  Fault detection in industrial plants is a hot research area as more and more
sensor data are being collected throughout the industrial process. Automatic
data-driven approaches are widely needed and seen as a promising area of
investment. This paper proposes an effective machine learning algorithm to
predict industrial plant faults based on classification methods such as
penalized logistic regression, random forest and gradient boosted tree. A
fault's start time and end time are predicted sequentially in two steps by
formulating the original prediction problems as classification problems. The
algorithms described in this paper won first place in the Prognostics and
Health Management Society 2015 Data Challenge.
