  Assessing the health status (HS) of system/component has long been a
challenging task in the prognostic and health management (PHM) study. Differed
from other regression based prognostic task such as predicting the remaining
useful life, the HS assessment is essentially a multi class classificatIon
problem. To address this issue, we introduced the random convolutional
kernel-based approach, the RandOm Convolutional KErnel Transforms (ROCKET) and
its latest variant MiniROCKET, in the paper. We implement ROCKET and MiniROCKET
on the NASA's CMPASS dataset and assess the turbine fan engine's HS with the
multi-sensor time-series data. Both methods show great accuracy when tackling
the HS assessment task. More importantly, they demonstrate considerably
efficiency especially compare with the deep learning-based method. We further
reveal that the feature generated by random convolutional kernel can be
combined with other classifiers such as support vector machine (SVM) and linear
discriminant analysis (LDA). The newly constructed method maintains the high
efficiency and outperforms all the other deop neutal network models in
classification accuracy.
