Honeywell International Inc.

Jiri Vass

Jiri Rojicek

Petr Endel

Cuong Huynh

Patrick Brisbine

Paul Kleinhans

Bose Falk

David Kucera

Bin Sai

Liana Maria Kiff

Conrad Bruce Beaulieu

Petr Stluka

Martin Strelec

Vladimir Bicik

Jana Trojanova

Greg Bernhardt

Ard Van Schie

Henrik Dibowski

A controller for classifying devices of a building management system (BMS). The controller may be configured to obtain an entity name for a device, extract a core name from the entity name, compare the core name to candidate core names, determine scores for each comparison, identify a highest score, identify a class of a candidate core name, and classify the device in the class.

Classifying devices from entity names based on contextual information

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

Scoring entity names of devices in a building management 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

Methods, systems, and computer-readable media are described herein. One method embodiment includes determining an unscaled efficiency signal of combustion equipment using data measured from the combustion equipment, determining a theoretical efficiency signal of the combustion equipment using a theoretical efficiency surface of the combustion equipment and a subset of the measured data, and normalizing the unscaled efficiency signal using values from a correlated portion of the theoretical efficiency signal to monitor efficiency of the combustion equipment. Other embodiments can include providing a performance indicator of the combustion equipment in response to an operational mode change.

Monitoring efficiency and operational mode changes of combustion equipment

A property of a signal may be detected by sampling a signal and determining a scalar indicator of the samples. The samples can be transformed to create a transformed signal. A scalar indicator of the transformed signal may be determined and compared to the scalar indicator of the sample. If the comparison yields a number greater than a selected threshold value then the signal that was sampled may be deemed to include the property. A property indicator signal may be driven low or high to indicate that the signal has the property. The time and duration of time that the signal is deemed to include the property may be recorded. The recorded data may be reviewed and appropriate action may be taken.

Signal property detector

Processing systems and methods used to identify faults in the operation of one or more field instruments are described and shown. In one embodiment, a sensing system includes one or more field instruments and a processing component configured to process data from the field instruments. This processing may include identifying invalid samples of data using an algorithm; correlating invalid samples of data to specific field instruments; and determining a likelihood of a fault occurring on the specific field instruments. The processing may occur in real-time using an online processing technique, or with an offline processing technique on data maintained in a data store.

Monitoring for invalid data from field instruments

A system for monitoring thermal comfort of a room. Sensor data concerning temperature, relative humidity, air speed and air flow may be collected from the room. The sensor data may provide thermal comfort level information about the room. The level information may be quantified in terms of a thermal comfort level index, such as a predicted mean vote, which can be used to identify one or more thermal comfort levels in the room with a numerical measure. The one or more levels may be portrayed as a visualization in terms of a 3D plot of temperature, humidity and air speed, several 2D plots, a dashboard, or other items. The visualization may easily enable one to see where setpoint adjustment is possible in a heating, ventilation and air conditioning system to save energy while maintaining thermal comfort acceptable to occupants in the room, whether during a heating season or a cooling season.

Room thermal comfort monitor

An approach for providing setpoint optimization for air handling units. An area for such optimization may incorporate choosing a thermal comfort level index, finding a zone air properties target area and computing a supply air properties target area. Beyond the present approach, an outdoor air portion may be optimized, a feasible psychrometric path found, supply airflow versus temperature optimized, and a controller run to meet setpoints.

Setpoint optimization for air handling units

A method for online condition-based monitoring (CBM) of a tank farm including a plurality of storage tanks includes providing a tank model including a diagnostic and/or predictive tank model based on calculated tank metrics that is derived from historical data including tank operation data. The calculated tank metrics include tank operational metrics based on the tank operational data for the storage tanks and tank condition metrics based on tank inspection or maintenance data for the storage tanks. The tank model provides relationships between the tank condition metrics and the tank operational metrics. Results are generated using the tank model including at least one failure indicia for at least a first of the storage tanks using the calculated tank metrics and current measured data for the first tank as inputs to the tank model. The failure indicia is processed for scheduling at least one maintenance task for the first tank.

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Prague, Czechia

Jiri Vass