Fault Detection in Hard Disk Drives Based on a Semi Parametric Model and Statistical Estimators

Queiroz, Lucas P.; Gomes, João P. P.; Rodrigues, Fernanda Luciano; Brito, Felipe T.; Chaves, Iago C.; Leite, Lucas G. M.; Machado, Javam C.

doi:10.1007/s00354-017-0016-0

Cited by 5 publications

(1 citation statement)

References 13 publications

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“…A fault detection method using a Gaussian mixture model to simulate a healthy hard disk was proposed to detect the faults in hard disk drives (HDDs). In the method, when the statistical estimator calculates these differences beyond the threshold, an exception will be detected via the similarity between a given HDD and the statistical model [18]. Matheus et al studied a new training model method and a new similarity measure [19].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Shi

Zeng

2020

Symmetry

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During actual engineering, due to the influence of complex operation conditions, the data of complex systems are distinct, and the range of similarity differs under complex operation conditions. Simultaneously, the length of the data used to calculate the similarity will also impact the result of the fault detection. According to these, this paper proposes a fault detection method based on correlation analysis and improved similarity. In the first place, the complex operation conditions are divided into several simple operation conditions via the existing historical data. In the next place, the length of the data used to calculate the similarity is determined by correlation analysis. Then, an improved similarity calculation method is proposed to make the range of the similarity under multi-operation conditions identical. Finally, this method is applied to the suspension system of the maglev train. The experiment results indicate that the method proposed in this paper can not only detect the fault or abnormal state of the suspension system but also observe the health index (HI) changes of the system at distinct times under multi-operation conditions.

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Section: Introductionmentioning

confidence: 99%

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Shi

Zeng

2020

Symmetry

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A survey on AI for storage

et al. 2022

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Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

Zhou

et al. 2020

IEEE Access

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The fault detection of the suspension system in a maglev train is of great importance for its operational safety and reliability. However, in random matrix theory (RMT), the size of the random matrix direct impacts the result of the mean spectral radius (MSR). In this paper, a state-of-the-art fault detection method for suspension systems is proposed using optimized RMT. The random matrix with the largest number of eigenvalues is obtained by reshaping the original data, with the help of the auto-correlation length from the correlation analysis. Finally, the optimized MSR is applied to detect the fault. The results of the experiment illustrate that the proposed method is applicable and effective. INDEX TERMS Fault detection; auto-correlation length; random matrix theory; mean spectral radius; suspension system.

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Fault Detection in Hard Disk Drives Based on a Semi Parametric Model and Statistical Estimators

Cited by 5 publications

References 13 publications

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

Fault Detection for Complex System under Multi-Operation Conditions Based on Correlation Analysis and Improved Similarity

A survey on AI for storage

Fault Detection Method for Suspension Systems of Maglev Train Based on Optimized Random Matrix Theory

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