  Power systems 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 or
near-future asset health index based on the current condition data. For
preventative asset management, it is highly desirable to estimate asset health
indices, especially for asset classes in which the assets share similar
electrical and/or mechanical characteristics. This important problem has not
been sufficiently addressed. This paper proposes a sequence learning based
method to estimate 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 Estimation methods.
