  Large rotating machines, e.g., compressors, steam turbines, gas turbines, are
critical equipment in many process industries such as energy, chemical, and
power generation. Due to high rotating speed and tremendous momentum of the
rotor, the centrifugal force may lead to flying apart of the rotor parts, which
brings a great threat to the operation safety. Early detection and prediction
of potential failures could prevent the catastrophic plant downtime and
economic loss. In this paper, we divide the operational states of a rotating
machine into normal, risky, and high-risk ones based on the time to the moment
of failure. Then a cascade classifying algorithm is proposed to predict the
states in two steps, first we judge whether the machine is in normal or
abnormal condition; for time periods which are predicted as abnormal we further
classify them into risky or high-risk states. Moreover, traditional
classification model evaluation metrics, such as confusion matrix, true-false
accuracy, are static and neglect the online prediction dynamics and uneven
wrong-prediction prices. An Online Prediction Ability Index (OPAI) is proposed
to select prediction models with consistent online predictions and smaller
close-to-downtime prediction errors. Real-world data sets and computational
experiments are used to verify the effectiveness of proposed methods.
