scholarly journals Remaining Useful Life Estimation of Hard Disk Drives using Bidirectional LSTM Networks

2021 ◽  
Author(s):  
Austin Coursey ◽  
Gopal Nath ◽  
Srikanth Prabhu ◽  
Saptarshi Sengupta
Keyword(s):  
Hard Disk Drives ◽  
Hard Disk ◽  
Remaining Useful Life ◽  
Disk Drives ◽  
Life Estimation ◽  
Useful Life ◽  
Bidirectional Lstm

Remaining Useful Life Estimation of Hard Disk Drives based on Deep Neural Networks

2018 ◽  
Author(s):  
Fernando Dione S. Lima ◽  
Francisco Lucas F. Pereira ◽  
Lucas G. M. Leite ◽  
Joao Paulo P. Gomes ◽  
Javam C. Machado
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Hard Disk Drives ◽  
Hard Disk ◽  
Remaining Useful Life ◽  
Disk Drives ◽  
Life Estimation ◽  
Useful Life

Investigation of Degradation Signature for Hard Disk Drives Using Vibration and Acoustic Emission Sensors

2009 ◽  
Author(s):  
Ibrahim Zeid ◽  
Sagar Kamarthi ◽  
Yogesh Bagul
Keyword(s):  
Acoustic Emission ◽  
Computer System ◽  
Signal To Noise Ratio ◽  
Hard Disk Drives ◽  
Hard Disk ◽  
Disk Drive ◽  
Remaining Useful Life ◽  
Acoustic Emission Sensors ◽  
Sensor Signals ◽  
Useful Life

The hard disk drive (HDD) is a critical component of any computer system. The performance of a computer system largely depends on the performance and health of its HDD. This paper investigates degradation signatures for the estimation of remaining useful life and the assessment of health of a HDD. Most of the mechanical faults in a HDD results in head-disk collision or friction. As a HDD ages, it may experience gradual damage to the head and scratches on the disk. One can expect that changes in the condition of head and disk may result in comparable changes in the characteristics of HDD vibration and acoustic emission signals. Based on this premise, this research conducted experiments on HDDs subject to accelerated deterioration. HDDs are monitored through vibration and acoustic emission sensors. Extracting features from these sensor signals, HDD degradation signatures are created. The results indicate that though degradation signatures exhibit a gradual trend with HDD aging, accurate assessment remaining useful life and health are not possible using these degradation signatures. Poor signal to noise ratio is the main impediment in this approach. The conclusion is that the best vibration and acoustic sensors available for this application are neither sensitive nor selective enough to capture the changes in the head and the disk of an aging HDD.

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

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