Tyco Fire & Security Gmbh

Johnson Controls Tyco IP Holdings Llp

Thomas M Seneczko

Kirk H Drees

Michael J Wenzel

Mohammad N ElBsat

Miguel Galvez

James Byron Burke

Timothy C Gamroth

David S Eidson

Liming Yang

Trent Mark Swanson

Abu Bakr Khan

Gary W Gavin

Gregory A Makowski

Sudhi R Sinha

Robert D Turney

Young Min Lee

Matthew J Ellis

Vaidhyanathan Venkiteswaran

Karl F Reichenberger

Walter Andrew Martin

Curtis Christian Crane

Ryan A Piaskowski

Justin Patrick Kauffman

Clay G Nesler

Shawn D Schubert

Jonathan D Douglas

Andrew J Boettcher

Jason T Sawyer

Donald R Albinger

Daniel M Curtis

Sastry K M Malladi

Sastry Km Malladi

Serdar Suindykov

Siddharth Goyal

Mark L Ziolkowski

Matthew J Deloge

Joseph F Mann

Ganesh Kalpundi

Renee Clair

Anza D'Antonio

Justin P Kauffman

A method of operating a chiller to avoid future surge events, the method comprises applying chiller operating data associated with a chiller as an input to one or more machine learning models; and generating a threshold for a controllable chiller variable to prevent a future chiller surge event from occurring based on an output of the one or more machine learning models, further comprising affecting operation of the chiller based on the threshold to prevent the future chiller surge event from occurring. The method enables automatic control of a chiller to avoid future chiller surge events.

Building system with automatic chiller anti-surge control

Systems and methods for reducing health risks with respect to an infectious disease in buildings. Health data for an infectious diseases is used to determine a health risk level for building spaces and individuals in the building. An air handling action or a disinfection action is performed based on the health risk level.

Air quality control and disinfection system

Systems and methods to integrate equipment models are described herein. Integration of equipment models can occur responsive to detection of new physical devices.

Automatic generation of data models

Systems and methods to generate new equipment models and/or connectors to integrate the new equipment models are described herein. Generation of new equipment models can include retrieving information from one or more publicly accessible data sources.

A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Building system with a time correlated reliability data stream

A building management system (BMS) for filtering a fluid within a building is shown. The system includes one or more sensors configured to measure one or more characteristics of a first fluid within an air duct of the BMS and measure one or more characteristics of a second fluid after the second fluid has been filtered. The system further includes a pollutant management system configured to receive data from the one or more sensors and control a filtration process. The filtration process selects a filter of a plurality of filters based on at least one of a level of the one or more characteristics of the first fluid or the one or more characteristics of the second fluid.

Systems and methods for optimizing ventilation, filtration, and conditioning schemes for buildings

A method of operating a chiller includes applying chiller operating data associated with the chiller as an input to one or more machine learning models, generating a boundary for a controllable chiller variable based on an output of the one or more machine learning models, and affecting operation of the chiller based on the boundary.

A building management system (BMS) for filtering a fluid within a building is shown. The system includes one or more sensors configured to measure one or more characteristics of a first fluid within an air duct of the BMS and measure one or more characteristics of a second fluid after the second fluid has been filtered. The system further includes a pollutant management system configured to receive data from the one or more sensors and control a filtration process. The filtration process selects a filter of a plurality of filters based on a level of the one or more characteristics of the first fluid and the one or more characteristics of the second fluid.

Growing community of inventors

Milwaukee, WI, United States of America

Thomas M Seneczko