  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.
