New Weibull Log-Logistic grey forecasting model for a hard disk drive failures

• Published in: Applied mathematical modelling 2024-07, Vol.131, p.669-690
• Main Authors: Chen, Rongxing, Xiao, Xinping
• Format: Article
• Language: English
• Subjects: Hard disk drive failure prediction; Interactive grey modeling; Multiple interactions effects; Weibull log-logistic mixture distribution
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2024.04.025

•A new Weibull log-logistic mixture distribution accumulation generation operator is established.•A novel grey model with multiple interaction effects is established.•The robustness and stability of the proposed method is proved and validated.•A new parameter estimation method is designed.•The proposed method is superior to other seven benchmark models in four case studies. Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives.