Fault and sensor error diagnostic strategies for a vapor compression refrigeration system by using fuzzy inference systems and artificial neural network

• New approaches are developed based on fuzzy inference system and artificial neural network.
• Fault and sensor error diagnostic strategies are supported by experimental measurement of a refrigeration system.
• New approaches have much more diagnostic ability to diagnose automatically the system faults and the errors of the sensors.

A fuzzy inference system (FIS) and an artificial neural network (ANN) were used for diagnosis of the faults of a vapor compression refrigeration experimental setup. A separate FIS was developed for detection of sensor errors. The fault estimation error of the FIS and ANN were evaluated by using the experimentally obtained sensor data. Separate FIS estimated the system faults and detected defective sensors in all test cases without any error. Levenberg Marquart (LM) type ANN algorithm was implemented to diagnose the system faults. Scaled conjugate gradient (SCG) and resilient backpropagation (RB) network type were also used to compare performances with the estimation of the LM algorithm. The LM type ANN estimated all fault conditions accurately in the test cases never observed before. The study demonstrated that the FIS and ANN could be used effectively to estimate the faulty conditions of the vapor compression refrigeration system.