18 May 2017
The overall objective of this paper is to develop a data-driven method using principle component analysis for fault detection and diagnosis for ground source heat pumps that can be used by servicemen to assist them to accurately detect and diagnose faults during the operation of the heat pump. Two semi-empirical models for heat pump unit were built to simulate fault free and faulty conditions in the heat pump. Both models have been cross-validated by fault free experimental data. Then the models were used to simulate several faults. Principle component analysis is used to reduce the dimensionality of the system. Then simple clustering technique is used for operation conditions classification and fault detection and diagnosis process. Each fault is represented by four clusters connected with three lines where each cluster represents different fault intensity level. The fault detection is performed by measuring the shortest orthogonal distance between the test point and the lines connecting the faults’ clusters. Simulated fault free and faulty data are used to train the model. Then, a new set of simulated data for faults is used to test the model and the model successfully detected and diagnosed all faults type and intensity level of the tested faults for different operation conditions with 98% accuracy. The fault detection and diagnosis method used simple nine temperatures and the electrical power, as an input to the fault detection and diagnosis model. Finally, a user friendly graphical user interface is built to facilitate the model operation by the serviceman.