Failure Prediction of Hard Disk Drives Based on Adaptive Rao–Blackwellized Particle Filter Error Tracking Method

Active failure prediction of hard disk drives (HDDs) is critical to prevent data loss and spare parts replacement decisions. Existing methods for failure predictions of HDDs always used a binary classifier to distinguish the healthy or failed HDDs and cannot address the problem of variable degradation states. In this article, an adaptive error tracking method is proposed for the HDD failure prediction. This method regards the extracted degradation feature as time serials and uses a state filter to estimate the real-time HDD's health status. Then, the HDD failure online prediction is achieved according to the alarm threshold determined by the adaptive error tracking. The degradation of an HDD is described by a first-order Markov hybrid jump degradation model, and the advanced Rao–Blackwellized particle filter algorithm, together with the expectation-maximization (EM) algorithm, is derived to estimate the model parameters adaptively. Finally, to verify the effectiveness of the proposed method, an accelerated degradation test (ADT) based on the vibration was carried out. And the data from ADT and real data center show that the proposed method performs much better than the previous methods, such as Kalman filter, SVM, MD, and recurrent neural network (RNN) based methods, with respect to failure prediction and the alarm distance, which helps to backup data and optimize maintenance decision costs for users.