Predictive Maintenance for Air-Conditioning Refrigeration and Heat Pump Systems : Data-driven Condition Monitoring and Fault Detection for Compressors
Year: 2024
Type: dissertation
Abstract: Heat pump and refrigeration systems consume a large amount of energy, and faults in these systems can cause significant wasted energy, so it is important to perform predictive maintenance for them. We propose a predictive maintenance approach that monitors isentropic efficiency in compressors based on historical data and machine learning. The results of monitoring can be used to detect faults in the systems. We predict the isentropic efficiency and compare it with the measured value to detect a fault, which is viewed as the difference between prediction and measurement. The difference implies that the current pattern of the system operation is different from the pattern of the time when historical data were collected. Isentropic efficiency is selected as an indicator that characterizes the operating conditions of compressors, using suitable feature variables for isentropic efficiency prediction. Real data of representative systems are selected for validation. Different structures and optimizers of neural networks are designed and validated. Results show that such models can accurately predict the isentropic efficiency and exhibit better performance than linear models and support vector regression. The problem of an imbalanced dataset is also studied. A Gaussian Mixture Model is used to analyse the distribution of historical data. A method is proposed that decides whether the model should be updated with new data when the distribution of input data has changed. It is assumed that if a proportion of new data proves that the system is fault-free and the rest of the data follow a different distribution and thus cannot provide persuasive results, the system is fault-free for the whole period. The model of one system is also applied on another similar system, in order to investigate the generalization performance of the model.
Author: Song, Ci
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7042516261