Abstract: A system and method is provided for fault detection in a turbine engine. The fault detection system and method uses discrete event system modeling to provide improved fault diagnosis and prognosis. The fault detection system and method receives sensor taken at multiple dynamic events occurring in different time windows. An event determination mechanism evaluates the received sensor data to determine if specified events have occurred. Indications of the occurrence of specified events are then passed to a discrete event system model. The discrete event system model analyzes the timing and sequencing of the event occurrences to determine if a fault has occurred. This method does not require a detailed modeling of the system, and thus can be applied to complex systems such as turbine engines.

Abstract: A system and method is provided for fault detection in a turbine engine. The fault detection system and method uses discrete event system modeling to provide improved fault diagnosis and prognosis. The fault detection system and method receives sensor taken at multiple dynamic events occurring in different time windows. An event determination mechanism evaluates the received sensor data to determine if specified events have occurred. Indications of the occurrence of specified events are then passed to a discrete event system model. The discrete event system model analyzes the timing and sequencing of the event occurrences to determine if a fault has occurred. This method does not require a detailed modeling of the system, and thus can be applied to complex systems such as turbine engines.

Abstract: A system and method is provided for startup characterization in a turbine engine that provides the ability to accurately characterize engine startup. The startup characterization system and method uses engine temperature sensor data and engine speed sensor data to accurately characterize the startup of the turbine engine by determining when several key engine startup conditions are reached. By storing and analyzing engine sensor data taken during these key conditions of engine startup, the system and method is able to accurately characterize the performance of the engine during startup. This information can be used as part of a trending system to determine when faults in the start transient regime are occurring or likely to occur. Additionally, this information can be used in real time by control systems to better control the startup and operation of the turbine engine.

Abstract: A system and method is provided that facilitates improved fault detection. The fault detection system provides the ability to detect symptoms of fault in the fuel system of a turbine engine. The fault detection system captures selected data from the turbine engines that is used to characterize the performance of the fuel system. The fault detection system includes a feature extractor that extracts salient features from the selected sensor data. The extracted salient features are passed to a classifier that analyzes the extracted salient features to determine if a fault is occurring or has occurred in the turbine engine fuel system. 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 method of diagnosing a turbine engine includes the steps of acquiring engine operating parameters; calculating corresponding engine residual values; computing the mean and the standard deviation of each engine residual value; normalizing dynamically each engine residual value to yield normalized engine residuals; mapping, via a cluster technique mapping, the normalized engine residuals as input vectors into an engine condition space having clusters representing either normal vector engine conditions or faulty vector engine conditions; and identifying a closest cluster within the engine condition space to determine whether the engine under analysis is normal or faulty. A belief factor may be obtained as a function of the distances between the input vectors and specific clusters.

Abstract: Methods for forming a field effect transistor are disclosed. An illustrated method comprises: forming a gate electrode on a substrate; and forming a nitride layer on at least a part of the gate electrode and the substrate.

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 method and system for early detection of incipient faults in an electric motor are disclosed. First, current and voltage values for one or more phases of the electric motor are measured during motor operations. A set of current predictions is then determined via a neural network-based current predictor based on the measured voltage values and an estimate of motor speed values of the electric motor. Next, a set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.

Abstract: A method and system for early detection of incipient faults in an electric motor are disclosed. First, current and voltage values for one or more phases of the electric motor are measured during motor operations. A set of current predictions is then determined via a neural network-based current predictor based on the measured voltage values and an estimate of motor speed values of the electric motor. Next, a set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.