Stratified Statistical Models in Hardware Reliability Analysis

Abstract

Reliability analysis is becoming paramount to the successful operation of systems. This paper considers the problem of hardware failure using hard disc drives and solid state drives as examples. Survivability analysis methods are used to predict hardware degradation by estimating the probability of an event occurring over time. Also, survival models account for incomplete data about the true time to event for censored observations. However, popular statistical methods do not account for features of real data such as the presence of outliers and categorical variables. In this paper, we propose to extend classical survival statistical methods by introducing an interpretable stratifying tree, each leaf of which corresponds to a statistical model. The experimental study is based on evaluating the dependence of the models’ quality as the depth of the tree increases. According to the experimental results, the proposed method outperforms classical statistical models. The results of the study demonstrate the effectiveness of the proposed approach and its potential in the field of reliability of complex technical systems.