Use of Qualitest’s machine learning for predictive maintenance solution to predict which transformer is likely to break one week ahead of time.
By predicting breakdowns more accurately, the company increased its ability to provide better SLA and operational efficiency.
In a short project time of 14 weeks, a proactive maintenance prediction solution was ready for rollout for different locations.
The company partnered with Qualitest to use its Machine Learning (ML) solution. The final model included three main components
(a) Device failure prediction per infrastructure category
(b) Root cause analytics
(c) Optimize predictive maintenance for each territory and infrastructure specifically for each electricity distribution facility.
Qualitest used complex data streams from multiple devices, transformers, and geo-location information. The main model of demand prediction was built using a machine learning model ensemble. The next phase included the use of a prediction modeling component for each territory. An optimized time-to-maintain for each device type was applied. The model components are now part of the company’s production environment.
Qualitest’s ‘AI-powered Machine Learning Predictive Analytics Solution’ proved to be fast, easy to use, and accurate when applied to complex data. The solution will enable the company to correctly predict transformer failure, increase SLA, and increase operational efficiency.