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 method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

Abstract: A system and method for blade health inspection is provided. The system may include a health monitoring processor, an excitation actuator, and a health monitoring sensor. The excitation actuator may pulse a force against an engine blade to cause non-integral vibratory excitations in engine blades. The health monitoring sensor may measure the vibratory excitations. The health monitoring processor may analyze the vibratory excitations to determine the health of the engine blade.

Abstract: A system and method for detecting foreign object debris damage is disclosed. A method for foreign object debris detection in a gas turbine engine may include receiving, by a controller, a first time-series data from a database, wherein the first time-series data comprises a feature, pre-processing, by the controller, the first time-series data to generate a second time-series data, generating a third time-series data via an anomaly detector model, sending, by the controller, the third time-series data to a foreign object debris (FOD) damage model, and determining, by the controller, that a FOD event has occurred based on data received from the FOD damage model. In various embodiments, the method may further comprise generating, by the controller, a health report (HR).

Abstract: A debris monitoring system has a first sensor configured to generate a first signal indicating a presence of a metallic particle in a lubrication system. A second sensor is configured to generate a second signal indicating the presence of a metallic particle in the lubrication system. A signal processor is configured to determine a presence of a metallic particle in a fluid passage based on a comparison of at least the first signal and the second signal; the second signal being used to verify accuracy of the first signal. A gas turbine engine and a method for monitoring a fluid passage for debris are also disclosed.

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 system and method for debris particle detection with adaptive learning are provided. The method includes receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database, detecting a feature in the ODM sensor data, generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data, selecting a maintenance action request based on the anomaly detection signal, and adjusting one or more of the feature, the anomaly, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data, and feedback from field maintenance of one or more engines that evolves over time.

Abstract: A system for identifying timeframes for borescope inspections for a gas turbine engine, having: monitoring systems monitor engine conditions, derive engine condition information and communicate such information to processors; feature interpreter module electronically communicates (i) with processors to process engine condition information, and (ii) feature interpreter information to processors, such information defines a probability of a FOD event; accumulator module electronically communicates (i) with processors to processes feature interpreter information, stored information since a last borescope inspection, and (ii) accumulator information to processors, such information defines an accumulated probability of a FOD event; and predictor and inspection planner module electronically communicates (i) with processors to processes accumulator information and stored information defines a threshold limits for a probability of a FOD event, and (ii) predictor and inspection planner information to processors, such i

Abstract: An article of manufacture including a tangible, non-transitory computer-readable storage medium having instructions stored thereon for detecting a fault in a gas turbine engine that, in response to execution by a controller, cause the controller to perform operations comprising receiving, by the controller, a first data output from a first flight data source; receiving, by the controller, a second data output from a second flight data source; determining, by the controller, a first flight realm of the first data output based on the second data output; identifying, by the controller, a fault threshold using a first parameter of the first flight realm; and comparing, by the controller, the first data output to the fault threshold.

Abstract: A gas turbine engine, a method, and a system for detecting oil debris are provided. The gas turbine engine includes an oil debris monitor sensor configured to detects oil debris in an oil flow, and generate a sensor signal based on the detected oil debris, a controller configured to control the oil flow through the oil debris monitor sensor using a plurality of valves, and a signal processor configured to receive the sensor signal from the oil debris monitor and to receive a valve system configuration from the controller, the signal processor further configured to generates a health indicator based on the sensor signal and valve configuration.

Abstract: A method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

Abstract: An article of manufacture including a tangible, non-transitory computer-readable storage medium having instructions stored thereon for detecting a fault in a gas turbine engine that, in response to execution by a controller, cause the controller to perform operations comprising receiving, by the controller, a first data output from a first flight data source; receiving, by the controller, a second data output from a second flight data source; determining, by the controller, a first flight realm of the first data output based on the second data output; identifying, by the controller, a fault threshold using a first parameter of the first flight realm; and comparing, by the controller, the first data output to the fault threshold.

