Hard Disk Drive Failure Prediction Method Based On A Bayesian Network

Chaves, Iago C.; Paula, Manoel Rui P.; Leite, Lucas G. M.; Gomes, João P. P.; Machado, Javam C.

doi:10.1109/ijcnn.2018.8489097

Cited by 15 publications

(5 citation statements)

References 13 publications

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“…e Bayesian network failure prediction method has been used with transfer learning so that HDD models with an abundance of data can be used to build prediction models for drives with a lack of data [6]. e Bayesian network-based method for failure prediction in HDDs (BNFH) [7] was proposed to estimate the remaining life of HDDs.…”

Section: Prediction Of Soon-to-failmentioning

confidence: 99%

“…To improve the performance of HDD failures prediction, many machine-learning-based prediction approaches have been proposed, including Bayesian algorithms [6][7][8][9], support vector machine (SVM) [10], classification tree (CT) [11,12], random forest (RF) [13,14], artificial neural network (ANN) [15], convolution neural network (CNN) [16], and recurrent neural network (RNN) [17,18]. RNN-based prediction models achieve the highest FDRs, and RF-based models attain the lowest FARs.…”

Section: Introductionmentioning

confidence: 99%

“…RNN-based prediction models achieve the highest FDRs, and RF-based models attain the lowest FARs. e reason is that the temporal dependence of drive health data is extracted by RNN models to acquire the characteristics of drive deterioration, while early researchers [5,7] seldom utilize the time sequence feature since the dataset scale is small. However, traditional RNN models only keep short-term memory due to the gradient vanishing or exploding [19].…”

Section: Introductionmentioning

confidence: 99%

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Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

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With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.

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Section: Prediction Of Soon-to-failmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

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“…The self-monitoring analysis and reporting technology (SMART) of the disk can analyze the working status of the hard disk and detect various attributes of the disk. The main research method is based on feature selection, selecting the main features that affect hard disk fault prediction and then establishing disk fault prediction models using machine learning algorithms such as decision trees, support vector machines, Bayesian networks, and neural networks [1][2][3]. However, standard machine learning models cannot deal with imbalanced data very well.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Disk Failure Based on Classification Intensity Resampling

Wu,

Guan

2024

Information

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With the rapid growth of the data scale in data centers, the high reliability of storage is facing various challenges. Specifically, hardware failures such as disk faults occur frequently, causing serious system availability issues. In this context, hardware fault prediction based on AI and big data technologies has become a research hotspot, aiming to guide operation and maintenance personnel to implement preventive replacement through accurate prediction to reduce hardware failure rates. However, existing methods still have weaknesses in terms of accuracy due to the impacts of data quality issues such as the sample imbalance. This article proposes a disk fault prediction method based on classification intensity resampling, which fills the gap between the degree of data imbalance and the actual classification intensity of the task by introducing a base classifier to calculate the classification intensity, thus better preserving the data features of the original dataset. In addition, using ensemble learning methods such as random forests, combined with resampling, an integrated classifier for imbalanced data is developed to further improve the prediction accuracy. Experimental verification shows that compared with traditional methods, the F1-score of disk fault prediction is improved by 6%, and the model training time is also greatly reduced. The fault prediction method proposed in this paper has been applied to approximately 80 disk drives and nearly 40,000 disks in the production environment of a large bank’s data center to guide preventive replacements. Compared to traditional methods, the number of preventive replacements based on our method has decreased by approximately 21%, while the overall disk failure rate remains unchanged, thus demonstrating the effectiveness of our method.

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“…Their approach does not incorporate the attributes collected by SMART. Chaves et al 14 obtained the RUL distribution estimates of HDDs using SMART attributes and a Bayesian Network. Lima et al 15 presented a RUL estimation approach for hard drives using LSTM networks.…”

Section: Introductionmentioning

confidence: 99%

Examining the impact of critical attributes on hard drive failure times: Multi‐state models for left‐truncated and right‐censored semi‐competing risks data

Oakley,

Forshaw,

Philipson

et al. 2023

Appl Stoch Models Bus & Ind

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The ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold‐based monitoring system named self‐monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs' remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi‐state models to the resulting semi‐competing risks data. The multi‐state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time‐dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions.

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Hard Disk Drive Failure Prediction Method Based On A Bayesian Network

Cited by 15 publications

References 13 publications

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

Prediction of Disk Failure Based on Classification Intensity Resampling

Examining the impact of critical attributes on hard drive failure times: Multi‐state models for left‐truncated and right‐censored semi‐competing risks data

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