Knowledge Graph Based Hard Drive Failure Prediction

Chhetri, Tek Raj; Kurteva, Anelia; Adigun, Jubril Gbolahan; Fensel, Dieter

doi:10.3390/s22030985

Cited by 18 publications

(6 citation statements)

References 58 publications

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“…This, however, may not accurately reflect the actual failure pattern, as failures can occasionally occur abruptly, which we regard as a limitation of this study. The recent work by Chhetri et al [100] on failure prediction, which focuses on hard drive failure prediction using knowledge graphs [101], has demonstrated even greater effectiveness. Future work will involve expanding on this approach of combining system metrics based on knowledge graphs, as described in [100], in order to achieve even greater effectiveness, as well as implementing the production method.…”

Section: Discussionmentioning

confidence: 99%

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

Self Cite

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Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

Chhetri

Dehury

Lind

et al. 2022

BDCC

Self Cite

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“…Various XAI methods are summarized in [68] that could be leveraged for PdM in the SME domain. Similarly Graph based approaches could be leveraged in PdM as described in the survey [69], e.g., Knowledge graph [70] and virtual Graph [71] would be useful from an SME perspective to work with limited computing resources. Another state-of-the-art software architecture for Human-AI teaming [72] for smart factories could also be handy for adopting Human-Center AI-based PdM methods in SMEs.…”

Section: Best Practices (Rq4)mentioning

confidence: 99%

A Systematic Mapping Study of Predictive Maintenance in SMEs

et al. 2022

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The rapid growth of Industry 4.0 and predictive methods fostered a great potential for state-of-the-art techniques in the industrial sector, especially in smart factories. The equipment failure or system breakdowns during run time of a factory creates a severe problems towards impoverishment of the production system and destitution of the business. Predictive Maintenance (PdM) is a cost-saving and data driven technique to predict the maintenance time of in-service equipment or systems to reduce breakdown time and increase productivity. Although PdM is pragmatically adopted in large-scale industries, there is a lack of studies that map the PdM adoption in small and medium-sized enterprises (SMEs). In this systematic mapping study (SMS), we focus on predictive maintenance from an SME perspective to explore the field for researchers, scientists, and developers to comprehend the potential of PdM systems, their challenges, distinctive characteristics, and best practices in SMEs. Our study is based on four research questions comprised of demographic data, key challenges, distinctive characteristics, and best practices of predictive maintenance in SMEs. We found that the current literature on PdM is deficient in the SME domain, especially the financial side is vague. There is a huge potential for PdM in SMEs to design cost models and focus on data availability impediments. Management and monitoring of PdM and skilled personnel are also inadequate. Thus, we present a study that extracts the knowledge from the existing literature about PdM in SMEs, finds the research gap, and can assist in identifying the barriers and challenges of PdM adoption in SMEs.

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“…The machine learning models classify hard drive failures 2–15 days in advance. Chhetri et al 8 use machine learning methods and knowledge graphs to predict hard drive failures. Drives are classified one day in advance.…”

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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Knowledge Graph Based Hard Drive Failure Prediction

Cited by 18 publications

References 58 publications

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence

A Systematic Mapping Study of Predictive Maintenance in SMEs

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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