SPC and Kalman filter-based fault detection and diagnosis for an air-cooled chiller

Abstract

Buildings worldwide account for nearly 40% of global energy consumption. The biggest energy consumer in buildings is the heating, ventilation and air conditioning (HVAC) systems. In HVAC systems, chillers account for a major portion of the energy consumption. Maintaining chillers in good conditions through early fault detection and diagnosis is thus a critical issue.

In this paper, the fault detection and diagnosis for an air-cooled chiller with air coming from outside in variable flow rates is studied. The problem is difficult since the air-cooled chiller is operating under major uncertainties including the cooling load, and the air temperature and flow rate. A potential method to overcome the difficulty caused by the uncertainties is to perform fault detection and diagnosis based on a gray-box model with parameters regarded as constants. The method is developed and verified by us in another paper for a water-cooled chiller with the uncertainty of cooling load. The verification used a Kalman filter to predict parameters of a gray-box model and statistical process control (SPC) for measuring and analyzing their variations for fault detection and diagnosis. The gray-box model in the method, however, requires that the air temperature and flow rate be nearly constant. By introducing two new parameters and deleting data points with low air flow rate, the requirement can be satisfied and the method can then be applicable for an air-cooled chiller. The simulation results show that the method with the revised model and some data points dropped improved the fault detection and diagnosis (FDD) performance greatly. It can detect both sudden and gradual air-cooled chiller capacity degradation and sensor faults as well as their recoveries.

Keywords

air-cooled chiller / fault detection and diagnosis (FDD) / statistical process control (SPC) / Kalman filter

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