Predicting the Health Degree of Hard Disk Drives With Asymmetric and Ordinal Deep Neural Models

Abstract

Predicting failures in Hard Disk Drives (HDD) is a major challenge that has been faced by both industry and academy in recent years. Being able to predict failure events may incur in avoiding data losses and also improve service availability. Among all failure prediction strategies, the health degree prediction is one of the most popular. The task of health degree prediction consists of, given a finite set of health states that are related to the degradation of the equipment, estimate which state reflects the actual degradation of the equipment. This problem is usually modeled as a classification task. Although many health degree prediction methods have been proposed, some practical details regarding this prediction task have been neglected in previous works. In this work we tackle two of these aspects: the ordinal nature of the problem and the different costs associated with miss-classifications. The problem can be considered as ordinal since classifying a HDD in a health level that is far from is true health level shall be more penalized than classifying in a near health level, thus a classical classification framework is not recommended. The different costs associated with mis-classifications are related to the fact that early predictions are preferred than late prediction since the later can result in failures. Such aspects are considered in a framework based on Deep Recurrent Neural Networks (DRNN). The choice of DRNN is given its remarkable performances in many applications including HDDs failure prediction. The resulting methods outperformed state-of-the-art approaches in a metric that consider the new aspects that motivated our proposal.