Honeywell International Inc.

Sunil K Menon

Dinkar A Mylaraswamy

Emmanuel Obiesie Nwadiogbu

George Daniel Hadden

Joydeb Mukherjee

Venkataramana Bantwal Kini

Onder Uluyol

Girija Parthasarathy

Russell D Braunling

Dale Mukavetz

Harold C Voges

Lewis P Olson

Kyusung Kim

David G Elpern

David B Goldstein

Gary Scott Whittaker

Darryl Busch

Dane Francis Wilson

Paul Frederick Dietrich

Dennice F Gayme

Ravindra Patankar

Charles M Ball

Jon E Schryver

Alex Morando

Brian G Hosler

Lawrence A Lem

Emmanuel Obisie Nwadiogbu

Environmental control systems and techniques for monitoring heat exchanger fouling are provided herein. In an embodiment, a technique for monitoring heat exchanger fouling includes providing a correlation of heat exchanger fouling to an operating variable. The correlation is programmed into a maintenance system that includes a processor that is adapted to receive data regarding the operating variable. The operating variable is chosen from inlet temperatures of input streams into the heat exchanger and outlet temperatures of output streams out of the heat exchanger, heat exchanger pressure drop, overall heat transfer coefficient value, speed of rotation of an air conditioning machine that includes the heat exchanger, and combinations thereof. Data regarding the operating variable is gathered via a sensor that is adapted to measure the operating variable. The measured operating variable is determined to correspond to excessive heat exchanger fouling in accordance with the correlation using the processor.

Environmental control systems and techniques for monitoring heat exchanger fouling

An improved fault detection system and method is provided. The fault detection system and method combines the use of discrimination and representation based feature extraction to reliably detect symptoms of faults in turbine engines. Specifically, the fault detection system and method uses a kernel-based Maximum Representation Discrimination Features (MRDF) technique to detect symptoms of fault in turbine engines. The kernel-based MRDF system and method combines the use of discriminatory features and representation features in historical sensor data to facilitate feature extraction and classification of new sensor data as indicative fault in the turbine engine. Furthermore, the kernel-based MRDF technique facilitates the uncovering of nonlinear features in the sensor data, thus improving the reliability of the fault detection.

Kernel-based fault detection system and method

Systems, methods and devices are provided that allow more efficient transfer and processing of sensor information in a hierarchical data system. The system provides for a plurality of component area managers (CAM), each of the CAMS being in operable communication with at least one of a plurality of transducers that monitors a phenomena of a component and in operable communication with a data bus. A CAM comprises a processor in operable communication with the at least one transducer of the plurality of transducers, wherein the first processor is configured to record data generated by the at least one transducer of the plurality of transducers, to reduce the recorded data, to place the reduced data on the data bus. The system also includes a transducer selection module controlled by the first processor by which the first processor selects one of the plurality of transducers to record and a rolling buffer in operable communication with the first processor and in operable communication with the at least one transducer by which to record the data generated by the at least one transducer of the plurality in a first-in-first-out manner.

Vehicle system monitoring and communications architecture

A health monitoring system for a vehicle system includes a plurality of managers and a decision support module. Each manager corresponds to a different sub-system of the vehicle system, and generates manager output pertaining to the sub-system based at least in part on an intermediate output. Each manager comprises a plurality of reasoners. Each reasoner corresponds to a different component of the sub-system, and comprises a plurality of modules and a reasoner fusion block. Each module obtains data regarding a different aspect of the component and generates a preliminary output based on the data. The reasoner fusion block is coupled to the plurality of modules, receives the preliminary output, and generates the intermediate output based on the preliminary output. The decision support module is coupled to the plurality of managers, receives the manager output therefrom, and provides a decision support output based on the manager output.

Vehicle health monitoring reasoner architecture for diagnostics and prognostics

A health monitoring system for a vehicle system includes an operational support system including a plurality of managers and a decision support module. Each manager corresponds to a different sub-system of the vehicle system, and comprises a plurality of reasoners and a fusion block. Each reasoner is configured to obtain data and provide preliminary output regarding a different component of the sub-system based on the data. The fusion block is coupled to the plurality of reasoners, and is configured to receive the preliminary output and generating manager output based on the preliminary output. The decision support module is coupled to the plurality of managers, and is configured to receive the manager output and provide a decision support output based on the manager output.

