基于转子振动和扩展卡尔曼滤波器的滑动轴承缺油状态识别
Identification of oil starvation in hydrodynamic journal bearing using rotor vibration and Extended Kalman Filter
摘要：
油膜不足是流体动压轴承的一种严重故障，其原因是供油量不足。如果检测得太晚，这种故障会使轴承的性能恶化，并损坏旋转机器。因此，应采用早期故障识别技术，以避免这种情况，并允许有效的维护。然而，有关乏油故障识别的文献相对较新，非常缺乏。在为数不多的研究中，在早期阶段使用纯粹的确定性技术确定了缺油，这可能会受到模型不准确的影响。另外，本文提出了一种新的方法来识别油不足故障的液体动压轴承使用转子振动响应和扩展的卡尔曼滤波器（EKF），这是一个随机确定的状态估计器，可以处理建模和测量误差。本文中进行的计算模拟表明，所提出的方法是适合于成功地识别油不足故障的实时噪声振动响应，从而代表了一个很有前途的工具，用于监测旋转机械。
英文摘要：
Oil starvation is a critical fault in hydrodynamic bearings caused by insufficient oil supply flowrate. When late detected, this fault can deteriorate the bearings' performance and damage the rotating machine. Thus, early fault identification techniques should be applied to avoid this scenario and allow effective maintenance. However, the literature about the identification of oil starvation fault is relatively recent and very scarce. Among the few studies available, oil starvation has been identified in early stages using a purely deterministic technique, which can be jeopardized by model inaccuracy. Alternatively, this paper proposes a novel method for the identification of oil starvation faults in hydrodynamic bearings using the rotor vibrational responses and the Extended Kalman Filter (EKF), which is a stochastic-determinist state estimator that can deal with modelling and measurement errors. Computational simulations performed in this paper show that the proposed method is suitable to successfully identify the oil starvation faults in real-time even with noisy vibrational responses, thus representing a promising tool for the monitoring of rotating machines.
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