Prognostic classification based on random convolutional kernel

2022-04-09 · Zekun Wu, Kaiwei Wu

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.

📄 PDF Abstract BibTeX arXiv:2204.04527

Code (0)

등록된 구현이 없습니다.

Tasks

Classification Management Multi-class ClassificationTime SeriesTime Series Analysis

Methods 이 논문이 사용한 방법론

ROCKET Linear classifier using random convolutional kernels applied to time series.

Low-Rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling

2018-10-31 · Chao Li, Zhun Sun, Jinshi Yu, Ming Hou 외

Although the convolutional neural networks (CNNs) have become popular for various image processing and computer vision task recently, it remains a challenging problem to reduce the storage cost of the parameters for reso…

General Classification Tensor Decomposition

ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

2019-10-29 · Angus Dempster, François Petitjean, Geoffrey I. Webb

Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets…

General Classification Time Series Time Series Analysis Time Series Classification

SPROCKET: Extending ROCKET to Distance-Based Time-Series Transformations With Prototypes

2025-12-09 · Nicholas Harner arxiv

Classical Time Series Classification algorithms are dominated by feature engineering strategies. One of the most prominent of these transforms is ROCKET, which achieves strong performance through random kernel features. …

Time Series Classification Feature Engineering

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

2019-11-12 · Tianfu Li, Zhibin Zhao, Chuang Sun, Li Cheng 외

Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a…

Fault Diagnosis Management Translation

Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels

2026-08-19 · Mouhamadou Mansour Lo, Mouad Talbaoui, Gildas Morvan, Mathieu Rossi 외 arxiv

Diagnosing faults in rotating machinery is essential for ensuring the reliability of industrial processes. Random convolutional kernel-based Time Series Classification (TSC) methods, such as ROCKET and its variants, prov…

Time Series Classification Computational Efficiency