Long-Term Health Index Prediction for Power Asset Classes Based on Sequence Learning

Abstract

Utility companies have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current asset health index based on the current condition data. For long-term preventative asset management, it is highly desirable to predict asset health indices in the next few years, especially for asset classes in which the assets share similar electrical, mechanical and condition degradation characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to predict long-term health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other prediction methods.