Prognostic classification based on random convolutional kernel

Assessing the health status (HS) of system/component has long been a challenging task in the prognostics 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, RandOm Convolutional KErnel Transforms (ROCKET) and its latest variant MiniROCKET, in the paper. We implement ROCKET and MiniROCKET on the NASA’s CMAPSS 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 outperform all the other deep neural network models in classification accuracy.