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Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index
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Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index

Yingjun Shen, Zhe Song and Andrew Kusiak
ArXiv.org
03/29/2022
DOI: 10.48550/arXiv.2203.16006
url
https://doi.org/10.48550/arXiv.2203.16006View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

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.

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