Abstract: A method and control for predicting the remaining useful life of an airfoil for a gas turbine engine includes the steps of monitoring conditions of the blade such as flutter, leaning, etc. A measured amount of deflection of the airfoil is compared to tabulated data to predict an expected crack length which is likely causing the deflection, etc. Once a predicted crack length has been identified, the amount of accumulated damage to the airfoil at the crack is monitored and stored. The amount of useful life for the blade can be predicted by compiling the accumulated damage over time. The useful life remaining can be displayed such that flight plans or maintenance schedules for the aircraft can be modified as appropriate.

Abstract: A method and control for predicting the remaining useful life of an airfoil for a gas turbine engine includes the steps of monitoring conditions of the blade such as flutter, leaning, etc. A measured amount of deflection of the airfoil is compared to tabulated data to predict an expected crack length which is likely causing the deflection, etc. Once a predicted crack length has been identified, the amount of accumulated damage to the airfoil at the crack is monitored and stored. The amount of useful life for the blade can be predicted by compiling the accumulated damage over time. The useful life remaining can be displayed such that flight plans or maintenance schedules for the aircraft can be modified as appropriate.

Abstract: The impending overheating of the compressor motor in a chiller is predicted by comparing measured output values in the chiller with output values predicted by passing said output values and chiller input values through a Kalman filter. Variances which exceed thresholds are used to predict the overheating condition. The Kalman filter is derived from a low order state space model of the chiller, the matrix values of which are derived by linear regression from the outputs and states resulting from random signal input excitation of a high order model of the chiller in a computer.

Abstract: A refrigerant monitoring system for a heating or cooling system includes a neural network that is used to compute the refrigerant charge in at least one refrigeration circuit of the system. The neural network is trained to learn certain characteristics of the heating or cooling system during a development mode of operation. The thus trained neural network timely computes refrigerant charge during a run time mode of operation. Information as to the computed refrigerant charge being at variance with the nominal amount of refrigerant charge for at least one refrigeration circuit is made available for assessment during the run time mode of operation.

Abstract: A system for monitoring an outdoor heat exchange coil of a heating or cooling system includes a neural network for computing the status of the coil. The neural network is trained during a development mode to learn certain characteristics of the heating or cooling system that will allow it to accurately compute the status of the coil. The thus trained neural network timely computes the status of the outdoor heat exchange coil during a run time mode of operation. Information as to the status of the coil is made available for assessment during the run time mode of operation.

Abstract: A monitoring system for a heating or cooling system includes a neural network for computing the status of one or more expansion valves within the system. The neural network is trained to learn certain characteristics of the heating or cooling system during a development mode of operation. The thus trained neural network timely computes the status of the one or more expansion valves during a run time mode of operation. Information as to the status of the one or more expansion valves is made available for real time assessment during the run time mode of operation.