Johnson Controls Tyco IP Holdings Llp

Tyco Fire + Security Gmbh

Johnson Controls Technology Company

Zhongyi Jin

Young Min Lee

Youngchoon Park

Sugumar Murugesan

Jaume Amores

Erik S Paulson

Ambuj Shatdal

Chenlu Zhang

Kelsey Carle Schuster

Steven R Vitullo

Young M Lee

Surajit Borah

Vish Ramamurti

Henan Wang

Ryan A Piaskowski

Santle Camilus

Clifford H Copass

Simin Zhou

A method for visualizing a building model comprises retrieving an identifier associated with a digital representation of a building asset from a digital representation representing at least a portion of a building, retrieving, using the identifier, a graphical representation of the asset from a building model having a different format than the digital representation, and rendering the graphical representation on a display device.

Building management system with dynamic building model enhanced by digital twins

A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.

Building system with string mapping based on a sequence to sequence neural network

A method for generating a graph data structure comprises receiving, by one or more processors, data associated with a building, generating one or more space nodes in the graph data structure corresponding to spaces within the building based on the data, generating one or more asset nodes in the graph data structure corresponding to assets within the building based on the data, associating sensor data with the one or more asset nodes based on the data, classifying the sensor data based on the data, and generating a relationship between at least two of the one or more space nodes, the one or more asset nodes, and the classified sensor data.

Self-configuring building management system

A method for commissioning a building comprises identifying a building asset within a digital representation of a building having one or more parameters describing relationships of the building asset within the building, controlling one or more other building assets included within the digital representation to change an environmental parameter of the building, monitoring a response of at least one of the building asset or one of the one or more other building assets to the change in the environmental parameter, generating a parameter for the digital representation associated with the building asset based on the monitoring, and updating the digital representation using the parameter.

Building management system with auto-configuration using existing points

A model management 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 determine whether fault data exists in equipment data used to generate a plurality of shutdown prediction models for the building equipment, generate a first performance evaluation value for each of the plurality of shutdown prediction models using a first evaluation technique in response to a determination that the fault data exists in the equipment data, generate a second performance evaluation value for each of the plurality of shutdown prediction models using a second evaluation technique in response to a determination that the fault data does not exist in the equipment data, and select one of the plurality of shutdown prediction models based on the first performance evaluation value and the second performance evaluation value.

Adaptive selection of machine learning/deep learning model with optimal hyper-parameters for anomaly detection of connected equipment

A building system of a building, the building system comprising one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive building metadata, the building metadata describing a plurality of components of the building, generate, based on the building metadata, a plurality of entities, each of the plurality of entities representing one of the plurality of components, and determine, based on the building metadata, relationships between the plurality of entities. The instructions cause the one or more processors to generate a plurality of metadata strings in a universal building schema comprising a plurality of characters representing a first entity of the plurality of entities, one or more second entities of the plurality of entities related to the first entity, and one or more relationships between the first entity and the one or more second entities.

Building system with naming schema encoding entity type and entity relationships

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

A chiller threshold management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller prediction models. The one or more processors are further configured to generate a first threshold evaluation value for each of the plurality of chiller prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second threshold evaluation value for each of the chiller prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select a first threshold for each of the plurality of chiller prediction models based on the first threshold evaluation values in response to the determination that chiller fault data exists in the chiller data, and select a second threshold for each of the plurality of chiller prediction models based on the second threshold evaluation values in response to the determination that chiller fault data does not exist in the chiller data.

Automatic threshold selection of machine learning/deep learning model for anomaly detection of connected chillers

A model management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller shutdown prediction models. The one or more processors are further configured to generate a first performance evaluation value for each of the plurality of chiller shutdown prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second performance evaluation value for each of the plurality of chiller shutdown prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select one of the plurality of chiller shutdown prediction models based on the first performance evaluation in response to the determination that chiller fault data exists in the chiller data, and select one of the plurality of chiller shutdown prediction models based on the second performance evaluation in response to the determination that chiller fault data does not exist in the chiller data.

Adaptive selection of machine learning/deep learning model with optimal hyper-parameters for anomaly detection of connected chillers

A chiller fault prediction system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers. The one or more processors are configured to label each of the plurality of single chiller prediction models and the plurality of cluster chiller prediction models as an accurately predicting chiller model or an inaccurately predicting chiller model based on a performance of each of the plurality of single chiller prediction models and a performance of each of the plurality of cluster chiller prediction models. The one or more processors are configured to predict a chiller fault with each of the plurality of single chiller prediction models labeled as the accurately predicting chiller models. The one or more processors are configured to predict a chiller fault for each of a plurality of assigned chillers assigned to one of a plurality of clusters labeled as the accurately predicting chiller model.

Adaptive training and deployment of single chiller and clustered chiller fault detection models for connected chillers

A building security system for a building includes one or more memory devices configured to store instructions. The instructions, when executed on one or more processors, cause the one or more processors to receive an access policy data structure for a building device, the access policy data structure indicating access policies for interactions of one or more other building devices with the building device, wherein the access policy data structure identifies the one or more other building devices with one or more building model queries, generate a dynamic access policy data structure for the building device by resolving the one or more building model queries with a building model to identify the one or more other building devices, wherein the dynamic access policy data structure comprises the access policies, and implement the access policies of the dynamic access policy data structure based on the one or more other building devices.

Building system with dynamic manufacturer usage description (MUD) files based on building model queries

A method for classifying an occluded object includes receiving, by one or more processing circuits, an image of the object that is partially occluded by a foreign object and classifying, by the one or more processing circuits, the object of the image into one of one or more classes of interest via an artificial neural network (ANN) by determining a plurality of neuron activations of neurons of the ANN for one or more foreign classes and the one or more classes of interest, subtracting one or more of the neuron activations of the one or more foreign classes from the neuron activations of the one or more classes of interest, wherein the foreign object belongs to one of the one or more foreign classes, and classifying the object of the image into the one of the one or more classes of interest based on the subtracting.

Growing community of inventors

Santa Clara, CA, United States of America

Zhongyi Jin