Elder Research: Predictive Maintenance at Scale

Industry: Industrial Engineering
Solution: Unsupervised Machine Learning for Predictive Maintenance
Impact: Real-time monitoring of turbines, cost savings through early failure detection

Overview
When Elder Research—a leading machine learning solutions provider with over two decades of experience—was seeking support on a high-impact project in the hydroelectric power industry, they turned to Ishango to accelerate delivery.

Their goal? To develop a predictive maintenance model that could detect early signs of failure across turbines in a hydroelectric power plant.

The Challenge
Predicting equipment failure is no small feat—especially in real-time. Elder Research needed to process vast amounts of operational data from turbines and build a solution that could detect anomalies without relying on labeled failure data.

The Ishango Approach
Ishango’s team worked closely with Elder Research to design and implement an unsupervised machine learning model using Python, hosted on AWS cloud infrastructure. The model was trained to recognise subtle patterns and deviations in turbine behavior, enabling proactive maintenance scheduling.

The Result
The result was a robust, real-time monitoring solution capable of identifying potential part failures early—saving both time and operational cost.