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This study evaluates the SelF-Rocket method for multi-class classification of mechanical and electrical faults in rotating machinery, enhancing the original random convolutional kernel approach with a multivariate extension. By comparing SelF-Rocket against leading ROCKET-based methods on benchmark datasets, the authors demonstrate its superior accuracy-latency trade-off, achieving the highest classification performance on the MaFaulDa dataset and competitive results on the ITSC-UDG dataset. These findings underscore the effectiveness of SelF-Rocket in diagnosing faults, which is crucial for maintaining industrial process reliability.
SelF-Rocket outperforms existing methods in fault classification while optimizing for both accuracy and computational efficiency.
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.
