Fault Detection Using Nonlinear Low-Dimensional Representation Of Sensor Data
2020 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM (RAMS 2020)(2020)
摘要
Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel principal component analysis (KPCA) for fault detection. We show that sensor data monitoring with low dimensional representations provides better interpretability and is conducive to edge processing in IoT applications.
更多查看译文
关键词
Kernel PCA, t-SNE, Fault Detection, IoT
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要