Vehicle health monitoring system architecture for diagnostics and prognostics disclosure

A system and method that provides improved fault detection in turbine engines is disclosed. The fault detection system provides the ability to detect symptoms of engine faults based on a relatively limited number of engine parameters that are sampled relatively infrequently. The fault detection system includes a sensor data processor that receives engine sensor data during operation and augments the sensor data. The augmented data set is passed to a fuzzy logic inference system that determines the likelihood that a fault has occurred. The inference system output can then be passed to a diagnostic system where evaluation of the output may yield a detailed diagnostic result and a prediction horizon.

Fault detection system and method using augmented data and fuzzy logic

Methods and apparatus are provided for determining refrigerant charge in a vapor compressor system (VCS) of an aircraft. The methods and apparatus comprise the following steps of, and/or means for, generating a data set from historical data representative of a plurality of VCS operating conditions over time, identifying one or more steady-state data points in the generated data set, forming a revised data set that includes at least the steady-state data points, using principal components analysis (PCA) to derive values for a plurality of minimally correlated input variables, supplying the derived values for the plurality of minimally correlated input variables and the corresponding values for the VCS refrigerant charge in the revised data set to a nonlinear neural network model, and deriving a simulator model characterizing a relationship between the plurality of minimally correlated input variables and the VCS refrigerant charge.

Neural network based refrigerant charge detection algorithm for vapor compression systems

A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships. The fault detection system uses a neural network to perform a data representation and feature extraction where the extracted features are analogous to principal components derived in a principal component analysis. This neural network data representation analysis can then be used to determine the likelihood of a fault in the system.

Nonlinear neural network fault detection system and method

Methods and apparatus are provided for predicting ice formation on and shedding from blades of an air cycle machine ('ACM') turbine. The method comprises generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, determining an amount of ice formation on the blades of the ACM turbine, and determining an amount of ice shed from the blades of the ACM turbine. The apparatus comprises means for generating operational condition data representative of ACM turbine operating conditions using a software model of the ACM, means for determining an amount of ice formation on the blades of the ACM turbine, and means for determining an amount of ice shed from the blades of the ACM turbine. In some embodiments, the means and apparatus may also involve predicting ACM wear, based at least in part on the predicted ice formation and shedding.

Method for predicting air cycle machine turbine ice formation and shedding and journal bearing wear

Methods and apparatus are provided pertaining to a design of experiments. The method comprises generating a data set from historical data; identifying and removing any fault data points in the data set so as to create a revised data set; supplying the data points from the revised data set into a nonlinear neural network model; and deriving a simulator model characterizing a relationship between the input variables and the output variables. The apparatus comprises means for generating a data set from historical data; means for identifying and removing any fault data points in the data set so as to create a revised data set; means for supplying the data points from the revised data set into a nonlinear neural network model; and means for deriving a simulator model characterizing a relationship between the input variables and the output variables.

Empirical design of experiments using neural network models

A system and method for fault detection is provided. The fault detection system provides the ability to detect symptoms of fault in turbine engines and other mechanical systems that have nonlinear relationships between two or more variables. The fault detection system uses a neural network to perform feature extraction from data for representation of faulty or normal conditions. The values of extracted features, referred to herein as scores, are then used to determine the likelihood of fault in the system. Specifically, the lower order scores, referred to herein as 'approximate null space' scores can be classified into one or more clusters, where some clusters represent types of faults in the turbine engine. Classification based on the approximate null space scores provides the ability to classify faulty or nominal conditions that could not be reliably classified using higher order scores.

Fault detection system and method using approximate null space base fault signature classification

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 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 engine faults that occur in transient conditions. The transient fault detection system includes a Hidden Markov Model detector that receives sensor data during transient conditions and determines 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 using Hidden Markov Models

A system for measuring pitting corrosion in metal using a digital camera having a lens and an output unit for identifying the color of a metal coupon on a gray scale and providing an output in the midrange of the scale when non corroded and different output after exposure to a corrosive environment. A computer is connected to the output of the camera to display the photographs to permit observation of the gray scale output based upon a minimum and maximum threshold inputs which are adjustable based on an observation of pitting. The points of interest on the coupon representing observed pitting are recorded and summed to calculate an estimated corrosion area corresponding to pitting the said coupon as a percentage of the total coupon area.

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Golden Valley, MN, United States of America

Sunil K Menon