Edge-Based RNN Anomaly Detection Platform in Machine Tools

Chia Yu Lin, Chih Ping Weng, Li-Chun Wang*, Hong-Han Shuai, Wen Peng Tseng

*此作品的通信作者

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

With the rapid advances in machine learning algorithms and sensing technologies, machine prognostics and health management (PHM) via data-driven approaches has become a trend in sophisticated machine tool industry. The run-to-failure data are necessary for data-driven approaches. However, the average life of the machine is two to three years, the time of collecting data is extended. It is a big challenge to collect run-to-failure data and build a PHM model. Therefore, we propose an Edge-based RNN Anomaly Detection Platform (ERADP). ERADP builds the model based on healthy data and notify anomalies two hours in advance. The true alarm rate is up to 100%. Besides, ERADP can accelerate the training time almost 120 times faster than the traditional model.

原文English
頁(從 - 到)139-146
頁數8
期刊Smart Science
7
發行號2
DOIs
出版狀態Published - 3 四月 2019

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