Abstract: A method for fault diagnosis of a bearing includes detecting, using an oil debris monitor (ODM) sensor, ODM data corresponding to an amount of debris flowing downstream from the bearing. The method also includes detecting, using a vibration sensor, vibration data corresponding to vibration of the bearing during use. The method also includes determining, by a controller, a vibration stage flag corresponding to a severity of damage of the bearing based on the vibration data. The method also includes determining, by the controller, a severity level of the damage of the bearing based on a combination of the vibration stage flag and the ODM data. The method also includes outputting, by an output device, the severity level.

Abstract: A fault isolation and ambiguity resolution system includes one or more analytic engines executable by a processing system and a reasoning system. The one or more analytic engines are operable to detect a fault associated with a monitored system based on data extracted from one or more data repository. The reasoning system includes a single fault isolator operable to identify an ambiguity group including the fault and one or more related faults of the monitored system. The reasoning system also includes an inference system operable to utilize evidence to resolve ambiguity between the fault and the one or more related faults of the ambiguity group as a diagnosis result.

Abstract: The present disclosure provides methods, systems, and computer-readable media for the fault detection and identification in an aircraft that may occur in real time during a flight, or any time the aircraft is operating. For example, a controller may receive and calculate various parameter values at various times during an aircraft flight, and compare those values to baseline values in order to determine if a fault has occurred. Additionally, the controller may identify a fault that has occurred by comparing a calculated fault signature value with a fault signature database comprising fault signatures and their associated faults.

Abstract: A method for fault diagnosis of a bearing includes detecting, using an oil debris monitor (ODM) sensor, ODM data corresponding to an amount of debris flowing downstream from the bearing. The method also includes detecting, using a vibration sensor, vibration data corresponding to vibration of the bearing during use. The method also includes determining, by a controller, a vibration stage flag corresponding to a severity of damage of the bearing based on the vibration data. The method also includes determining, by the controller, a severity level of the damage of the bearing based on a combination of the vibration stage flag and the ODM data. The method also includes outputting, by an output device, the severity level.

Abstract: A fault isolation and ambiguity resolution system includes one or more analytic engines executable by a processing system and a reasoning system. The one or more analytic engines are operable to detect a fault associated with a monitored system based on data extracted from one or more data repository. The reasoning system includes a single fault isolator operable to identify an ambiguity group including the fault and one or more related faults of the monitored system. The reasoning system also includes an inference system operable to utilize evidence to resolve ambiguity between the fault and the one or more related faults of the ambiguity group as a diagnosis result.

Abstract: System and methods for detecting anomalies and identifying faults of a gas turbine engine may include a recorder in communication with a processor. The recorder may be configured to capture archival data of the gas turbine engine. A flight normalizer module may be configured to produce normalized results based on the archival data. A flight parameter features module may be configured to generate flight parameter features based on the normalized results. A data warehouse module may be configured to determine suspected fault classes by comparing the flight parameter features against training parameter features stored in the data warehouse module based on queries from the flight parameter features module. A majority vote module may be configured to determine a diagnosed fault class based on the suspected fault classes.

Abstract: The present disclosure provides methods, systems, and computer-readable media for the fault detection and identification in an aircraft that may occur in real time during a flight, or any time the aircraft is operating. For example, a controller may receive and calculate various parameter values at various times during an aircraft flight, and compare those values to baseline values in order to determine if a fault has occurred. Additionally, the controller may identify a fault that has occurred by comparing a calculated fault signature value with a fault signature database comprising fault signatures and their associated faults.

Abstract: System and methods for detecting anomalies and identifying faults of a gas turbine engine may include a recorder in communication with a processor. The recorder may be configured to capture archival data of the gas turbine engine. A flight normalizer module may be configured to produce normalized results based on the archival data. A flight parameter features module may be configured to generate flight parameter features based on the normalized results. A data warehouse module may be configured to determine suspected fault classes by comparing the flight parameter features against training parameter features stored in the data warehouse module based on queries from the flight parameter features module. A majority vote module may be configured to determine a diagnosed fault class based on the suspected fault classes.

