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.

中文翻译：

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用非对称和有序深度神经模型预测硬盘驱动器的健康程度

预测硬盘驱动器 (HDD) 的故障是近年来工业界和学术界面临的重大挑战。能够预测故障事件可能会避免数据丢失并提高服务可用性。在所有故障预测策略中，健康度预测是最受欢迎的策略之一。健康度预测的任务包括，给定一组与设备退化相关的有限健康状态，估计哪个状态反映了设备的实际退化。这个问题通常被建模为分类任务。尽管已经提出了许多健康度预测方法，但在以前的工作中忽略了有关该预测任务的一些实际细节。在这项工作中，我们处理以下两个方面：问题的顺序性质以及与错误分类相关的不同成本。这个问题可以被认为是有序的，因为在远离真实健康水平的健康水平上对硬盘进行分类比在接近健康水平上分类会受到更多的惩罚，因此不推荐经典的分类框架。与错误分类相关的不同成本与这样一个事实有关，即早期预测比晚期预测更受欢迎，因为后者可能导致失败。在基于深度循环神经网络 (DRNN) 的框架中考虑了这些方面。选择 DRNN 的原因在于其在包括 HDD 故障预测在内的许多应用中的卓越性能。由此产生的方法在一个指标中优于最先进的方法，该指标考虑了激发我们提议的新方面。