Honeywell International Inc.

Jiri Rojicek

Vladimir Bicik

Wendy K Foslien

Matthew E Garr

Jiri Vass

Karel Macek

Petr Stluka

Henrik Dibowski

Petr Endel

Ondrej Holub

Jana Trojanova

Vaclav Slimacek

Karel Marik

Liana Maria Kiff

Conrad Bruce Beaulieu

Marek Sikora

Radek Fisera

Zdenek Schindler

Greg Bernhardt

Jakub Malaník

Jan Berka

Petr Cisar

Eva Jerhotova

Frank P DiMartino, Jr

An approach for measuring health of a system using benchmarks. The approach may incorporate defining benchmarks with sensors providing data of selected conditions of a system, collecting a predetermined amount of most recent data from the sensors for one or more modes of the system, training a model that represents the system, the model having a response variable that is a function of the predetermined amount of the most recent data from the sensors, applying the model trained, to the benchmarks to get a response of the model in a form of the response variable that is the function of the predetermined amount of the most recent data from the sensors, and comparing the response of the model of the most recent time window with a response of the model of a previous time windows to determine health of the system.

Process for adaptable health, degradation and anomaly detection of systems using benchmarks

Embodiments of the disclosure provide for determination and/or notifying of when an asset requires maintenance. Such embodiments enable outputting notifications in circumstances where tracked data for operations of an asset indicate or are predicted to violate particular thresholds. Some embodiments receive a feedback data set for an asset, identify a command data set for the asset, determine a delay value based at least in part on the feedback data set and the command data set, determine a command-feedback difference value based at least in part on the feedback data set and the command data set, and output a degradation indicator based at least in part on the delay value and the command-feedback difference value.

Apparatuses, computer-implemented methods, and computer program products for improved asset degradation monitoring and prediction

A tool for providing a visualization of a system may reveal an interactive navigation environment for building performance observation and assessment. The tool may be associated with a processor. The environment may incorporate a treemap, a graph pane, a treemap filter, a graph pane selector, a selected units box and a date/time control mechanism. A visualization of the environment, among other things, may be presented on a display. The treemap may exhibit a building geometry and/or equipment units hierarchically, along with some data information. Units may be interactively selected from the treemap and placed in the box for analysis. The graph pane may show a configuration and display of unit analysis. Selection of detailed views for units in the box may be provided by the graph pane selector. Date and time intervals for analysis may be selected by the control mechanism.

Interactive navigation environment for building performance visualization

Various embodiments described herein relate to providing asset behavior modeling. In this regard, a request to obtain one or more asset insights with respect to an asset is received. In response to the request, a model is executed based on sensor data associated with the asset and asset scenario data associated with different operation scenarios for the asset. The model represents predicted asset behavior of the asset to provide the one or more asset insights. Furthermore, in response to the request, one or more actions are performed based on the one or more asset insights.

Asset behavior modeling

An approach and system incorporating obtaining a semantic model for a device, identifying properties from the semantic model, processing the properties, obtaining a property role, identifying a property role filter that correlates to the property role, and generating parameter values associated with the property role filter.

Configuring devices of a building automation system

Methods, devices, and systems for matching a building automation algorithm to a building automation system are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to select a building automation algorithm from a pre-defined library of building automation algorithms, determine whether the selected building automation algorithm matches a building information model associated with the building automation system, and output, upon the selected building automation algorithm matching the building information model, the selected algorithm.

Matching a building automation algorithm to a building automation system

A system controls and monitors a heating, ventilation, and air conditioning (HVAC) system. The system receives raw data from HVAC equipment, controller performance monitoring (CPM) indicators associated with the HVAC equipment, and a set of rules associated with the HVAC equipment. The system processes the CPM indicators and the raw data using the set of rules to generate fault relevancies, and processes the fault relevancies.

Application of reasoning rules for fault diagnostics of control-related faults

A method includes obtaining energy consumption information representative of energy consumption behaviors of multiple customers, grouping the multiple customers into multiple different clusters based on the consumption behaviors of the multiple customers, and generating an energy consumption model for each different cluster to enable forecasting of energy demand of the multiple customers.

Load forecasting for residential sector demand response

A thermal control system for a building is disclosed, which includes a regression model: Given a forecast temperature outside the building, the regression model predicts how much an HVAC system will cost to run during a day, for a given set of time-varying target temperatures for all the thermostats in the thermal control system. The thermal control system may also include an optimizer, which invokes multiple applications of the regression model. Given a forecast temperature outside the building, the optimizer predicts an optimal set of time-varying target temperatures for all the thermostats in the thermal control system. Running the HVAC system with the optimal set of time-varying target temperatures should have a reduced or a minimized cost, or a reduced or minimized total energy usage. The optimizer works by running the regression model repeatedly, while adjusting the time-varying target temperature for each thermostat between runs of the model.

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Prague, Czechia

Jiri Rojicek