Johnson Controls Tyco IP Holdings Llp

Tyco Fire + Security Gmbh

Carrier Corporation

Jaume Amores

Sugumar Murugesan

Zhongyi Jin

Young Min Lee

Steven R Vitullo

Young M Lee

Kelsey Carle Schuster

Henan Wang

Zhen Jia

Alan Matthew Finn

Kishore K Reddy

Yanzhi Chen

Michael James Risbeck

Chenlu Zhang

Zhanhong Jiang

Santle Camilus Kulandai Samy

Sofiane Yous

Keith J Power

Saman Cyrus

A method includes obtaining a fault prediction model for building equipment, predicting, with the fault prediction model, both (i) whether a fault will occur during a first prediction bin and (ii) whether a fault will occur during a second prediction bin, performing a first mitigating action for the building equipment if the fault is predicted to occur during the first prediction bin, and performing a second mitigating action for the building equipment if the fault is predicted to occur during the second prediction bin.

Building equipment control system with modular models

A method of generating a fault determination in a building management system (BMS), the method including receiving signal data, generating, using a number of fault detection models, a number of fault indications based on the signal data, generating, using a weighting function, based on the number of fault indications, a fault score, comparing the fault score to a fault value, and determining, based on the comparison, an existence of a fault.

Robust fault detection and diagnosis by constructing an ensemble of detectors

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 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 method for searching video includes receiving a video query, the video query including a first video query term identifying a first foreground object, a second video query term identifying a second foreground object, and a third video query term identifying a spatiotemporal relationship; searching the video in response to the video query for the first foreground object and the second foreground object within the spatiotemporal relationship; and generating a search result in response to the searching.

Growing community of inventors

Cork, Ireland

Jaume Amores