Abstract: An engine monitor includes a first tuned modeling unit, a second tuned modeling unit and a monitoring unit. The first tuned modeling unit models dynamics of a first engine by processing first control data with a first engine model to provide modeled first engine parameter data, and at least partially adjusts the modeled first engine parameter data for model error in the first engine model to provide first tuned parameter data. The second tuned modeling unit models dynamics of a second engine by processing second control data with a second engine model to provide modeled second engine parameter data, and at least partially adjusts the modeled second engine parameter data for model error in the second engine model to provide second tuned parameter data. The monitoring unit correlates the first and the second tuned parameter data to monitor operation of the first and the second engines.

Abstract: An example data assessment method for a diagnostic system includes receiving a set of initialization data, analyzing the set of initialization data to identify a data outlier in the set of initialization data, and determining whether the set of initialization data is bimodal. The method further includes establishing a set of cleaned data based on the analysis and establishing a baseline operating condition for the device using the set of cleaned data.

Abstract: A method of fusing information within a turbomachine health management system according to an exemplary aspect of the present disclosure includes, among other things, sending information collected within a first subsystem from the first subsystem to a system level reasoner, and adjusting a different, second subsystem based on the information from the first subsystem.

Abstract: The invention increases the fidelity of gas turbine engine module performance estimates that in turn enhance performance tracking and visibility. The methodology obviates input signal filtering since filtering may mask the onset and detection of a rapid measurement shift indicative of serious engine performance issues. The invention supports trend detection processes for timely detection, isolation and alerting of performance and engine system related faults.

Abstract: An example data assessment method for a diagnostic system includes receiving a set of initialization data, analyzing the set of initialization data to identify a data outlier in the set of initialization data, and determining whether the set of initialization data is bimodal. The method further includes establishing a set of cleaned data based on the analysis and establishing a baseline operating condition for the device using the set of cleaned data.

Abstract: A method for monitoring performance of an actuator is provided that includes the steps of: 1) determining an actuator response time constant in a control system, which actuator response time constant is representative of a task time taken by the actuator to complete a selected percentage of a task; 2) determining a rate of change of the actuator response time constant over a predetermined number of events; 3) determining a fault value indicative of when the actuator response time constant will equal or exceed an error time constant using the determined rate of change of the actuator response time constant; and 4) outputting a signal from the control system representative of the determined fault value.

Abstract: Systems and methods involving engine models are provided. A representative method for updating a gas turbine engine reference model includes: determining whether an absolute difference between a dominant time constant of a sensor and an assumed sensor model time constant is outside a tolerance value; and responsive to determining that the absolute difference is outside the tolerance value, updating the assumed sensor model time constant with the dominant time constant of the sensor.

Abstract: A method for monitoring performance of an actuator is provided that includes the steps of: 1) determining an actuator response time constant in a control system, which actuator response time constant is representative of a task time taken by the actuator to complete a selected percentage of a task; 2) determining a rate of change of the actuator response time constant over a predetermined number of events; 3) determining a fault value indicative of when the actuator response time constant will equal or exceed an error time constant using the determined rate of change of the actuator response time constant; and 4) outputting a signal from the control system representative of the determined fault value.

Abstract: A system and method for narrowing an assessment of root cause to a single stage within a gas turbine engine module using coupling factors and known instrumentation non-repeatability. Embodiments examine certain root cause (RC) effects that manifest themselves as coupling levels, or ratios (CR), between an engine module's efficiency change ?? and its flow parameter change ?FP.

Abstract: An apparatus for assessing health of a device comprising a data alignment module for receiving a plurality of sensory outputs and outputting a synchronized data stream, an analysis module for receiving the synchronized data stream and outputting at least one device health feature, and a high level diagnostic feature information fusion module for receiving the at least one device health feature and outputting a device health assessment.

Abstract: An isolation method and system is described for distinguishing between turbine case cooling (TCC) and high pressure turbine (HPT) performance faults. A trend is observed in gas path parameter data during cruise and a resulting percent ? signature across the shift in the gas path parameters is assignable to either an HPT or TCC performance fault. During either fault, exhaust gas temperature (EGT) will shift upward. Since take-off EGT margin is calculated from take-off data, the shift, or lack of shift in EGT margin may be used to differentiate between TCC and HPT faults.

Abstract: Systems and methods involving engine models are provided. A representative method for updating a gas turbine engine reference model includes: determining whether an absolute difference between a dominant time constant of a sensor and an assumed sensor model time constant is outside a tolerance value; and responsive to determining that the absolute difference is outside the tolerance value, updating the assumed sensor model time constant with the dominant time constant of the sensor.