Fuzzy Logic Control and Fault Detection in Centralized Chilled Water System

Sulaiman, Noor Asyikin; Othman, Mohd Fauzi; Abdullah, Hayati

doi:10.1109/ssci.2015.265

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…A lab-scale of chilled water system is used in this paper as described in [23][24][25]. It is a prototype of a chiller system that consists of a cooling tower, chiller, AHU and two test rooms.…”

Section: Data Collectionmentioning

confidence: 99%

Fault detection for air conditioning system using machine learning

Sulaiman

Abdullah

et al. 2020

IJ-AI

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Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.

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Section: Data Collectionmentioning

confidence: 99%

Fault detection for air conditioning system using machine learning

Sulaiman

Abdullah

et al. 2020

IJ-AI

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“…Lauro et al [136] used fuzzy logic to detect abnormal power consumption of building fan coil units. The proposed method involves analyzing fault detection using statistical methods, neighborhood and average value comparisons without errors, and employing a clustering technique.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand,

Ramaji,

Sadeghi

2023

Buildings

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Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

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“…Figure 1 shows the lab-scaled system developed in this research as described in [7], [8], [13]. It consists of a chiller, cooling tower, AHU, and two rooms to replicate an actual centralised chilled water air conditioning system.…”

Section: Lab Scaled Of Centralised Chilled Water Air Conditioning Systemmentioning

confidence: 99%

“…However, since the system itself is a complex and dynamic system, the development of mathematical modelling is complex and requires detailed information regarding the system and is challenging to derive [6]. In contrast, simplified mathematical modelling using a lumped parameter approach developed in [7], [8] are simpler to derive. However, the number of available fault models of air conditioning systems is still limited [9].…”

Section: Introductionmentioning

confidence: 99%

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

Sulaiman

Abdullah

et al. 2022

IJEECS

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Data-driven fault detection and diagnosis system (FDD) has been proven as simple yet powerful to identify soft and abrupt faults in the air conditioning system, leading to energy saving. However, the challenge is to obtain reliable operation data from the actual building. Therefore, a lab-scaled centralized chilled water air conditioning system was successfully developed in this paper. All necessary sensors were installed to generate reliable operation data for the data-driven FDD. Nevertheless, if a practical system is considered, the number of sensors required would be extensive as it depends on the number of rooms in the building. Hence, parameters impact in the dataset were also investigated to identify critical parameters for fault classifications. The analysis results had identified four critical parameters for data-driven FDD: the rooms' temperature (TTCx), supplied chilled water temperature (TCHWS), supplied chilled water flow rate (VCHWS) and supplied cooled water temperature (TCWS). Results showed that the data-driven FDD successfully diagnosed all six conditions correctly with the proposed parameters for more than 92.3% accuracy; only 0.6-3.4% differed from the original dataset's accuracy. Therefore, the proposed parameters can reduce the number of sensors used for practical buildings, thus reducing installation costs without compromising the FDD accuracy.

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Fuzzy Logic Control and Fault Detection in Centralized Chilled Water System

Cited by 9 publications

References 9 publications

Fault detection for air conditioning system using machine learning

Fault detection for air conditioning system using machine learning

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Parameter selection in data-driven fault detection and diagnosis of the air conditioning system

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