Case Study:
Eliminating Unpredictable HVAC Failure with Cognitive Anomaly Detection

By downloading this case study, you will learn how DataRPM improved the reliability of heating, ventilation and air conditioning units (HVACs) with minimal service disruptions through cognitive anomaly detection and failure prediction.

Business Challenge:

A leading HVAC manufacturer wanted to achieve high reliability and low maintenance cycles for its equipment by adopting a predictive maintenance solution. DataRPM had to:

  1. Find anomalies that could lead to system failure
  2. Create a predictive model that could identify anomalies before they appeared, facilitating corrective action before equipment breakdown

What We Did:

DataRPM’s CADP product identified previously unknown anomalies and predicted them early enough for corrective actions. With a 93% prediction accuracy in detecting equipment failure, the client was able to achieve annual maintenance cost savings of over $48 million.

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About DataRPM

DataRPM is an award-winning predictive analytics company focused on delivering the next generation predictive maintenance solutions for the Industrial IoT. DataRPM platform automates data science leveraging the next frontier in machine learning known as meta-learning, which is machine learning on machine learning. The platform increases prediction quality and accuracy by over 300% in 1/30th the time and resources delivering 30% in cost savings or revenue growth for business problems around predicting asset failures, reducing maintenance costs, optimizing inventory and resources, predicting quality issues, forecasting warranty and insurance claims and managing risks better.

Industry Recognitions and Awards

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