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arXiv · 2608.18716

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

Mouhamadou Mansour Lo·Mouad Talbaoui·Gildas Morvan·Mathieu Rossi·Fabrice Morganti·David Mercier
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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.

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Mouhamadou Mansour Lo, Mouad Talbaoui, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier. 2026-08-19. Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels. https://arxiv.org/abs/2608.18716

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