Fault Identification and Fault Impact Analysis of The Vapor Compression Refrigeration Systems in Buildings: A System Reliability Approach

Abstract

The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the required operations and performance in these systems. Various components and subsystems are included in the VCRS, which need to be analyzed for system reliability. This research’s objective is conducting a comprehensive system reliability analysis on the VCRS by focusing on fault identification and determining the fault impacts on these systems. A typical VCRS in an office building is selected for this research regarding this objective. The corresponding reliability data, including the probability distributions and parameters, are collected from references to perform the reliability evaluation on the components and subsystems of the VCRS. Then the optimum distribution parameters have been obtained in the next step as the main findings. Additionally, by applying optimization techniques, efforts have been taken to maximize the system’s reliability. Finally, a comparison between the primary and the optimized systems (with new distribution parameters) has been performed over their lifetime to illustrate the system’s improvement percentage.

Air conditioning reliability analysis based on dynamic Bayesian network and Markov model

“…Chang et al [4] adopted failure mode and effect analysis (FMEA) to analyze the failure of scroll compressor for system air conditioning, and evaluated the reliability of scroll compressor through design of experiments (DOE). Mostafa et al [5] studied the reliability of the vapor compression refrigeration system (VCRS) components and optimized the overall reliability of the system by using the reliability block diagram (RBD) method and singleobjective genetic algorithm (GA). Unfortunately, the incompleteness and complexity of system information can lead to many uncertainties in the analysis process, which traditional reliability analysis methods are difficult to deal with.…”