云存储硬盘亚纳米级头盘界面的失效机理和可靠性预测技术

随着存储面密度的不断提高，当今硬盘的头盘间距已降到1nm以下，由于微观接触力、摩擦力、静电力、分子间作用力的影响，头盘间的近接触损伤已在所难免，这对头盘界面、乃至整个硬盘的可靠性都提出了巨大的挑战。本项目以云存储硬盘亚纳米头盘界面为研究对象，建立亚纳米级头盘界面动力学模型，考虑磁头折臂和空气轴承的耦合作用以及各微观界面力的影响；研究头盘动力学特性演化规律和头盘表面微观组织的损伤扩展规律，揭示出亚纳米头盘界面的退化/失效机理，建立头盘界面的失效物理模型；提出失效物理模型和数据驱动模型融合方法来克服单纯使用失效物理模型会受到材料、几何特性等方面差异性影响的缺点；提出基于非线性滤波技术—unscented Kalman filter具有自适应性的实时故障预测模型，以预报硬盘潜在威胁和硬盘的残余寿命。本项目研究工作源于学科前沿和工程需求，对云存储硬盘质量改善和可靠性的提高具有现实意义。

With the continuous increase of the storage areal density, current spacing between head and disk has decreased to sub-one-nanometer. Due to the effect of contact force, friction, electrostatic force, intermolecular force, a wear of head/disk can not avoid, which brings huge challenges to head-disk interface and hard disk. This project aims to deal with such challenges of head-disk interface in cloud hard disk drives. First, a dynamics model of sub-one-nanometer head-disk interface taking the coupling effect between head stack assembly and air bearing, and the interfacial forces into account was developed. To reveal the failure mechanisms of head-disk interface, the dynamic evolution of head-disk interface and the wear progression of head and disk were also investigated. Upon the mechanisms, a physics-of-failure model was built. The physics-of-failure model, however, has shortcomings in dealing with the reliability prediction due to the diversity of the material, and geometry in hard disk drives. To amend it, a fusion model that integrates both physics-of-failure model and data-driven model was built. Furthermore, a new prediction model based on the unscented Kalman filter, which can predict the dynamic behaviors of the performance degradation data and update the model parameters adaptively was developed. With the help of this model, the potential risks and residual life of hard disk drive can be predicted in time. This is a cutting edge research and is critical for engineering practice. It is also helpful to improve the quality and reliability of cloud hard disk drives.