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.
翻译:为了预测具有多变时间序列传感器数据的系统或装置的故障,建议采用包含新特征衍生法的分类技术,预测具有多变时间序列传感器数据的故障。我们把多变时间序列传感器数据作为图像处理,供可视化和计算之用。失败遵循与根源密切相关的各种模式。对原始传感器数据应用了不同的预设变异,以更好地说明故障模式的特点。除了特征衍生外,还采用共通法进一步改进性能。此外,还提议了深神经网络的一般算法结构,以处理多种类型的数据,而手动特性工程较少。我们采用拟议的方法,及早预测计算机磁盘驱动器的故障,以改进存储系统的可用性和避免数据损失。分类精确度随着丰富特性(称为智能特性)而大为改善。