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Microsystem Technologies

, Volume 23, Issue 8, pp 3097–3105 | Cite as

An effective system-level vibration prediction analysis approach for data storage system chassis

  • Jiaping Yang
  • Cheng Peng Tan
  • Zhimin He
  • Zhi Yong Ching
  • Chye Chin Tan
Technical Paper
  • 83 Downloads

Abstract

In modern data storage systems, multiple disk drives arrays are densely located within a chassis or rack. Meanwhile, the chassis houses power suppliers, cooling fans, PCBs and switch boards too. During operation, mechanical disturbances from disk drive arm rotary motions, cross-talks between disk drives and cooling fans can generate adversely influence on a data storage system performance. In particular, the voice coil motor (VCM) torque induced by disk drive seeking operation affects not only its own but also neighbouring drives in terms of hard disk drive (HDD) throughput performance. Vibration analysis plays an important role in chassis system design for robustness which can mitigate the mechanical vibrations impact on disk drive throughput performances. In this paper, we report a finite element simulation based approach to predict vibration responses of the storage system chassis excited by operating disk drives and cooling fans. The purpose is to provide an effective vibration prediction analysis method that could aid in evaluating system-level vibration characteristics for chassis designers. An analytical model is described to formulate disk drive vibration behaviours in terms of design parameters. A full finite element model including the whole chassis structure, HDDs and its mounting mechanism components, storage controller boards and backplane board is constructed for simulation study. The mechanical disturbances are characterised by measuring VCM torque spectrums and fan operational vibration behaviours. HDD vibration responses at system-level are computed and well compared with the measurement results.

Keywords

Mode Shape Disk Drive Hard Disk Drive Vibration Response Voice Coil Motor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jiaping Yang
    • 1
  • Cheng Peng Tan
    • 1
  • Zhimin He
    • 1
  • Zhi Yong Ching
    • 1
  • Chye Chin Tan
    • 1
  1. 1.Data Storage InstituteSingaporeSingapore

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