@inproceedings{d6150771707a433982b9cd80c65edaa8,
title = "Extracting relevant features for diagnosing machine tool faults in cloud architecture",
abstract = "This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.",
author = "Li, {Yu Yung} and Yang, {Haw Ching} and Hao Tieng and Cheng, {Fan Tien}",
year = "2015",
month = oct,
day = "7",
doi = "10.1109/CoASE.2015.7294299",
language = "English",
series = "IEEE International Conference on Automation Science and Engineering",
publisher = "IEEE Computer Society",
pages = "1434--1439",
booktitle = "2015 IEEE Conference on Automation Science and Engineering",
address = "United States",
note = "11th IEEE International Conference on Automation Science and Engineering, CASE 2015 ; Conference date: 24-08-2015 Through 28-08-2015",
}