LAUSR

Large rotating machines 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 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 classification algorithm is proposed to predict the states in two steps; first, we determine whether the machine is in normal or abnormal condition; for time periods predicted as abnormal, we further classify them into risky or high-risk state. Moreover, traditional classification model evaluation metrics, such as confusion matrix and true–false accuracy, are static and neglect online prediction dynamics and uneven error-prediction prices. An online prediction ability index is proposed to select prediction models with consistent online predictions and smaller close-to-downtime prediction errors. Real-world data and computational experiments are used to verify the effectiveness of the proposed method.