Honeywell International Inc.

Harold C Voges

Dinkar A Mylaraswamy

Sunil K Menon

George Daniel Hadden

Valerie Guralnik

Emmanuel Obiesie Nwadiogbu

Kevin M Kramer

Emmanuel Obisie Nwadiogbu

Steven C Gaetjens

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 for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.

System and method for combining diagnostic evidences for turbine engine fault detection

A trainable, extensible, automated data-to-knowledge translator is described. One aspects of the present invention includes a computerized system having at least one repository to store user-specified rules that govern the processing of data by the computerized system and at least one processing module to process data according to the rules and to generate knowledge from the data. Another aspect of the present invention is a computerized method of translating data to knowledge. The computerized method includes providing user-specified rules to govern the behavior of a computerized system for translating data to knowledge, and processing data according to the rules to generate knowledge. A further aspect of the present invention is a computer readable medium having computer-executable instructions stored thereon for executing a method of translating data to knowledge. The computerized method comprises receiving data in an unstructured form, converting the data to a neutral form, processing data according to user-specified rules to translate the data from the neutral form to knowledge, and exporting the knowledge to a knowledge repository.

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Shoreview, MN, United States of America

Harold C Voges