Thesis
Predictive Maintenance for Chilled Water System at Commercial Buildings
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2024
- Thesis identifier
- T17073
- Person Identifier (Local)
- 202050615
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Commercial buildings are equipped with critical systems that need strong attention by applying efficient maintenance practices. One of these systems is the chilled water system (CWS), which contains sophisticated components and consumes significantly higher levels of energy and financial resources compared to other systems. Given the relevance of the issue, this research study started with the following guiding research question: “What are the approaches or methods to implement predictive maintenance (PdM) or fault detection for a chilled water system at commercial buildings?” The review of the literature (with more than 180 studies analysed) identified several research gaps, which are (1) the impact of the technical correlation between CWS components on fault detection remains unknown, (2) there is a significant level of variations in defining CWS faults and their importance, (3) the data measurement of these faults is not standardised leading to unclear data collection practice, and (4) the resolution of these faults remains inconclusive. Accordingly, four research questions were generated. Two research methods were assigned to answer the generated four research questions: an industry survey and a case study. The industry survey adhered to construction guidelines and a pilot study. Subsequently, it was sent to 761 professionals of commercial buildings in the city of Riyadh, Kingdom of Saudi Arabia, out of which 304 responses were considered and analysed. For the second research method, a case study, a novel methodological framework has been developed and implemented. The framework contained three phases: set-up, machine learning and quality control. The first phase proposed arrangements to prepare the framework, while in the literature, studies were directly started with building the detection model. The second phase proposed a decision tree model to detect faults. The final phase suggested managerial steps for monitoring, controlling, and evaluating the maintenance framework which includes the detection model, while in the literature, studies were ended with presenting the model accuracy. In addition, a second case study has been conducted for external validity purposes. This research project has proposed an intelligent maintenance framework for the whole CWS components in line with Industry 4.0, which includes a fault detection model using machine learning. During three empirical periods, the research questions have been answered and verified, with the proposed detection mode achieving greater than or equal to 20 per cent improvement in detecting faults at the two case study sites compared to the current building management system. This thesis makes significant theoretical contributions, which are adding and recording additional faults to the ones mentioned by the literature, providing an action to fix each fault, providing fault frequencies that can be used in data collection and machine learning, and confirming the technical relevance between CWS components. Practically, this thesis makes significant contributions by proposing the said methodological framework, which contains an intelligent detection model. The framework inherently led to three other contributions, which are providing a simplified schematic for CWS, providing a proper location for each reading tool for data collection purpose, and providing a control plan for continuous monitoring for CWS. The aforementioned theoretical and practical contributions give a strong value for this research as they delivered a holistic maintenance guide for CWS at commercial buildings. At the end of this thesis, several areas for future research are suggested as well as the author’s own reflection is shared.
- Advisor / supervisor
- Mendibil, Kepa
- Resource Type
- DOI
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PDF of thesis T17073 | 2024-10-11 | Público | Descargar |