It is necessary that a reliable and optimal control system has the ability of fault tolerance, which recovers the faulty and/or missed data in time. Principal component analysis (PCA) is presented to model HVAC monitored systems by using the measured data under normal operation condition (NOC). PCA splits the measurement space into two subspaces, one principal component subspace (PCS) and the other residual subspace (RS). When the faulty or missed data is observed, it will be projected into PCS and RS. It is then recovered by sliding the faulty or missed data to PCS via iteration. Examples of HVAC monitoring system have demonstrated that the approach has good performance to recover faulty or missed data and thus can be embedded into the system to achieve fault-tolerant control.

The application of thermoeconomic optimization design in an air-conditioning system is important in achieving economical life cycle cost. Previous work on thermoeconomic optimization mainly focused on directly calculating exergy input into the system. However, it is usually difficult to do so because of the uncertainty of input power of fan on the air side of the heat-exchanger and that of pump in the system. This paper introduces a new concept that exergy input into the system can be substituted for the sum of exergy destruction and exergy output from the system according to conservation of exergy. Although it is also dicult for a large-scale system to calculate exergy destruction, it is feasible to do so for a small-scale system, for instance, villa air conditioner (VAC). In order to perform thermoeconomic optimization, a program is firstly developed to evaluate the thermodynamic property of HFC134a on the basis of Martin-Hou state equation. Authors develop thermodynamic and thermoeconomic objective functions based on second law and thermoeconomic analysis of VAC system. Two optimization results are obtained. The design of VAC only aimed at decreasing the energy consumption is not comprehensive. Life cycle cost at thermoeconomic optimization is lower than that at thermodynamic optimization.