| dc.contributor | 2019智慧企業資訊應用發展國際研討會 | |
| dc.creator (作者) | 白浩廷 | |
| dc.date (日期) | 2019-06 | |
| dc.date.accessioned | 17-Jul-2019 15:04:21 (UTC+8) | - |
| dc.date.available | 17-Jul-2019 15:04:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-Jul-2019 15:04:21 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/124328 | - |
| dc.description.abstract (摘要) | 異常偵測應用包括防治金融詐欺、改善工業設備營運等。以紙業為例，造紙流程涉及上千個感測器，形成一個高維度且巨量的資料集。德國PTS指出每次斷紙事件將造成至少6,000歐元(相當於210,000新臺幣)損失，斷紙頻率約每日發生6至9次。此外，斷紙造成的能耗虛耗約占總生產量的2%至7%，不僅是經濟上的虧損還會浪費大量資源。本研究探討異常偵測技術與其應用在斷紙分析之發展狀況，我們以非線性支持向量機分類方法(Non-linear SVM, N-SVM)分析國外斷紙資料集，並從分析結果論述工業數據之異常偵測挑戰、異常因素探索以及資料品質的重要性。 | |
| dc.description.abstract (摘要) | Anomaly detection technology has been widely applied to varied areas, e.g., fraud detection for credit cards, fault detection in safety critical systems, and so on. In paper industry, the paper-making process involves in thousands of sensors, which forms high-dimensional large amounts of data. In analyzing such big data, the state-of-the-art methods probably suffer from computation and distortion problems. According to PTS, a paper break costs around 210,000 NT$, and it occurred 6 to 9 times per day. In addition, paper breaks cause 2–7% of the total production loss. In this paper, we survey the taxonomy of anomaly detection methods and their applications in analyzing paper breaks. Moreover, we adopt the non-linear SVM method (N-SVM) to analyze the paper breaks dataset. Finally, we discuss the findings and illustrate the importance of anomaly exploration and data quality. | |
| dc.format.extent | 103044 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | 2019智慧企業資訊應用發展國際研討會 | |
| dc.subject (關鍵詞) | 大數據分析、機器學習、斷紙 | |
| dc.subject (關鍵詞) | Big data analytics, machine learning, paper breaks | |
| dc.title (題名) | 工業大數據之異常偵測分析挑戰：以紙業為例 | |
| dc.type (資料類型) | conference | |
