine learning model achieved a precision of 73% and a recall of 71%, exceeding the identified accuracy and precision targets.

Impact #1

The deployment of C3 AI Reliability led to significant operational improvements. The manufacturer was able to rapidly integrate sensor data, normalize and cluster disparate readings, and run machine learning algorithms to identify deteriorating conditions before failures occur. This proactive approach to maintenance reduced business disruptions and downtimes, leading to improved customer satisfaction. The project was completed in less than a week, demonstrating the speed of development and deployment. The machine learning model achieved a precision of 73% and a recall of 71%, exceeding the identified accuracy and precision targets.

Benefit #1	$178M annual benefit identified
Benefit #2	Project completed in 4 weeks from receiving data to model delivery
Benefit #3	Achieved 73% model precision

Benefit #1

$178M annual benefit identified

Benefit #2

Project completed in 4 weeks from receiving data to model delivery

Benefit #3

Achieved 73% model precision

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Overview

Enterprise AI for Predicting HVAC Chiller Failures: A Case Study

Operational Impact

Quantitative Benefit