Abstract: A propulsion system for an aircraft is disclosed and includes a water recovery system and a water monitoring system. The water recover system includes a condenser that is arranged along the core flow path and is configured to extract water from an exhaust gas flow. The water monitoring system includes a sensor and a controller programed to determine a condition of water and generate a prompt based on information communicated from the at least one sensor.

Abstract: A propulsion system for an aircraft includes a hydrogen fuel system, a water recovery system and a water pressure and quantity monitoring system. The water recovery system uses a condenser to extract water from an exhaust gas flow. The water pressure and quantity monitoring system measures water pressures and quantities at various locations in the water recovery system to assess the health and efficiency of the water recovery/supply system and the propulsion system.

Abstract: A propulsion system for an aircraft includes a hydrogen fuel system, a water recovery system and a water pressure and quantity monitoring system. The water recovery system uses a condenser to extract water from an exhaust gas flow. The water pressure and quantity monitoring system measures water pressures and quantities at various locations in the water recovery system to assess the health and efficiency of the water recovery/supply system and the propulsion system.

Abstract: A propulsion system for an aircraft is disclosed and includes a water recovery system and a water monitoring system. The water recover system includes a condenser that is arranged along the core flow path and is configured to extract water from an exhaust gas flow. The water monitoring system includes a sensor and a controller programed to determine a condition of water and generate a prompt based on information communicated from the at least one sensor.

Abstract: A method to effectively utilize an oil debris monitor to provide advanced warning to mechanical system failures in a high noise system by adapting detection and annunciation algorithms to the background noise in a system that includes collecting I and Q channel data from a sensor; processing the I and Q channel data to both calculate a noise based (RMS adjusted) detection threshold and to identify a ferrous and nonferrous signal; processing the ferrous and nonferrous signals to determine signal peaks above the RMS adjusted detection threshold; adjusting a detection threshold if more signal peaks observed than allowable particles in a given time window; transmitting the detection threshold previously determined to particle detection, rate limit adjustment, detectability algorithms, and estimated mass loss accumulation.

Abstract: An engine parameter estimation tuning system includes an engine parameter estimator unit and a gas path analysis (GPA) unit. The engine parameter estimator unit includes an onboard model (OBM) configured to output estimated parameters based on operation of a gas turbine engine. The gas path analysis (GPA) unit includes a performance health monitor unit configured to adjust a long-term deterioration parameter independently from adjustment of a short-term tuning parameter to tune one or more targeted estimation parameters included in the estimated engine parameter. In this manner, the engine parameter estimation tuning system can realize the different time scales associated with uncertainties in an engine and accommodate them separately so that the estimated engine parameters become much more accurate.

Abstract: A method for fault identification for a gas turbine engine includes receiving sensor data from at least one health monitoring sensor of a health monitoring system for a gas turbine engine; utilizing a pre-filter to filter the sensor data and obtain filtered data based on a plurality of signatures of fault conditions of the health monitoring system and the gas turbine engine; utilizing at least one transfer function to extract features from the filtered sensor data based on the plurality of signatures; utilizing a machine learning technique to analyze the extracted features, and determine whether any of the extracted features are indicative of a fault condition in the health monitoring system or the gas turbine engine; and based on the analysis indicating the presence of a fault condition, providing a fault detection notification. A health monitoring system for a gas turbine engine is also disclosed.

Abstract: A method to effectively utilize an oil debris monitor to provide advanced warning to mechanical system failures in a high noise system by adapting detection and annunciation algorithms to the background noise in a system that includes collecting I and Q channel data from a sensor; processing the I and Q channel data to both calculate a noise based (RMS adjusted) detection threshold and to identify a ferrous and nonferrous signal; processing the ferrous and nonferrous signals to determine signal peaks above the RMS adjusted detection threshold; adjusting a detection threshold if more signal peaks observed than allowable particles in a given time window; transmitting the detection threshold previously determined to particle detection, rate limit adjustment, detectability algorithms, and estimated mass loss accumulation.

Abstract: A method to effectively utilize an oil debris monitor to provide advanced warning to mechanical system failures in a high noise system by adapting detection and annunciation algorithms to the background noise in a system that includes collecting I and Q channel data from a sensor; processing the I and Q channel data to both calculate a noise based (RMS adjusted) detection threshold and to identify a ferrous and nonferrous signal; processing the ferrous and nonferrous signals to determine signal peaks above the RMS adjusted detection threshold; adjusting a detection threshold if more signal peaks observed than allowable particles in a given time window; transmitting the detection threshold previously determined to particle detection, rate limit adjustment, detectability algorithms, and estimated mass loss accumulation.

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 to effectively utilize an oil debris monitor to provide advanced warning to mechanical system failures in a high noise system by adapting detection and annunciation algorithms to the background noise in a system that includes collecting I and Q channel data from a sensor; processing the I and Q channel data to both calculate a noise based (RMS adjusted) detection threshold and to identify a ferrous and nonferrous signal; processing the ferrous and nonferrous signals to determine signal peaks above the RMS adjusted detection threshold; adjusting a detection threshold if more signal peaks observed than allowable particles in a given time window; transmitting the detection threshold previously determined to particle detection, rate limit adjustment, detectability algorithms, and estimated mass loss accumulation.

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.