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.

中文翻译：

---

SFFDD：具有丰富功能的深度神经网络，用于故障预测及其在计算机磁盘驱动程序中的应用

提出了一种结合新颖特征推导方法的分类技术，用于预测具有多变量时间序列传感器数据的系统或设备的故障。我们将多元时间序列传感器数据视为用于可视化和计算的图像。失败遵循与根本原因密切相关的各种模式。不同的预定义转换应用于原始传感器数据，以更好地表征故障模式。除了特征推导之外，还使用了集成方法来进一步提高性能。此外，提出了一种深度神经网络的通用算法架构，以较少的人工特征工程来处理多种类型的数据。我们将所提出的方法应用于计算机磁盘驱动器的早期预测故障，以提高存储系统的可用性并避免数据丢失。丰富的特征（称为智能特征）大大提高了分类精度。