SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver

A classification technique incorporating a novel feature derivation method is proposed for predicting failure of a system or device with multivariate time series sensor data. We treat the multivariate time series sensor data as images for both visualization and computation. Failure follows various patterns which are closely related to the root causes. Different predefined transformations are applied on the original sensors data to better characterize the failure patterns. In addition to feature derivation, ensemble method is used to further improve the performance. In addition, a general algorithm architecture of deep neural network is proposed to handle multiple types of data with less manual feature engineering. We apply the proposed method on the early predict failure of computer disk drive in order to improve storage systems availability and avoid data loss. The classification accuracy is largely improved with the enriched features, named smart features.

A Nonlinear Model for Time Synchronization

The current algorithms are based on linear model, for example, Precision Time Protocol (PTP) which requires frequent synchronization in order to handle the effects of clock frequency drift. This paper introduces a nonlinear approach to clock time synchronize. This approach can accurately model the frequency shift. Therefore, the required time interval to synchronize clocks can be longer. Meanwhile, it also offers better performance and relaxes the synchronization process. The idea of the nonlinear algorithm and some numerical examples will be presented in this paper in detail.