Abstract: A system for identifying timeframes for borescope inspections for a gas turbine engine, having: monitoring systems monitor engine conditions, derive engine condition information and communicate such information to processors; feature interpreter module electronically communicates (i) with processors to process engine condition information, and (ii) feature interpreter information to processors, such information defines a probability of a FOD event; accumulator module electronically communicates (i) with processors to processes feature interpreter information, stored information since a last borescope inspection, and (ii) accumulator information to processors, such information defines an accumulated probability of a FOD event; and predictor and inspection planner module electronically communicates (i) with processors to processes accumulator information and stored information defines a threshold limits for a probability of a FOD event, and (ii) predictor and inspection planner information to processors, such i

Abstract: A system and method for blade health monitoring is provided. The system and method may improve health diagnostic capabilities for engine blades, thus reducing the risk of blade failure during engine operation, increasing the useful life of each engine blade, and allowing for more accurate maintenance and/or inspection schedules of engine blades. The system may include a health monitoring processor, an excitation actuator, and a health monitoring sensor. The excitation actuator may pulse a force against an engine blade to cause non-integral vibratory excitations in engine blades. The health monitoring sensor may measure the vibratory excitations. The health monitoring processor may analyze the vibratory excitations to determine the health of the engine blade.

Abstract: A method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

Abstract: A system and method for detecting foreign object debris damage is disclosed. A method for foreign object debris detection in a gas turbine engine may include receiving, by a controller, a first time-series data from a database, wherein the first time-series data comprises a feature, pre-processing, by the controller, the first time-series data to generate a second time-series data, generating a third time-series data via an anomaly detector model, sending, by the controller, the third time-series data to a foreign object debris (FOD) damage model, and determining, by the controller, that a FOD event has occurred based on data received from the FOD damage model. In various embodiments, the method may further comprise generating, by the controller, a health report (HR).

Abstract: A method for foreign object debris detection in a gas turbine engine may comprise receiving, by a controller, a plurality of master configuration parameters, receiving, by the controller, a plurality of individual configuration parameters in response to the plurality of master configuration parameters, receiving, by the controller, a first time-series data from a database, pre-processing, by the controller, the first time-series data to generate a second time-series data, implementing, by the controller, an anomaly detector model, the anomaly detector model configured to generate a third time-series data, and implementing, by the controller, a foreign object debris (FOD) damage model, the FOD damage model receiving the third time-series data. The pre-processing the first time-series data, the implementing the anomaly detector model, and the implementing the FOD damage model may be defined by the plurality of individual configuration parameters.

Abstract: A system and method for debris particle detection with adaptive learning are provided. The method includes receiving oil debris monitoring (ODM) sensor data from an oil debris monitor sensor and fleet data from a database, detecting a feature in the ODM sensor data, generating an anomaly detection signal based on detecting an anomaly by comparing the feature in the ODM sensor data to a limit defined by system information stored in the fleet data, selecting a maintenance action request based on the anomaly detection signal, and adjusting one or more of the feature, the anomaly, the limit, and the maintenance action request by applying an adaptive learning algorithm that uses the ODM sensor data, fleet data, and feedback from field maintenance of one or more engines that evolves over time.

Abstract: A gas turbine engine, a method, and a system for detecting oil debris are provided. The gas turbine engine includes an oil debris monitor sensor configured to detects oil debris in an oil flow, and generate a sensor signal based on the detected oil debris, a controller configured to control the oil flow through the oil debris monitor sensor using a plurality of valves, and a signal processor configured to receive the sensor signal from the oil debris monitor and to receive a valve system configuration from the controller, the signal processor further configured to generates a health indicator based on the sensor signal and valve configuration.

Abstract: A debris monitoring system has a first sensor configured to generate a first signal indicating a presence of a metallic particle in a lubrication system. A second sensor is configured to generate a second signal indicating the presence of a metallic particle in the lubrication system. A signal processor is configured to determine a presence of a metallic particle in a fluid passage based on a comparison of at least the first signal and the second signal; the second signal being used to verify accuracy of the first signal. A gas turbine engine and a method for monitoring a fluid passage for debris are also disclosed.