Transfer Learning for Bayesian Networks with Application on Hard Disk Drives Failure Prediction

2017 ◽  
Author(s):  
Francisco Lucas Falcao Pereira ◽  
Fernando Dione dos Santos Lima ◽  
Lucas Goncalves de Moura Leite ◽  
Joao Paulo Pordeus Gomes ◽  
Javam de Castro Machado
Keyword(s):  
Bayesian Networks ◽  
Transfer Learning ◽  
Hard Disk Drives ◽  
Failure Prediction ◽  
Hard Disk ◽  
Disk Drives

scholarly journals A Two-Step Parametric Method for Failure Prediction in Hard Disk Drives

2014 ◽  
Vol 10 (1) ◽  
pp. 419-430 ◽  
Author(s):  
Yu Wang ◽  
Eden W. M. Ma ◽  
Tommy W. S. Chow ◽  
Kwok-Leung Tsui
Keyword(s):  
Parametric Method ◽  
Hard Disk Drives ◽  
Failure Prediction ◽  
Hard Disk ◽  
Disk Drives

Evaluation of Recurrent Neural Networks for Hard Disk Drives Failure Prediction

2018 ◽  
Author(s):  
Fernando Dione S. Lima ◽  
Francisco Lucas F. Pereira ◽  
Iago C. Chaves ◽  
Joao Paulo P. Gomes ◽  
Javam C. Machado
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Hard Disk Drives ◽  
Failure Prediction ◽  
Hard Disk ◽  
Disk Drives

scholarly journals Random-forest-based failure prediction for hard disk drives

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771880648 ◽  
Author(s):  
Jing Shen ◽  
Jian Wan ◽  
Se-Jung Lim ◽  
Lifeng Yu
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Hard Disk Drives ◽  
Failure Prediction ◽  
Prediction Method ◽  
Hard Disk ◽  
Coarse Grained ◽  
Disk Drives ◽  
Large Scale Data ◽  
Real World Datasets

Failure prediction for hard disk drives is a typical and effective approach to improve the reliability of storage systems. In a large-scale data center environment, the various brands and models of drives serve diverse applications with different input/output workload patterns, and non-ignorable differences exist in each type of drive failures, which make this mechanism much challenging. Although many efforts are devoted to this mechanism, the accuracy still needs to be improved. In this article, we propose a failure prediction method for hard disk drives based on a part-voting random forest, which differentiates prediction of failures in a coarse-grained manner. We conduct groups of validation experiments on two real-world datasets, which contain the SMART data of 64,193 drives. The experimental results show that our proposed method can achieve a better prediction accuracy than state-of-the-art methods.

BaNHFaP: A Bayesian Network Based Failure Prediction Approach for Hard Disk Drives

2016 ◽  
Author(s):  
Iago C. Chaves ◽  
Manoel Rui P. de Paula ◽  
Lucas G.M. Leite ◽  
Lucas P. Queiroz ◽  
Joao Paulo P. Gomes ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Hard Disk Drives ◽  
Failure Prediction ◽  
Hard Disk ◽  
Disk Drives ◽  
Prediction Approach

scholarly journals Examining the feasibility of the urban mining of hard disk drives

2020 ◽  
Vol 248 ◽  
pp. 119216
Author(s):  
Laura Talens Peiró ◽  
Alejandra Castro Girón ◽  
Xavier Gabarrell i Durany
Keyword(s):  
Hard Disk Drives ◽  
Hard Disk ◽  
Disk Drives ◽  
Urban Mining

scholarly journals Evaluation of Vibration Characteristics of Suspensions for Hard Disk Drives and Reducing the Vibration by Using Viscoelastic Dampers.

2002 ◽  
Vol 68 (667) ◽  
pp. 720-727 ◽  
Author(s):  
Hiromitsu MASUDA ◽  
Toshihiko SHIMIZU ◽  
Mikio TOKUYAMA ◽  
Haruhide TAKAHASHI ◽  
Kousaku WAKATSUKI ◽  
...  
Keyword(s):  
Hard Disk Drives ◽  
Hard Disk ◽  
Vibration Characteristics ◽  
Disk Drives ◽  
Viscoelastic Dampers
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