Abstract: An improved fault detection system and method is provided. The fault detection system and method combines the use of discrimination and representation based feature extraction to reliably detect symptoms of faults in turbine engines. Specifically, the fault detection system and method uses a kernel-based Maximum Representation Discrimination Features (MRDF) technique to detect symptoms of fault in turbine engines. The kernel-based MRDF system and method combines the use of discriminatory features and representation features in historical sensor data to facilitate feature extraction and classification of new sensor data as indicative fault in the turbine engine. Furthermore, the kernel-based MRDF technique facilitates the uncovering of nonlinear features in the sensor data, thus improving the reliability of the fault detection.

Abstract: A system and method that provides improved fault detection in turbine engines is disclosed. The fault detection system provides the ability to detect symptoms of engine faults based on a relatively limited number of engine parameters that are sampled relatively infrequently. The fault detection system includes a sensor data processor that receives engine sensor data during operation and augments the sensor data. The augmented data set is passed to a fuzzy logic inference system that determines the likelihood that a fault has occurred. The inference system output can then be passed to a diagnostic system where evaluation of the output may yield a detailed diagnostic result and a prediction horizon.

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: Described are methods and apparatus, including computer program products, for controlling time-sensitive data in a packet-based network. Data associated with a call is received, and the data includes an identifier associated with the data, a data source, or one or more data destinations. The data is associated with a logical trunk group selected from a plurality of logical trunk groups each in communication with the one or more data destinations over a packet-based network. The logical trunk group is selected based in part on the identifier. Calls through the packet-based network are managed based in part on the associated logical trunk group.

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 pertaining to a design of experiments. The method comprises generating a data set from historical data; identifying and removing any fault data points in the data set so as to create a revised data set; supplying the data points from the revised data set into a nonlinear neural network model; and deriving a simulator model characterizing a relationship between the input variables and the output variables. The apparatus comprises means for generating a data set from historical data; means for identifying and removing any fault data points in the data set so as to create a revised data set; means for supplying the data points from the revised data set into a nonlinear neural network model; and means for deriving a simulator model characterizing a relationship between the input variables and the output variables.

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 system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships between two or more variables. The fault detection system uses a neural network to perform feature extraction from data for representation of faulty or normal conditions. The values of extracted features, referred to herein as scores, are then used to determine the likelihood of fault in the system. Specifically, the lower order scores, referred to herein as “approximate null space” scores can be classified into one or more clusters, where some clusters represent types of faults in the turbine engine. Classification based on the approximate null space scores provides the ability to classify faulty or nominal conditions that could not be reliably classified using higher order scores.

Abstract: A system and method is provided for lightoff detection in a turbine engine that provides improved accuracy and consistency. The system and method use engine temperature and engine rotation speed sensor data to determine a first time interval during which the lightoff is estimated to have occurred. A peak gradient in the temperature sensor data within the first time interval and a peak gradient in the engine rotation sensor data within the first time interval are then determined. The temperature peak gradient time and the engine rotation peak gradient time are then used as boundaries to determine a narrowed time interval within the first time interval. The minimum gradient in the engine rotation sensor data in the narrowed time interval is determined, with the minimum gradient time in the engine rotation sensor data comprising an accurate estimate of lightoff time for the turbine engine.

Abstract: A transient fault detection system and method is provided that facilitates improved fault detection performance in transient conditions. The transient fault detection system provides the ability to detect symptoms of fault in engine that occur in transient conditions. The transient fault detection system includes a feature extractor that measures sensor data during transient conditions and extracts salient features from the measured sensor data. The extracted salient features are passed to a classifier that analyzes the extracted salient features to determine if a fault has occurred during the transient conditions. Detected faults can then be passed to a diagnostic system where they can be passed as appropriate to maintenance personnel.

Abstract: A condition assessment and end-of-life prediction system that includes a virtual condition assessment instrument and a virtual end-of-life prediction instrument. The virtual condition assessment instrument measures the condition of the equipment and includes a data capture subsystem for sampling a set of analog signals and converting them into digital signals, a model-based component to estimate disturbances and predict an expected response, a signal-based component to process output from the model-based component, a classification component to process output from the signal-based component, a fuzzy logic expert component to combine information from the classification component and the model-based component in order to assess the condition of the equipment, and a condition assessment panel to display the condition of the equipment.

Abstract: A transient fault detection system and method is provided that facilitates improved fault detection performance in transient conditions. The transient fault detection system provides the ability to detect symptoms of engine faults that occur in transient conditions. The transient fault detection system includes a Hidden Markov Model detector that receives sensor data during transient conditions and determines if a fault has occurred during the transient conditions. Detected faults can then be passed to a diagnostic, system where they can be passed as appropriate to maintenance personnel.

Abstract: A transient fault detection system and method is provided that facilitates improved fault detection performance in transient conditions. The transient fault detection system provides the ability to detect symptoms of fault in engine that occur in transient conditions. The transient fault detection system includes a feature extractor that measures sensor data during transient conditions and extracts salient features from the measured sensor data. The extracted salient features are passed to a classifier that analyzes the extracted salient features to determine if a fault has occurred during the transient conditions. Detected faults can then be passed to a diagnostic system where they can be passed as appropriate to maintenance personnel.

Abstract: A transient fault detection system and method is provided that facilitates improved fault detection performance in transient conditions. The transient fault detection system provides the ability to detect symptoms of engine faults that occur in transient conditions. The transient fault detection system includes a Hidden Markov Model detector that receives sensor data during transient conditions and determines if a fault has occurred during the transient conditions. Detected faults can then be passed to a diagnostic, system where they can be passed as appropriate to maintenance personnel.

Abstract: The apparatus (101) and method for spectrum management in a multipoint communication system controls upstream channel usage for secondary stations (110) transmitting information to a primary station (101) and downstream channel usage for secondary stations (110) receiving information from a primary station (101). The preferred apparatus (101) embodiment includes a processor arrangement (120) having a master controller (121) and a plurality of processors (130), with the processor arrangement connected to a channel interface (125). The apparatus (101) and method controls channel load balancing, channel congestion, and channel assignment in a multipoint communication system, and controls upstream channels independently from downstream channels. Factors and parameters utilized in such channel control and allocation include error parameters, channel noise parameters, transmit and receive loading factors, and congestion parameters.