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https://scholarbank.nus.edu.sg/handle/10635/211996
Title: | FAULT DETECTION AND DIAGNOSIS FOR CHILLERS USING RECURRENT NEURAL NETWORKS | Authors: | SIM SER LYN | Keywords: | Recurrent Neural Network Fault Detection Fault Detection and Diagnosis Neural Network Heating Ventilation and Air-Conditioning System Building Maintenance |
Issue Date: | 13-Dec-2021 | Citation: | SIM SER LYN (2021-12-13). FAULT DETECTION AND DIAGNOSIS FOR CHILLERS USING RECURRENT NEURAL NETWORKS. ScholarBank@NUS Repository. | Abstract: | Heating, Ventilation and Air-Conditioning (HVAC) chiller systems require constant maintenance, where faulty operations can result in untimely costly replacements and an overall drop in HVAC system efficiency of the building. A big part of building energy consumption is attributed to chillers where early fault diagnosis can prevent further deterioration and reduced energy usage. The aim of this dissertation study is to develop a reliable and accurate fault detection and diagnosis tool for centrifugal chillers. This project analyses the various researched FDD methods that are currently utilised, before proposing the selected generic artificial intelligent FDD strategy of a sequential Recurrent Neural Network (RNN) with long short-term memory (LSTM) layers. As compared to other commonly used neural network structures such as ANN and CNN, RNN is a more suitable candidate for chillers due to its ability to identify recurrent relationships and capture sequential information presented by soft faults. In particular, the LSTM architecture retains fault memory over long periods of time, due to the presence of a special ‘memory block’ (Hochreiter & Schmidhuber, 1997). The chiller data used in training the RNN model is derived from ASHRAE RP-1043, in which 7 typical single faults were identified at 4 severity levels. Our RNN model was capable of detecting and diagnosing chiller faults effectively, with a 92.2% accuracy rate, alongside a micro-average Area Under Receiver Operating Characteristic (AUROC) score of 0.955. | URI: | https://scholarbank.nus.edu.sg/handle/10635/211996 |
Appears in Collections: | Bachelor's Theses |
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