Data-driven and knowledge-assisted model-based frameworks for supporting facility maintenance
Tesis bakımını desteklemeye yönelik veri odaklı ve bilgi destekli model tabanlı çerçeveler
- Tez No: 859919
- Danışmanlar: DR. ÖĞR. ÜYESİ ASLI AKÇAMETE GÜNGÖR
- Tez Türü: Doktora
- Konular: İnşaat Mühendisliği, Civil Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2024
- Dil: İngilizce
- Üniversite: Orta Doğu Teknik Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: İnşaat Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 264
Özet
Efficient facility maintenance management enhances operational functionality while reducing costs. In practice, however, the lack of (i) historical work order records or their completeness, (ii) updates or complete documentation of facility tasks, and (iii) a sustainable infrastructure makes it difficult to systematically access maintenance information when needed. Moreover, the absence of an intelligent reasoning mechanism extends problem identification and reasoning time. Therefore, this study aims to develop data-driven and knowledge-supported model-based solutions for root-cause reasoning to enhance efficiency in facility maintenance management. In this study, first, an intelligent reasoning approach is proposed for data-driven monitoring to streamline fault reasoning, which combines the maintenance team's expertise with machine learning algorithms in a hybrid intelligence approach to improve the fault reasoning predictions continuously. Hierarchical Neural Networks are developed to group numerous system faults into manageable classification problems, and their prediction capabilities are enhanced through a feedback mechanism developed. Secondly, a BIM-based work order management framework is introduced through visual programming. It links the assets and space to the counterparts in the model and tags observable symptoms, the fault source asset, spatial information, and the impacted assets using symbols and color coding. Using these links in the work order records and standardizing their descriptions, a fault network is created to construct relations between symptoms, fault types, and their assets. When a new work is requested, an analysis approach is proposed to isolate and reason the fault by filtering the network connections utilizing the similarities based on model-derived spatial, systemic, and feature-based relations. The proposed solutions are examined through test cases, and their effectiveness is verified to present the potential of the proposed methods.
Özet (Çeviri)
Efficient facility maintenance management enhances operational functionality while reducing costs. In practice, however, the lack of (i) historical work order records or their completeness, (ii) updates or complete documentation of facility tasks, and (iii) a sustainable infrastructure makes it difficult to systematically access maintenance information when needed. Moreover, the absence of an intelligent reasoning mechanism extends problem identification and reasoning time. Therefore, this study aims to develop data-driven and knowledge-supported model-based solutions for root-cause reasoning to enhance efficiency in facility maintenance management. In this study, first, an intelligent reasoning approach is proposed for data-driven monitoring to streamline fault reasoning, which combines the maintenance team's expertise with machine learning algorithms in a hybrid intelligence approach to improve the fault reasoning predictions continuously. Hierarchical Neural Networks are developed to group numerous system faults into manageable classification problems, and their prediction capabilities are enhanced through a feedback mechanism developed. Secondly, a BIM-based work order management framework is introduced through visual programming. It links the assets and space to the counterparts in the model and tags observable symptoms, the fault's source asset, spatial information, and the impacted assets using symbols and color coding. Using these links in the work order records and standardizing their descriptions, a fault network is created to construct relations between symptoms, fault types, and their assets. When a new work is requested, an analysis approach is proposed to isolate and reason the fault by filtering the network connections utilizing the similarities based on model-derived spatial, systemic, and feature-based relations. The proposed solutions are examined through test cases, and their effectiveness is verified to present the proposed methods' potential.
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