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

Abstract: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, provide an attractive trade-off between predictive performance and computational efficiency. In this work, we evaluate SelF-Rocket for the multi-class diagnosis of both mechanical and electrical faults and introduce, as a new contribution, a multivariate extension of the original method. The proposed approach is compared with leading ROCKET-based methods on two public benchmark datasets, MaFaulDa (mechanical faults) and ITSC-UDG (stator inter-turn short circuits), under both univariate and multivariate settings. Experimental results show that SelF-Rocket achieves the best overall accuracy-latency trade-off among the evaluated methods, obtaining the highest classification performance on MaFaulDa while remaining highly competitive on the more challenging ITSC-UDG dataset.

Machine Learning (cs.LG)

arXiv:2608.18716 [cs.LG]

(or arXiv:2608.18716v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.18716 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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