United Technologies Corporation

Simmonds Precision Products, Inc.

Yiqing Lin

Stella M Oggianu

Meggan Harris

Michael J Giering

Gregory S Hagen

Ozgur Erdinc

Alexander I Khibnik

Robert K Thornton

Daniel Hiatt

Kaiyu Wang

Moon C Kim

Ramona A Georgescu

A microgrid according to an exemplary aspect of the present disclosure includes, among other things, a plurality of intelligent electronic devices configured to communicate directly with one another in a first language. Each of the intelligent electronic devices includes a controller and a gateway. The gateway is configured to convert incoming messages from the first language to a second language native to the controller. The first language is different than the second language. A method is also disclosed.

Intra-microgrid communication architecture

A microgrid controller includes a database in communication with a processor. The processor is operable to receive at least one microgrid input, to determine a first plurality of optimal power characteristic levels at a corresponding one of a plurality of first time intervals for a first time period, and to determine a second plurality of optimal power characteristic levels of a device determined at a corresponding one of a plurality of second time intervals for a second time period. The first time intervals are found at a first frequency different than a second frequency of the second time intervals. One of the second plurality of optimal power characteristic level corresponds to one of the first plurality of optimal power characteristic levels at each first time interval. The processor is configured to control a device optimal power characteristic level in response to the second plurality of optimal power characteristic levels.

Microgrid system and controller

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.

Oil debris monitoring (ODM) with adaptive learning

A method of adjusting a rotary blade includes receiving performance data for a rotary blade, receiving an adjustment constraint for the rotary blade, defining an adjustment space for alternative adjustment solutions for the rotary blade including a plurality of alternative adjustment solutions, and calculating expected performance for one of the plurality of alternative adjustment solutions. Based on the expected performance of the one of the plurality of alternative adjustment solutions, the method determines whether to calculate expected performance for another of the plurality of alternative adjustment solutions.

Systems and methods for determining rotary blade track and balance adjustments

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Glastonbury, CT, United States of America

Yiqing Lin