论文标题
基于随机卷积内核的预后分类
Prognostic classification based on random convolutional kernel
论文作者
论文摘要
在预后和健康管理（PHM）研究中，评估系统/组件的健康状况（HS）一直是一项艰巨的任务。与其他基于回归的预后任务不同，例如预测剩余的使用寿命，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随机的基于卷积内核的方法，随机的卷积内核变换（火箭）及其最新的变体Minirocket。我们在NASA的CMPASS数据集上实施了火箭和Minirocket，并使用多传感器时间序列数据评估涡轮风扇引擎的HS。在处理HS评估任务时，这两种方法都非常准确。更重要的是，它们表现出相当大的效率，尤其与基于深度学习的方法相比。我们进一步揭示了随机卷积内核生成的特征可以与其他分类器（例如支持向量机（SVM）和线性判别分析（LDA））结合使用。新构建的方法保持高效率，并在分类精度方面胜过所有其他DEOP中性网络模型。
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.
