English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 115496/146528 (79%)
造訪人次 : 55441296      線上人數 : 335
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/153664


    題名: A Seq2Seq transformation strategy for generalizing a pre-trained model in anomaly detection of rolling element bearings
    作者: 呂欣澤
    Lu, Owen H.T.
    貢獻者: 創國學士班
    關鍵詞: Anomaly detection;Transformer;Rolling element bearings
    日期: 2024-11
    上傳時間: 2024-09-10 13:08:04 (UTC+8)
    摘要: The monitoring of the health of Rolling Element Bearings (REBs) in the rolling mill process was recently automated through signal processing and machine learning technologies. However, large data sets are required, and existing methods require precise labels. Moreover, the scarcity of equipment anomalies challenges their practical identification. In addition, machine learning approaches require human resources to manage machine learning models because all REBs in the production line are not identical. Thus, we propose herein a Seq2Seq Transformation Strategy inspired by the accurate performance of such the methods in Natural Language Processing (NLP) and Computer Visioning (CV). Our strategy aims to use an anomaly detection model trained on one signal to detect anomalies in other signals, reducing the need for model management in practical scenarios. Specifically, we considered time series vibration signals collected from the REBs to be analogous to linguistic data, which are sequential and time variant. In essence, our strategy generates synthetic data from a vibration signal represented as the target domain by using a transformer. Subsequently, a model trained by another signal called the source domain is employed to detect the conditions of the target domain. The goal of transformation is to minimize the differences between the distributions of the synthetic and source data. We assumed that the target conditions could be identified from the synthetic data by the source model if the detection model trained on the source domain was precise. Our strategy was experimentally evaluated on six large, unlabeled data sets collected over 1 year from a rolling mill production line in a steel factory. The strategy significantly improved the accuracy of anomaly detection, which had initially been unacceptable when the source model was directly applied to the detection of the target conditions.
    關聯: Expert Systems with Applications, Vol.254, 124297, pp.1-12
    資料類型: article
    DOI 連結: https://doi.org/10.1016/j.eswa.2024.124297
    DOI: 10.1016/j.eswa.2024.124297
    顯示於類別:[創國學士班] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML301檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