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Developing Analytics to Predict Maintenance Needs for Electric Utility Assets

GE Digital is a leader in digital transformation for the energy network, providing greater insight, oversight, and foresight, enabling renewable energy penetration, reducing emissions, increasing plant productivity, and adapting to demand.

The Tauber Team worked with the GE Digital team to develop a Machine Learning tool that will monitor power generation equipment and provide operations and maintenance technicians with an estimation of the time until a maintenance activity is required.

The purpose of this new tool is to provide useful insights to GE and its partners, allowing plants to better schedule maintenance activities and improve up-time.

View team project summary


Nicholas Paris – EGL (BSE Computer Science & MS Data Science)

Morgan Sadler – Master of Business Administration

Aseem Tuli – Master of Science in Engineering in Industrial & Operations Engineering



Devang Gandhi – Senior Staff Data and Analytics Scientist

Mahesh Asati – Senior Director of Data and Analytics

Matt Schnugg – VP Data Science and Analytics Engineering



Seth Guikema – College of Engineering

John Silberholz – Ross School of Business