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

• URL: http://arxiv.org/abs/2608.18716v1
• Date: Wed, 19 Aug 2026 09:18:32 GMT
• Title: Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels
• Abstract summary: We evaluate SelF-Rocket for the multi-class diagnosis of both mechanical and electrical faults.<n>We show that SelF-Rocket achieves the best overall accuracy-latency trade-off among the evaluated methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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