Abstract: A condition-based maintenance system having instrumentation for collecting data from one or more wind turbines and having a performance monitor for analyzing the data. Also, the system may have a wind turbine anomaly detector. Information from the performance monitor and the anomaly detector may be used for indicating conditions of the one or more wind turbines. These conditions may be a basis for determining maintenance recommended for any of the wind turbines.

Abstract: Methods and systems for detecting features in thin-wall structures are provided. A plurality of ultrasonic waves are generated within a thin-wall structure. At least one echo from the plurality of ultrasonic waves within the thin-wall structure is detected. The at least one echo is processed to determine a position of at least one feature in the thin-wall structure based on Embedded Ultrasonic Structure Radar (EUSR) beamforming performed in the frequency domain.

Abstract: A computer implemented method includes providing information related to wind speed and direction prediction to a wind turbine energy prediction module, providing wind energy production prediction information to a resource allocation engine, and combining the wind energy production prediction information with information about non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

Abstract: A computer implemented method includes receiving condition information from a plurality of condition detection sensors coupled to a wind turbine and receiving wind turbine controller information. An anomaly detection algorithm is applied to identify maintenance activities for the wind turbine as a function of both the wind turbine condition information and the wind turbine controller information.

Abstract: Methods and apparatus are provided for detecting an anomaly in fuel demand data and fuel supply data generated by a fuel metering system for an engine. The method comprises collecting the fuel demand data and the fuel supply data during operation of the fuel metering system, generating expected fuel supply data based on the collected fuel demand data and a nominal response model describing the expected behavior of the fuel metering system, and detecting the anomaly if a difference between the expected fuel supply data and the collected fuel supply data exceeds a predetermined threshold.

Abstract: Methods and apparatus are provided for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft. The methods and apparatus comprise the following steps of, and/or means for, generating a data set from historical data representative of a plurality of VCS operating conditions over time, identifying one or more steady-state data points in the generated data set, forming a revised data set that includes at least the steady-state data points, using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables, supplying the derived values for the plurality of minimally correlated input variables and the corresponding values for the VCS refrigerant charge in the revised data set to a nonlinear neural network model, and deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.

Abstract: A method for performing diagnostics for an engine comprises the steps of identifying an engine component as potentially being related to operational data of an engine, calculating a deviation from a thermodynamic model, and comparing the deviation with root cause deviation measures. The deviation relates the engine component to an adjustment to the thermodynamic model with respect to a variable of the thermodynamic model, based at least in part on the operational data. Each root cause deviation measure relates one of a plurality of potential root causes to the thermodynamic model with respect to the variable of the thermodynamic model.

Abstract: Methods and apparatus are provided for predicting ice formation on and shedding from blades of an air cycle machine (“ACM”) turbine. The method comprises generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, determining an amount of ice formation on the blades of the ACM turbine, and determining an amount of ice shed from the blades of the ACM turbine. The apparatus comprises means for generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, means for determining an amount of ice formation on the blades of the ACM turbine, and means for determining an amount of ice shed from the blades of the ACM turbine. In some embodiments, the means and apparatus may also involve predicting ACM wear, based at least in part on the predicted ice formation and shedding.

Abstract: Methods and apparatus are provided for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft. The methods and apparatus comprise the following steps of, and/or means for, generating a data set from historical data representative of a plurality of VCS operating conditions over time, identifying one or more steady-state data points in the generated data set, forming a revised data set that includes at least the steady-state data points, using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables, supplying the derived values for the plurality of minimally correlated input variables and the corresponding values for the VCS refrigerant charge in the revised data set to a nonlinear neural network model, and deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.

Abstract: Methods and apparatus are provided for predicting ice formation on and shedding from blades of an air cycle machine (“ACM”) turbine. The method comprises generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, determining an amount of ice formation on the blades of the ACM turbine, and determining an amount of ice shed from the blades of the ACM turbine. The apparatus comprises means for generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, means for determining an amount of ice formation on the blades of the ACM turbine, and means for determining an amount of ice shed from the blades of the ACM turbine. In some embodiments, the means and apparatus may also involve predicting ACM wear, based at least in part on the predicted ice formation and shedding.

Abstract: A model reduction system and method that facilitates improved component lifing is provided. The model reduction system and method uses a range of operating conditions and system identification techniques to reduce a physics-based component model. Specifically, system identification techniques are used to create a reduced component model. The reduced component model facilitates the use of measured operating conditions in calculating component lifing. Specifically, the reduced component lifing model provides the ability to predict selected parameters of interest at specified critical locations without requiring excessive computations. Thus, the reduced component model can be used with actual measured operating conditions to calculate component lifing over the life of the component. Thus, the reduced component lifing model facilitates improved component lifing calculation.