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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andy, K.: Backblaze Hard Drive Stats for 2017 (2017). https://www.backblaze.com/blog/hard-drive-stats-for-2017/
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., Trujillo, S.: Real-time predictive maintenance for wind turbines using big data frameworks. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 70–77. IEEE, June 2017
Zhao, P., Kurihara, M., Tanaka, J., Noda, T., Chikuma, S., Suzuki, T.: Advanced correlation-based anomaly detection method for predictive maintenance. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 78–83. IEEE, June 2017
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3(3), 210–229 (1959)
Brownlee, J.: Discover Feature Engineering, How to Engineer Features and How to Get Good at It (2014). https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
Giles, C.L., Lawrence, S., Tsoi, A.-C.: Rule inference for financial prediction using recurrent neural networks. In: IEEE Conference on Computational Intelligence for Financial Engineering, p. 253. IEEE Press (1997)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks 18(5–6), 602–610 (2005)
Pinheiro, E., Weber, W.-D., Barroso, L.A.: Failure Trends in a Large Disk Drive Population (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Su, CJ., Tsai, LC., Huang, SF., Li, Y. (2020). Deep Learning-Based Real-Time Failure Detection of Storage Devices. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-20454-9_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20453-2
Online ISBN: 978-3-030-20454-9
eBook Packages: EngineeringEngineering (R0)