Johnson Controls Technology Company

Johnson Controls Tyco IP Holdings Llp

Tyco Fire & Security Gmbh

Steven R Vitullo

Kelsey Carle Schuster

Andrew J Boettcher

Young Min Lee

Sugumar Murugesan

Youngchoon Park

John F Kuchler

Riley P Shelton

Michael J Wenzel

Vijaya S Chennupati

Gerald A Asp

Peter A Craig

Jaume Amores

Vivek Narain

Michael Richard Rhodes

Kirk H Drees

Matthew J Asmus

Graeme Willmott

Zhongyi Jin

Jaume Amores Llopis

Michael R Rhodes

Henan Wang

Jonathan Cox

Sudhi R Sinha

Karl F Reichenberger

Homero L Noboa

Young M Lee

Ada L Ma

Anas W I Alanqar

Ryan C Beaty

Shawn A Schlagenhaft

Ryan A Baumgartner

Steven C Snyder

A system includes processors configured to perform operations including obtaining a base design resource production of a building asset and a base resource production data set comprising a base resource production of the building asset at a plurality of operating points, calculating a scaled resource production data set comprising a scaled resource production of the building asset at the plurality of operating points by scaling the base resource production data set based on a new design resource production of the building asset relative to the base design resource production of the building asset, generating a resource consumption data set comprising a resource consumption of the building asset at the plurality of operating points based on the scaled resource production data set, and initiating an automated action based on the scaled resource production data set and the resource consumption data set.

Central plant optimization system with scalable asset models

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 method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.

Control system for building equipment with dynamic capacity constraints

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 building energy management system includes building equipment, a data collector, an analytics service, a timeseries database, and an energy management application. The building equipment monitor and control one or more variables in the building energy management system and provide data samples of the one or more variables. The data collector collects the data samples from the building equipment and generates a data timeseries including a plurality of the data samples. The analytics service performs one or more analytics using the data timeseries and generates a results timeseries including a plurality of result samples indicating results of the analytics. The timeseries database stores the data timeseries and the results timeseries. The energy management application retrieves the data timeseries and the results timeseries from the timeseries database in response to a request for timeseries data associated with the one or more variables.

Building energy management system with energy analytics

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 building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause the one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a first plurality of times within a first time period; analyze cumulative sum values associated with a second plurality of times occurring before the first time to identify a second time of the second plurality of times at which a second cumulative sum value is at a local minimum; and determine that a first fault began at the second time.

Building system with early fault detection

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.

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 determine an average of a minimum half of sorted energy consumption values for a first time period and determine an average of a maximum half of sorted energy consumption values for a second time period. The instructions also cause the processor to determine a ratio of the average of the minimum half of sorted energy consumption values for the first time period to the average of the maximum half of sorted energy consumption values for the second time period, compare the calculated ratio to an adaptively tunable threshold value and activate a system responsive to the calculated ratio exceeding the adaptively tunable threshold value.

Systems and methods for adaptively tuning thresholds for fault detection in buildings

A method for determining a stability of a building device includes receiving time series data from building equipment regarding a first operational parameter of the building equipment, determining, for one or more time steps of the time series data, a deviation between a setpoint and a recorded value of the first operational parameter, calculating, for the one or more time steps, a trapezoidal area based on an absolute value of the deviation and a length of a time step, calculating a stability index value for the building equipment by summing the trapezoidal areas for a subset of the one or more time steps over a time horizon, and initiating an automated response based on the stability index value.

Stability index for connected equipment

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

The present disclosure is directed to a method for performing energy analytics in a building management system. The method can include collecting respective data samples of one or more variables from building equipment during a first period of time and a second period of time. The method can include calculating a first plurality of values for one or more energy audit metrics based on the data samples collected during the first period of time and the second period of time. The method can include comparing the first plurality of values and second plurality of values. The method can include displaying, based on the comparison, at least one of the first plurality of values and/or at least one of the second plurality of values on a dashboard to facilitate adjustment of the one or move variables.

Building energy management system with virtual audit metrics

A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a plurality of times within a first time period; determine a first time at which a first cumulative sum value is at a first maximum; identify a second cumulative sum value at a second maximum at a second time occurring after the first time; compare the identified second cumulative sum value to a threshold; and based on determining that the identified second cumulative sum value does not exceed the threshold, determine that a first fault ended at the first time.

Growing community of inventors

Milwaukee, WI, United States of America

Steven R Vitullo