Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings LLP

Tyco Fire + Security Gmbh

Kelsey Carle Schuster

Youngchoon Park

Sudhi R Sinha

Vaidhyanathan Venkiteswaran

Erik S Paulson

Steven R Vitullo

Vijaya S Chennupati

Christopher J Verink

John F Kuchler

Riley P Shelton

Michael Richard Rhodes

Young Min Lee

Justin J Ploegert

Sajjad Pourmohammad

Ada L Ma

Sugumar Murugesan

Zhongyi Jin

Jaume Amores

Elyse R Hobson

Henan Wang

Jonathan Cox

Michael R Rhodes

Sarah Just

Matthias Walczyk

Andrew S Pike

John M Sullivan

A method includes obtaining an actual value of a condition affected by operating the building equipment, operating the building equipment using a setpoint for the condition, calculating a stability index based on a timeseries of errors between the actual value and the setpoint, comparing the stability index to a criterion, in response to the stability index satisfying the criterion, executing an action relating to the building equipment, and in response to the stability index not satisfying the criterion, not executing the action.

Performance monitoring and control system for connected building equipment with stability index

A system includes equipment operable to affect, monitor, or control one or more variable states or conditions in a building. The system further includes circuitry configured to perform a plurality of performance checks for the equipment, determine a plurality of individual performance check indicators based on the plurality of performance checks using a plurality of first weights determined based on different timings, generate an overall performance index for the equipment using the plurality of individual performance check indicators and a plurality of second weights, and initiate or execute a preventative maintenance action for the equipment based on the overall performance index.

Time varying performance indication system for connected equipment

A building management system includes one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive a publication by an agent on an agent communication channel, the publication comprising timeseries data, identify, based on the publication, an object entity of an entity database associated with the agent, wherein the entity database includes one or more object entities and relationships between the one or more object entities and one or more data entities, identify a data entity related to the object entity based on a relationship of the relationships relating the object entity and the data entity, and ingest the timeseries data into the data entity.

Building system with digital twin based agent processing

A system for a building management system simulation includes one or more processors and memory. The memory includes instructions stored thereon, that when executed by the one or more processors, cause the one or more processors to generate a space agent representing a space in a building, the space agent to maintain an environmental condition of the space based on an optimization state of the space, generate an equipment agent representing a device that serves the space, and register the space agent and the equipment agent to a space communication channel associated with the space. The space agent communicates with the equipment agent over the space communication channel.

Systems and methods for digital twin-based equipment control

A system for predicting performance of building equipment includes one or more processors and memory storing instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations. The operations include obtaining sensor data generated by one or more sensors while operating the building equipment to affect one or more environmental conditions of a building, generating a predicted performance of the building equipment using a machine learning model if the machine learning model comprises prior data similar to the sensor data, and requesting feedback from an expert related to the predicted performance of the building equipment if the sensor data are different from the prior data.

Analysis system with machine learning based interpretation

A system includes a plurality of vibration sensors configured to be coupled to a unit of building equipment, an edge device arranged between the plurality of vibration sensors and a communications network. The edge device is programmed to detect an abnormality in vibration data from the plurality of vibration sensors by ingesting streams of vibration data from the plurality of vibration sensors, performing signal processing on the streams of vibration data to obtain inputs for a machine learning model, determining whether the abnormality is occurring by applying the inputs to the machine learning model, determining a trend associated with the abnormality based on outputs of the machine learning model, and causing a portion of the vibration data to be communicated to a first computing system via the communications network in response to the trend satisfying a criterion.

Edge devices and gateways with machine learning for detecting anomalies in building equipment vibration data

Building smart entity system with agent based data ingestion and entity creation using time series data

A building management system includes building equipment operable to affect a variable state or condition of a building and a controller including a processing circuit. The processing circuit is configured to obtain a vibration data set related to vibrations of the building equipment and analyze the vibration data set by one or more machine learning models to generate a set of probabilities. The set of probabilities is related to a probability that the vibration data set is abnormal. The processing circuit is configured to identify the vibration data set as normal or abnormal based on the set of probabilities and initiate a corrective action responsive to identifying the vibration data set as abnormal.

Building management system with machine learning for detecting anomalies in vibration data sets

Systems and methods for agent based building simulation for optimal control

A building management system of a building includes one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to generate agents, each agent of the agents paired with one entity of a plurality of entities of an entity database, wherein the entity database includes relationships between the entities, wherein the entities represent physical building entities of the building comprising building equipment or building spaces. The instructions cause the one or more processors to communicate, by the plurality of agents, data of the physical building entities via a plurality of agent communication channels and perform, by the plurality of agents, one or more operations for the plurality of entities based on the data.

Building smart entity system with agent based communication and control

One or more non-transitory computer-readable storage media having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to detect an individual who enters into a lobby of a building, retrieve context information corresponding to the individual, update a user profile of the individual to indicate the individual has arrived at the building, and control one or more access control devices to grant the individual access to the building based on the context information.

Lobby management system

A fault prediction system for building equipment includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to receive device data for a plurality of devices of the building equipment, the device data indicating performance of the plurality of devices; generate, based on the received device data, a plurality of prediction models comprising at least one of single device prediction models generated for each of the plurality of devices or cluster prediction models generated for device clusters of the plurality of devices; label each of the plurality of prediction models as an accurately predicting model or an inaccurately predicting model based on a performance of each of the plurality of prediction models; and predict a device fault with each of the plurality of prediction models labeled as an accurately predicting model.

Adaptive training and deployment of single device and clustered device fault detection models for connected equipment

Systems and methods of generating performance index for connected equipment in a building management system are provided. The connected equipment measures monitored variables and generates status codes. The system obtains data points of the monitored variables and the status codes from past N time units, and connected equipment specific parameters. The system performs first performance checks for the connected equipment using the status codes from the past N time units. The system performs second performance checks using the data points of the monitored variables from the past N time units, the connected equipment specific parameters, and a set of predetermined rules. The system determines individual performance check indicators based on the first performance checks and the second performance checks using first weights each determined based on a different timing. The system generates an overall performance index for the connected equipment using the individual performance check indicators and second weights.

One embodiment of the present disclosure is a system for predicting performance of building equipment. The system comprises one or more sensors in communication with the building equipment, and the sensors are operable to detect characteristics from the building equipment. The system further comprises a computing device in communication with the sensors and in the same geographic location as the sensors. The computing device comprises one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to receive data from the sensors, the data based on the detected characteristics. The one or more processors also generate, based on a machine learning model and the data, a predicted performance of the building equipment when the machine learning model comprises a prior data substantially similar to the data.

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Wauwatosa, WI, United States of America

Kelsey Carle Schuster