| 书目名称 | Data-Driven Fault Detection for Industrial Processes | | 副标题 | Canonical Correlatio | | 编辑 | Zhiwen Chen | | 视频video | | | 概述 | Publication in the field of technical sciences | | 图书封面 |  | | 描述 | .Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.. | | 出版日期 | Book 2017 | | 关键词 | Multivariate statistical process monitoring; Performance evaluation; Data-Driven method; Subspace metho | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-658-16756-1 | | isbn_softcover | 978-3-658-16755-4 | | isbn_ebook | 978-3-658-16756-1 | | copyright | Springer Fachmedien Wiesbaden GmbH 2017 |
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发表于 2025-3-21 16:20:32
