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: The present invention is directed to a mathematical approach to detect faults by reconciling known data driven techniques with a physical understanding of the HVAC system and providing a direct linkage between model parameters and physical system quantities to arrive at classification rules that are easy to interpret, calibrate and implement. The fault modes of interest are low system refrigerant charge and air filter plugging. System data from standard sensors is analyzed under no-fault and full-fault conditions. The data is screened to uncover patterns though which the faults of interest manifest in sensor data and the patterns are analyzed and combined with available physical system information to develop an underlying principle that links failures to measured sensor responses. These principles are then translated into online algorithms for failure detection.

Abstract: A method for detecting and predicting refrigerant level includes the steps of determining an estimated value for a parameter indicative of refrigerant level and comparing that estimated value to an actual value. The difference between the actual and estimated value provides a refrigerant charge indicator value. The charge indicator value is indicative of the amount of refrigerant contained within the system. A change value is combined with the charge indicator value to provide a prediction for the future value of the charge indicator value. This future value is determined based on a rate of change and charge indicator value over a selected period of time.

Abstract: A refrigerant system is provided with a method and a control programmed to perform the method, in which a low charge of refrigerant is identified. The mass flow of refrigerant through the system is calculated utilizing at least two different methods. The two calculated mass flow rates are compared, and if they differ by more than predetermined amount, a determination is made that there is a low charge of refrigerant within the system.

Abstract: A bypass factor of an evaporator is used to indicate when an air filter of an HVAC is clogged. The bypass factor represents the amount of air that is bypassed without direct contact with the evaporator. As the air filter clogs, the bypass factor decreases. The bypass factor can also be used for early detection of clogging of the air filter. A first bypass factor is calculated by using the temperature measurements, and a second bypass factor is calculated by using the airflow rate of the air. The difference between the two bypass factors determines the error. An increase in the error indicates that the air filter is clogged. A coefficient of performance of the evaporator can also be calculated to detect if the air filter is clogged. A decrease in the coefficient of performance indicates that the air filter is clogged.

Abstract: The temperature of the air exiting an evaporator and the relative humidity of the air entering and exiting the evaporator can be calculated by using existing sensors in a vapor compression system. The temperature of the air exiting the evaporator is calculated by using the detected temperature of the air entering the evaporator, the saturation temperature of the air, and a bypass factor. The relative humidity of the air entering and exiting the evaporator are then estimated using a psychrometric chart. By using the existing sensors to determine the temperature of the air exiting the evaporator and the relative humidity of the air entering and exiting the evaporator, the load requirement of the vapor compression system can be calculated without employing additional sensors. The system capacity of the vapor compression system can be matched to the load requirement to allow the effective use of electric power.

Abstract: The present invention is directed to a mathematical approach to detect faults by reconciling known data driven techniques with a physical understanding of the HVAC system and providing a direct linkage between model parameters and physical system quantities to arrive at classification rules that are easy to interpret, calibrate and implement. The fault modes of interest are low system refrigerant charge and air filter plugging. System data from standard sensors is analyzed under no-fault and full-fault conditions. The data is screened to uncover patterns though which the faults of interest manifest in sensor data and the patterns are analyzed and combined with available physical system information to develop an underlying principle that links failures to measured sensor responses. These principles are then translated into online algorithms for failure detection.

Abstract: A method for detecting and predicting refrigerant level includes the steps of determining an estimated value for a parameter indicative of refrigerant level and comparing that estimated value to an actual value. The difference between the actual and estimated value provides a refrigerant charge indicator value. The charge indicator value is indicative of the amount of refrigerant contained within the system. A change value is combined with the charge indicator value to provide a prediction for the future value of the charge indicator value. This future value is determined based on a rate of change and charge indicator value over a selected period of time.

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: A method for estimating the bone quality, or skeletal status of a vertebrate on the basis of two-dimensional image data of cortical bone, and which is determined from a density variation of the cortical bone. Intra cortical resorption, scalloping, spongiosation, striation, periosteal resorption, and/or endosteal resorption caused by osteoporosis, osteopenia or hyperparathyroidism may be determined as oblong density variations detected by filtering the image data with two oblong Gaussian kernels (using a Difference Of Gaussian technique) or at corresponding frequencies in the power spectrum.