论文标题
转子零件的预后基于级联分类和在线预测能力指数
Prognosis of Rotor Parts Fly-off Based on Cascade Classification and Online Prediction Ability Index
论文作者
论文摘要
大型旋转机器，例如压缩机，蒸汽涡轮机，燃气轮机是许多工艺行业（例如能源，化学和发电）的关键设备。由于转子的高旋转速度和巨大的动量，离心力可能会导致转子零件的飞行，这给操作安全带来了巨大威胁。对潜在失败的早期发现和预测可能会阻止灾难性的植物停机时间和经济损失。在本文中，我们将旋转机器的运行状态分为正常，风险和高风险的，基于失败的时间。然后提出了分类算法的级联分类以分两个步骤预测状态，首先我们判断机器是正常还是异常状态。对于被预测为异常的时间段，我们将它们进一步分为风险或高风险状态。此外，传统的分类模型评估指标，例如混乱矩阵，真正的false精度，是静态的，并且忽略了在线预测动态和不均匀的错误预测价格。提出了一个在线预测能力指数（OPAI），以选择具有一致的在线预测和较小近距离预测错误的预测模型。现实世界数据集和计算实验用于验证提出方法的有效性。
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.
