Detecting the failures that are worth preventing

Cover Photo by Nicholas Doherty on Unsplash

The adoption of a data-driven strategy for Predictive Maintenance empowers energy and utility industries. A successful Predictive Maintenance implementation can reduce maintenance costs by 25–35%, eliminate downtimes by 70–75%, reduce downtime by 35–45%, and increase productivity by 25–35%.

In this case study, an organization with a strong presence in the European and American markets wanted to reduce the resources committed to their systems' maintenance and operationalization.

Understanding the representativeness of failures in the training data was crucial for assessing performance impact, where the adoption of a Data-Centric AI approach combined with synthetic data generation to improve the training dataset, were vital for a better Predictive Maintenance strategy. 

Download this case study to learn more about:

• The impact of data quality in predictive maintenance & pro active failure detection
• The benefits delivered by synthetic data generation to balance underrepresented labels and meters behaviour