基于随机卷积核的预测分类
Prognostic classification based on random convolutional kernel
摘要：
评估系统/组件的健康状态（HS）一直是预测和健康管理（PHM）研究中具有挑战性的任务。与其他基于回归的预测任务（如预测剩余使用寿命）不同，HS评估本质上是一个多类分类问题。为了解决这个问题，我们在论文中介绍了基于随机卷积核的方法，RandOm卷积核变换（ROCKET）及其最新变体MiniROCKET。我们在NASA的CMPASS数据集上实现了ROCKET和MiniROCKET，并利用多传感器时间序列数据评估了涡轮风扇发动机的HS。这两种方法在处理HS评估任务时显示出很高的准确性。更重要的是，它们表现出相当高的效率，特别是与基于深度学习的方法相比。我们进一步揭示了随机卷积核生成的特征可以与其他分类器，如支持向量机（SVM）和线性判别分析（LDA）相结合。新构造的方法保持了较高的效率，并在分类精度上优于所有其他的深度神经网络模型。
英文摘要：
Assessing the health status (HS) of system/component has long been a challenging task in the prognostic and health management (PHM) study. Differed from other regression based prognostic task such as predicting the remaining useful life, the HS assessment is essentially a multi class classificatIon problem. To address this issue, we introduced the random convolutional kernel-based approach, the RandOm Convolutional KErnel Transforms (ROCKET) and its latest variant MiniROCKET, in the paper. We implement ROCKET and MiniROCKET on the NASA's CMPASS dataset and assess the turbine fan engine's HS with the multi-sensor time-series data. Both methods show great accuracy when tackling the HS assessment task. More importantly, they demonstrate considerably efficiency especially compare with the deep learning-based method. We further reveal that the feature generated by random convolutional kernel can be combined with other classifiers such as support vector machine (SVM) and linear discriminant analysis (LDA). The newly constructed method maintains the high efficiency and outperforms all the other deop neutal network models in classification accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
