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An overview of the PES Pareto Method for decomposing baseline noise sources in hard disk position error signals

机译:分解硬盘位置误差信号中基线噪声源的PES Pareto方法概述

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This paper gives an overview of the PES Pareto Method, a useful tool for identifying and eliminating key contributors to uncertainty in the Position Error Signal (PES) of a magnetic disk drive servo system. Once identified and ranked according to their overall effect on PES, the top-ranking sources can be worked on first, either by finding ways to reduce their magnitude or by altering system components to reduce sensitivity to the contributors. The PES Pareto Method is based on three ideas: (1) an understanding of how Bode's Integral Theorem applies to servo system noise measurements, (2) a measurement methodology that allows for the isolation of individual noise sources, and (3) a system model that allows these sources to be recombined to simulate the drive's Position Error Signal. The method requires the measurement of frequency response functions and output power spectra for each servo system element. Each input noise spectrum can then be inferred and applied to the closed loop model to determine its effect on PES uncertainty. The PES Pareto Method is illustrated by decomposing PES signals that were obtained from a hard disk drive manufactured by Hewlett-Packard Company. In this disk drive, it is discovered that the two most significant contributors to PES baseline noise are the turbulent wind flow generated by the spinning disks ("Windage") and the noise involved in the actual readback of the Position Error Signal ("Position Sensing Noise").
机译:本文概述了PES Pareto方法,这是一种有用的工具,可用于识别和消除导致磁盘驱动器伺服系统位置误差信号(PES)不确定性的关键因素。一旦根据其对PES的总体影响对其进行了识别和排序,就可以首先找到排名靠前的来源,方法是找到降低其幅度的方法,或者通过更改系统组件以降低对贡献者的敏感性。 PES Pareto方法基于以下三个思想:(1)了解Bode积分定理如何应用于伺服系统噪声测量;(2)允许隔离单个噪声源的测量方法;以及(3)系统模型允许重新组合这些源以模拟驱动器的位置误差信号。该方法需要测量每个伺服系统元件的频率响应函数和输出功率谱。然后,可以推断出每个输入噪声谱并将其应用于闭环模型,以确定其对PES不确定性的影响。通过分解从惠普公司制造的硬盘驱动器获得的PES信号来说明PES Pareto方法。在此磁盘驱动器中,发现引起PES基准噪声的两个最重要的因素是旋转磁盘产生的湍流风(“ Windage”)和实际回读位置误差信号(“ Position Sensing”)时所涉及的噪声。噪声”)。

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