基于随机卷积核的多类机电故障分类
Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels
- University of Artois（阿图瓦大学）
- LSEE
- LGI2A
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究评估SelF-Rocket用于机电故障多类诊断，提出其多变量扩展方法，在两个基准数据集上对比ROCKET类方法，证实该方法的准确率-延迟权衡最优且在挑战性数据集上表现出色。
AI中文摘要：
旋转机械的故障诊断对保障工业过程的可靠性至关重要。基于随机卷积核的时间序列分类（TSC）方法，如ROCKET及其变体，在预测性能与计算效率之间提供了极具吸引力的平衡。本研究评估了SelF-Rocket在机械与电气故障的多类诊断中的表现，并作为新贡献引入了该原始方法的多变量扩展。将所提方法与领先的基于ROCKET的方法在两个公共基准数据集上进行比较，分别是机械故障数据集MaFaulDa和定子匝间短路数据集ITSC-UDG，涵盖单变量和多变量两种设置。实验结果显示，在评估的方法中，SelF-Rocket实现了最佳的准确率-延迟权衡，在MaFaulDa上获得了最高的分类性能，同时在更具挑战性的ITSC-UDG数据集上仍保持高度竞争力。
英文摘要：
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.
