Honeywell International Inc.

Onder Uluyol

Kyusung Kim

Chris Hickenbottom

Charles M Ball

Dinkar A Mylaraswamy

Sunil K Menon

Raj Mohan Bharadwaj

Girija Parthasarathy

Emmanuel Obiesie Nwadiogbu

Subhabrata Ganguli

Dale Mukavetz

Tomas Rudolecky

Zdenek Hrncir

A system and method for identifying engine wear based on engine oil debris is provided. The provided system and method utilize metallic debris data provided in summary reports. For each object of metallic debris, a zap count, an initial (pre-zap) impedance and an impedance change (post zap) are processed with configurable reference variables. Alerts (suggesting potential engine wear) are generated responsive to the impedance values of the objects of metallic debris and the reference variables.

System and method for evaluating chip zap data

A system and method include receiving a set of sampled measurements for each of multiple sensors, wherein the sampled measurements are at irregular intervals or different rates, re-sampling the sampled measurements of each of the multiple sensors at a higher rate than one of the sensor's set of sampled measurements, and synchronizing the sampled measurements of each of the multiple sensors.

Synchronizing data from irregularly sampled sensors

A system and method are provided for detecting a spall in an engine. The vehicle, for example, may include, but is not limited to an engine, an oil debris sensor coupled to the engine, a communications system, and a processor communicatively coupled to the oil debris sensor and the communications system. The processor may be configured to increment a counter when the oil debris sensor detects a particle over a predetermined size, increment the counter when a mass of a plurality of particles under the predetermined size exceeds a predetermined mass threshold, transmit, via the communications system, a first message when the counter exceeds a predetermined counter threshold, and reset the counter after predetermined amount of time.

System and method for detecting spall initiation and defining end of life in engine components

Methods and systems for estimating wear damage of a rolling-element bearing system including at least one rolling-element bearing are provided. A first number of first condition indicators representative of the wear damage of the at least one rolling-element bearing are generated. A second number of second condition indicators are generated based on the first plurality of first condition indicators. The second number is less than the first number an indication of the wear damage of the at least one rolling-element bearing is generated based on the second number of second condition indicators.

Method and system for detecting incipient bearing failures

Methods and systems for characterizing and estimating the wear damage of a rolling-element bearing are provided. At least one geometric attribute, such as the length, of a damage feature (e.g., a spall cavity) on the rolling-element bearing is calculated based on a geometry of the rolling-element bearing. An accumulated mass of damage particles ejected from the rolling-element bearing during operation of the rolling-element bearing is determined. When the accumulated mass of the damage particles exceeds a predetermined threshold, an indication or a signal is generated. The predetermined threshold of the accumulated mass is based on the at least one geometric attribute of the damage feature. The calculation of the at least one geometric attribute may be based on, for example, a depth of the damage feature, a radius of the rolling elements in the bearing, a radius of an outer race of the bearing as measured from an inner surface thereof, and the number of rolling elements in the bearing.

Method and system for characterizing wear damage on a rolling-element bearing

A testbed for testing health of software includes an input model, a hardware model, and a resource modeler. The input model represents an input system used in conjunction with the software. The hardware model represents one or more hardware components used in conjunction with the software. The resource modeler is coupled to the input model and the hardware model, and is configured to estimate effects on the software of conditions of the hardware components, the input system, or both.

Software health management testbed

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.

Fault detection system and method for turbine engine fuel systems

A substance detection system having an electronic nose and classifier. The system may extract feature from the electronic signals representing the smells from the electronic nose sensing an unknown substance. The features may be formatted as smellprints that are synthesized as data. The features may be classified, and in a binary tournament fashion, as an illustrative example, may be mapped to be compared or correlated with features of known substances. The known substance or substances having the highest scores for good mapping, comparison or correlation with the sensed substance, may be reviewed in view of decision criteria to determine an identification of the sensed substance.

Substance detection system

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.

System and method for turbine engine startup profile characterization

A system and method is provided for detecting anomalies in turbine engines emanating from the main shaft and/or main shaft bearings. The anomaly detection system includes a sensor data processor and a matrix analysis mechanism. The sensor data processor receives engine sensor data, including main engine speed data during spin down, and formats the engine sensor data into an appropriate matrix. The matrix analysis mechanism receives the sensor data matrix and performs a singular value analysis on the sensor data matrix to detect potential anomalies in the turbine engine main shaft and/or bearings. The output of the matrix analysis mechanism is passed to a diagnostic system where further evaluation of the anomaly detection determination can occur.

System and method for turbine engine anomaly detection

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.

System and method for lightoff detection in turbine engines

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.

Transient fault detection system and method

A sensor error compensation system and method is provided that facilitates improved sensor error detection and compensation. The sensor error compensation system includes an expected value generator and a sensor fault detector. The expected value generator receives sensor data from a plurality of sensors in a turbine engine. From the sensor data, the expected value generator generates an expected sensor value for a sensor under test. The expected sensor value is passed to the sensor fault detector. The sensor fault detector compares the expected sensor value to a received sensor value to determine if a sensor error has occurred in the sensor under test. If an error has occurred, the error can be compensated for by generating a replacement sensor value to substitute for erroneous received sensor value.

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Fridley, MN, United States of America

Onder Uluyol