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ONLINE REAL-TIME MONITORING SYSTEM THROUGH USING ADAPTIVE ANGULAR-VELOCITY VKF ORDER TRACKING

机译:自适应角速度VKF订单跟踪的在线实时监控系统

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When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®.This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig:
机译:当旋转机器运行时,从机器获取的声机械信号能够显示其运行状态和机器状况。由于旋转操作而在周期性负载下的机械系统通常在测量中以正弦波的叠加来响应,这些正弦波的频率是参考轴速度的整数倍(或分数整数)。在本研究中,我们基于自适应角速度Vold-Kalman滤波阶次跟踪(AV2KF_OT)算法构建了一个在线实时机器状态监测系统,该算法是通过DSP芯片模块和LabVIEW®编码的用户界面实现的。本文简要介绍了计算方案的理论推导和数值实现。实验工作证明了使用开发的在线实时状态监视系统的有效性。它们是通过使用带有轴颈的转子测试台对流体引起的不稳定性涡流进行启动检测:

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