用随机卷积核方法实现电机与机械故障的高效多类分类
Multi-Class Electrical and Mechanical Fault Classification Using Random Convolutional Kernels
- 基于随机卷积核设计多变量故障分类模型
- 在两个数据集上均取得最佳准确率与速度平衡
- 适合工业设备智能诊断场景快速部署
旋转机械故障诊断对保障工业流程可靠性至关重要。基于随机卷积核的时间序列分类方法（如ROCKET及其变体）在预测性能与计算效率之间提供了良好权衡。本文评估了SelF-Rocket在机械与电气故障多类别诊断中的表现，并提出原方法的多变量扩展。该方法在两个公开基准数据集MaFaulDa（机械故障）和ITSC-UDG（定子匝间短路）上，对比了多种主流ROCKET方法，在单变量与多变量设置下进行实验。结果表明，SelF-Rocket在所有评估方法中实现了最优的准确率-延迟权衡：在MaFaulDa上达到最高分类性能，在更具挑战性的ITSC-UDG数据集上仍保持高度竞争力。
原文摘要 · Abstract (English)
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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