Condition Monitoring with Time Series Data Based on Probabilistic Model

被引：1

作者：

Soh, Jaehyun ^{[1
]}

Kim, DaeEun ^{[1
]}

机构：

[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul, South Korea

来源：

2021 24TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2021) | 2021年

基金：

新加坡国家研究基金会;

关键词：

Gaussian mixture model (GMM); Data selection; Condition monitoring; Condition-based maintenance (CBM); Prognostics and health management (PHM); CONDITION-BASED MAINTENANCE;

D O I：

10.23919/ICEMS52562.2021.9634481

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As many systems become automated, system maintenance is becoming more critical. It is important always to monitor the system condition to maintain the system more efficiently and stably. In this paper, we propose a probability-based algorithm that analyzes time-series data of a complex system. We evaluate various system conditions with high accuracy by analyzing critical data among time-series data with GMM-based probability.

引用

页码：2630 / 2634

页数：5

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