Academic Journal
Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process
العنوان: | Fault Detection-Based Multiple Local Manifold Learning and Its Application to Blast Furnace Ironmaking Process |
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المؤلفون: | Ke Wang, Ping Wu, Siwei Lou, Haipeng Pan, Jinfeng Gao |
المصدر: | Electronics, Vol 12, Iss 23, p 4773 (2023) |
بيانات النشر: | MDPI AG |
سنة النشر: | 2023 |
المجموعة: | Directory of Open Access Journals: DOAJ Articles |
مصطلحات موضوعية: | data-driven method, fault detection, manifold learning, blast furnace ironmaking process, Electronics, TK7800-8360 |
الوصف: | Process safety plays a vital role in the modern process industry. To prevent undesired accidents caused by malfunctions or other disturbances in complex industrial processes, considerable attention has been paid to data-driven fault detection techniques. To explore the underlying manifold structure, manifold learning methods including Laplacian eigenmaps, locally linear embedding, and Hessian eigenmaps have been utilized in data-driven fault detection. However, only the partial local structure information is extracted from the aforementioned methods. This paper proposes fused local manifold learning (FLML), which synthesizes the typical manifold learning methods to find the underlying manifold structure from different angles. A more comprehensive local structure is discovered under a unified framework by constructing an objection optimization function for process data dimension reduction. The proposed method takes advantage of different manifold learning methods. Based on the proposed dimension reduction method, a new data-driven fault detection method is developed. Hotelling’s |
نوع الوثيقة: | article in journal/newspaper |
اللغة: | English |
تدمد: | 2079-9292 |
Relation: | https://www.mdpi.com/2079-9292/12/23/4773; https://doaj.org/toc/2079-9292; https://doaj.org/article/ea7112ba05224aa5889f77220d670a10 |
DOI: | 10.3390/electronics12234773 |
الاتاحة: | https://doi.org/10.3390/electronics12234773 https://doaj.org/article/ea7112ba05224aa5889f77220d670a10 |
رقم الانضمام: | edsbas.48E4BEE |
قاعدة البيانات: | BASE |
تدمد: | 20799292 |
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DOI: | 10.3390/electronics12234773 |