Estimating Mean Time to Failure of Solid-State Drives via Self-Organizing Map and Model Averaging

Abstract

In this article, a two-step approach is developed to estimate mean time to failure (MTTF) of solid-state drives (SSD) by first formulating a composite health indicator via multichannel signal fusion and further predicting the remaining useful life(RUL) under degradation model misspecification. Specifically, an unsupervised neural network based on self-organizing map is constructed to approximate the highly nonlinear relationship between multivariate monitoring attributes and a univariate SSD health indicator. For each SSD, the composite health indicator over time is further calibrated by smoothing techniques and formulated into a general path degradation model with a uniform failure threshold. By extrapolating each degradation path to hit the failure threshold, the RULs of SSDs are obtained as pseudofailure times, which are fitted by various lifetime distributions. Finally, a novel model averaging strategy is proposed to weigh the MTTFs estimated by multiple combinations of candidate degradation models and lifetime distributions to alleviate the impact of model misspecification. A real-world SSD dataset is used to demonstrate the feasibility of the proposed two-step approach. Numerical results suggest that the proposed approach better characterizes the underlying degradation process under different model assumptions and settings.