Power generation involves a complicated systems of systems which depend on predictive maintenance to operate productively and within O&M budgets. But existing analytic methods make it difficult to identify suboptimal conditions or unique failure inducing behaviors and require significant effort and resources to maintain over time. With the rise of digitization and new asset technologies, how can maintenance teams take advantage of these changing industrial dynamics?

Digitization and advanced analytics present a significant opportunity to leverage asset data and improve power generating operations. Machine learning solutions are used in predictive maintenance to extract important machine insights automatically and amplify the value of subject matter experts. This webinar will provide an overview of how machine learning can be used to improve productivity and walks through several customer case studies that illustrate these concepts in action.

Attendees will learn:

1. What solution components are necessary for a machine learning approach to work well in their industrial operation
2. How machine learning supplements and fits into existing predictive maintenance strategies at the asset and plant-wide level
3. How machine learning predictive maintenance solutions can be implemented to drive short and long-term productivity