Abstract: A method and system for detecting and predicting air filter condition for an air handling system operates by determining a system resistance to air flow. The system resistance is utilized to determine a detection statistic indicative of current filter condition and to predict remaining life of the air filter. The system resistance is determined using models that approximate the expected operation of the air handling system. The approximation is then compared to actual values to obtain a difference. Once the difference between the approximated value exceeds a threshold value, an alarm is initiated that is indicative of system resistance. The remaining air filter life is then determined by using historically gathered data, or by using a known degradation rate of the air filter. Once the remaining life of the air filter is estimated, replacement can be scheduled that would coincide with other maintenance.

Abstract: A method of diagnosing sensor faults for a heating, ventilation and air conditioning system includes the steps of creating a component model for a specific component within the system. The component model is created through the use of commonly available manufacturing data. Data within the system is input into the component model and compared to calculated and predicted values that are also calculated using the identical component models. Differences between the calculated and actual values is determined and compared to a threshold difference value. If the difference exceeds the threshold value, then a fault is detected. The specific type of sensor fault is determined using probability distribution analysis. Each type of sensor fault produces a different type of statistical deviation from normal distribution.

Abstract: An elevator system (20) includes wireless communicating portions (40, 42) that communicate with each other to provide elevator cab (22) position information within a hoistway (24). In one example, a first communicating portion (40) is supported on the elevator cab (22) that generates a radio frequency trigger signal (58) that is received by a second communicating portion (42) at a selected position along the hoistway (24). The second communicating portion (42) responsively generates an ultrasound signal (64) that is received by the first communicating portion (40). A characteristic of the received locating signal, such as the timing between the trigger signal and the receipt of the locating signal, provides position information regarding the cab within the hoistway.

Abstract: A vapor compression system includes a compressor, a gas cooler, an expansion device, and an evaporator. Refrigerant is circulated through the closed circuit cycle. Preferably, carbon dioxide is used as the refrigerant. Adaptive control is employed to optimize the coefficient of performance of the vapor compression system. As the system changes over time, a model that operates the system is modified. The model is determined by an adaptive control algorithm including variable coefficients. As the model changes, the variables of the adaptive control algorithm change. A control of the gas cooler is then adjusted to regulate the high pressure of the system, and therefore the coefficient of performance. In a first example, Least Mean Squares (LMS) is used to modify the variables of the adaptive control algorithm to optimize the coefficient of performance. In a second example, the coefficient of performance is optimized by a slowly varying periodic excitation method.

Abstract: A vapor compression system includes a compressor, a gas cooler, an expansion device, and an evaporator. Refrigerant is circulated through the closed circuit cycle. Preferably, carbon dioxide is used as the refrigerant. Adaptive control is employed to optimize the coefficient of performance of the vapor compression system. As the system changes over time, a model that operates the system is modified. The model is determined by an adaptive control algorithm including variable coefficients. As the model changes, the variables of the adaptive control algorithm change. A control of the gas cooler is then adjusted to regulate the high pressure of the system, and therefore the coefficient of performance. In a first example, Least Mean Squares (LMS) is used to modify the variables of the adaptive control algorithm to optimize the coefficient of performance. In a second example, the coefficient of performance is optimized by a slowly varying periodic excitation method.