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Deep Learning-Based Real-Time Failure Detection of Storage Devices

  • Chuan-Jun Su
  • Lien-Chung Tsai
  • Shi-Feng Huang
  • Yi Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)

Abstract

With the rapid development of cloud technologies, evaluating cloud-based services has emerged as a critical consideration for data center storage system reliability, and ensuring such reliability is the primary priority for such centers. Therefore, a mechanism by which data centers can automatically monitor and perform predictive maintenance to prevent hard disk failures can effectively improve the reliability of cloud services. This study develops an alarm system for self-monitoring hard drives that provides fault prediction for hard disk failure. Combined with big data analysis and deep learning technologies, machine fault pre-diagnosis technology is used as the starting point for fault warning. Finally, a predictive model is constructed using Long and Short Term Memory (LSTM) Neural Networks for Recurrent Neural Networks (RNN). The resulting monitoring process provides condition monitoring and fault diagnosis for equipment which can diagnose abnormalities before failure, thus ensuring optimal equipment operation.

Keywords

Big data Hard disk Failure prediction Recurrent neural networks (RNN) Long and short term memory (LSTM) 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Chuan-Jun Su
    • 1
  • Lien-Chung Tsai
    • 1
  • Shi-Feng Huang
    • 1
  • Yi Li
    • 1
  1. 1.Taoyuan CityTaiwan, R.O.C.

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