公开(公告)号：US20240185122A1

公开(公告)日：2024-06-06

申请号：US18116974

申请日：2023-03-03

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Young M. Lee ,  Wenwen Zhao ,  Brian E. Keenan ,  Santle Camilus Kulandai Samy ,  Michael J. Risbeck ,  Zhanhong Jiang ,  Chenlu Zhang ,  Saman Cyrus

IPC: G06N20/00 ,  G05B19/042 ,  G06N7/01

CPC classification number: G06N20/00 ,  G05B19/042 ,  G06N7/01 ,  G05B2219/2614

Abstract: A method for training a fault probability model using warranty claim data includes obtaining, by a processing circuit, a first data set for failed building devices based on warranty claim data associated with the building devices; receiving, by the processing circuit, design change data associated with the building devices and determining a design change date based on the design change data; comparing, by the processing circuit, a manufacturing date for each of the failed building devices with the design change date; removing, by the processing circuit, any building devices from the first data set in response to the manufacturing date preceding the design change date to create an updated first data set; generating, by the processing circuit, a training data set comprising the updated first data set; and training, by the processing circuit, a fault probability model using the training data set to produce a trained model.

公开(公告)号：US20240346036A1

公开(公告)日：2024-10-17

申请号：US18419449

申请日：2024-01-22

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Julie J. Brown ,  Young M. Lee ,  Rajiv Ramanasankaran ,  Sastry KM Malladi ,  Michael Tenbrock ,  Levent Tinaz ,  Samuel A. Girard ,  David S. Elario ,  Juliet A. Pagliaro Herman ,  Miguel Galvez ,  Trent M. Swanson ,  John F. Kuchler ,  Deepak Budhiraja ,  Daniela M. Natali ,  Josip Lazarevski ,  Scott Deering ,  Gary W. Gavin ,  Kristen Sheppard-Guzelaydin ,  James Young ,  Prashanthi Sudhakar ,  Kaleb Luedtke ,  Karl F. Reichenberger ,  Wenwen Zhao ,  Adam R. Grabowski ,  Lauren C. Dern ,  Nicole A. Madison ,  Dana S. Petersen ,  Nevin L. Forry ,  Pedriant Pena ,  Ghassan R. Hamoudeh ,  Ryan G. Danielson

IPC: G06F16/25 ,  G06F11/07 ,  G06F16/332

CPC classification number: G06F16/258 ,  G06F11/079 ,  G06F16/3329

Abstract: A method including training, by one or more processors, a generative AI model using a plurality of first unstructured service reports corresponding to a plurality of first service requests handled by technicians for servicing building equipment. The plurality of first unstructured service reports include unstructured data not conforming to a predetermined format or conforming to a plurality of different predetermined formats. The method includes receiving, by the processors, a second service request for servicing building equipment. The method includes generating, by the processors using the generative AI model, a user interface prompting a user to provide information about a problem leading to the second service request as unstructured data not conforming to the predetermined format or conforming to the plurality of different predetermined formats. The method includes automatically initiating, by the processors, one or more actions to address the problem based on the information provided via the user interface.

公开(公告)号：US20230153490A1

公开(公告)日：2023-05-18

申请号：US17530257

申请日：2021-11-18

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Young M. Lee ,  Wenwen Zhao ,  Jaume Amores Llopis

IPC: G06F30/27

CPC classification number: G06F30/27

Abstract: A method for generating a reliability model, comprising receiving, by a processing circuit, historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, calculating, by the processing circuit, a runtime of a chiller of the one or more chillers based on the two or more event dates, calibrating, by the processing circuit, the runtime by determining an idle time associated with the chiller corresponding to a location of the chiller and performing an operation using the runtime and the idle time to generate a calibrated runtime, and training, by the processing circuit, a chiller reliability model using the calibrated runtime to produce a trained model.

公开(公告)号：US20240345554A1

公开(公告)日：2024-10-17

申请号：US18419456

申请日：2024-01-22

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Julie J. Brown ,  Young M. Lee ,  Rajiv Ramanasankaran ,  Sastry KM Malladi ,  Michael Tenbrock ,  Levent Tinaz ,  Samuel A. Girard ,  David S. Elario ,  Juliet A. Pagliaro Herman ,  Miguel Galvez ,  Trent M. Swanson ,  John F. Kuchler ,  Deepak Budhiraja ,  Daniela M. Natali ,  Josip Lazarevski ,  Scott Deering ,  Gary W. Gavin ,  Kristen Sheppard-Guzelaydin ,  James Young ,  Prashanthi Sudhakar ,  Kaleb Luedtke ,  Karl F. Reichenberger ,  Wenwen Zhao ,  Adam R. Grabowski ,  Lauren C. Dern ,  Nicole A. Madison ,  Dana S. Petersen ,  Nevin L. Forry ,  Pedriant Pena ,  Ghassan R. Hamoudeh ,  Ryan G. Danielson

IPC: G05B15/02

CPC classification number: G05B15/02

Abstract: A method including training, by one or more processors, a generative AI model using first operating data from building equipment and a plurality of first service reports indicating a plurality of first problems associated with the building equipment. The method may include predicting, by the one or more processors using the generative AI model, one or more future problems likely to occur with the building equipment based on second operating data from the building equipment. The method may include automatically initiating, by the one or more processors, one or more actions to prevent the one or more future problems from occurring or mitigate an effect of the one or more future problems.

公开(公告)号：US20240185257A1

公开(公告)日：2024-06-06

申请号：US17971342

申请日：2022-10-21

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Young M. Lee ,  Wenwen Zhao

IPC: G06Q30/00 ,  G06F40/279

CPC classification number: G06Q30/012 ,  G06F40/279

Abstract: A method for generating a training data set includes receiving, by a processing circuit, warranty claim data associated with one or more building devices or building device components; processing, by the processing circuit, the warranty claim data using natural language processing to generate a training data set comprising one or more causes and solutions associated with failure of the one or more building devices or the building device components; and training, by the processing circuit, a component reliability model using the training data set to produce a trained model.

公开(公告)号：US20230418281A1

公开(公告)日：2023-12-28

申请号：US18214945

申请日：2023-06-27

Applicant: Johnson Controls Tyco IP Holdings LLP

Inventor： Young M. Lee ,  Wenwen Zhao ,  Arunkumar Vedhathiri ,  Aditya Varma Penmetsa ,  Yulizar Rachmat

IPC: G05B23/02 ,  G06Q10/0631 ,  G06Q10/0635

CPC classification number: G05B23/0283 ,  G06Q10/0635 ,  G06Q10/06315 ,  G05B23/0251

Abstract: A method for affecting operation of building equipment includes providing a plurality of reliability models that model failure probabilities of components of the building equipment as functions of equipment runtime, providing associations of the components with a plurality of subsystems of the building equipment, calculating, for the plurality of subsystems of the building equipment, probabilities of subsystem failure based on the reliability models for the components and the associations, and initiating an automated action to affect operation of the building equipment based on the probabilities of subsystem failure.