@inproceedings{e4f7df91ed2e46ef817e167705eb4799,
title = "Data-driven Methodology for State Detection of Gearbox in PHM Context",
abstract = "With the development of artificial intelligence technology, data-driven PHM technology has been widely used for life cycle health management of equipment. Equipment will generate a lot of data in the process of operation and production. Analyzing the data and establishing machine learning model can accurately evaluate the operation status of equipment. Increasingly, extracting knowledge from data has become an important task in organizations for performance improvements. Data is the resource for equipment health assessment, so it is of great significance to focus on the research of data quality. Based on this, the main work of this paper is as follows. (1) The data quality issues are discussed in the context of PHM. (2) The PHM framework is proposed for improving the reliability of equipment. (3) Several machine learning algorithms are introduced for state detection. (4) The proposed technology is applied to real cases, and the results are analyzed and visualized in detail.",
keywords = "Data quality, Gearbox, PHM, State detection",
author = "Qiuan Chen and Yi Liu and Shengwen Hou and Feng Duan and Zhiqiang Cai",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021 ; Conference date: 15-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/PHM-Nanjing52125.2021.9612946",
language = "英语",
series = "2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li",
booktitle = "2021 Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021",
}