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arXiv Machine Learning By Mouhamadou Mansour Lo, Mouad Talbaoui, Gildas Morvan, Mathieu Rossi, Fabrice Morganti, David Mercier

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

Read the original on arXiv Machine Learning →

The paper evaluates SelF‑Rocket, a random convolutional kernel-based method, for multi‑class diagnosis of mechanical and electrical faults in rotating machinery. It introduces a multivariate extension of SelF‑Rocket and compares it with leading ROCKET‑based methods on two public benchmarks, MaFaulDa and ITSC‑UDG. Results show SelF‑Rocket achieves the best accuracy‑latency trade‑off, excelling on MaFaulDa and remaining competitive on the more challenging ITSC‑UDG dataset.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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