EQUIPMENT FAULT DETECTION, DIAGNOSTICS AND DISAGGREGATION SYSTEM

• US 20140172400A1
• Filed: 12/14/2012
• Published: 06/19/2014
• Est. Priority Date: 12/14/2012
• Status: Active Grant

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Majewski, Joseph, Anglin, Mark, Gall, Thomas, Fisera, Radek
• Granted Patent: US 10,083,255 B2
• Time in Patent Office: Days
• US Class Current: 703/18
• CPC Class Codes: F24F 1/0035 characterised by introduction of outside air to the room
• CPC Class Codes: F24F 11/32 Responding to malfunctions or emergencies
• CPC Class Codes: F24F 2221/16 mounted on the roof
• CPC Class Codes: F24F 3/001 in which the air treatment in the central station takes place by means of a heat-pump or by means of a reversible cycle reversible cycle for humidifying and drying air F24F3/147
• CPC Class Codes: G01H 1/00 Measuring characteristics of vibrations in solids by using direct conduction to the detector G01H9/00, G01H11/00 take precedence
• CPC Class Codes: G06F 2119/06 Power analysis or power optimisation
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 30/367 Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

Abstract

A system for fault detection and diagnostics of equipment. The system may also be capable of disaggregation and/or virtual submetering of energy consumption by equipment, such as that of heating, ventilation and air conditioning, lighting, and so forth, in a building. Vibration and current sensors, along with one or more algorithms, may be utilized for fault detection and diagnostics of equipment. Models may be developed to aid in deducing energy consumption of individual components of equipment, and the like, for a building.

Claims

1. A roof top unit fault detection and diagnostics appliance comprising:

a computer for connection to a roof top unit;

a vibration sensor for attachment to a roof top unit and connected to the computer; and

one or more current sensors for connection to a power feed to the roof top unit and connected to the computer; and

wherein;

the vibration sensor provides signals representing vibration at the roof top unit to the computer;

the one or more current sensors provide signals representing total power consumption by the roof top unit; and

the signals representing vibration and total power consumption are analyzed to check for certain types of degradation and malfunction of the roof top unit.

2. The appliance of claim 1, wherein the computer comprises:

a fault detection and diagnostic and efficiency monitoring algorithm module; and

wherein the fault detection and diagnostic and efficiency monitoring algorithm module detects equipment faults and/or operation inefficiencies in the heating, cooling and mechanical subsystems of the roof top unit.

3. The appliance of claim 2, wherein the computer further comprises a load baselining manager that deduces energy consumption of individual components of the roof top unit.

4. The appliance of claim 2, wherein the computer further comprises a load baselining manager that reduces energy consumption by individual components of the roof top unit by removing detected equipment faults and/or operation inefficiencies in the heating, cooling and mechanical subsystems of the roof top unit as indicated by the fault detection and diagnostic and efficiency monitoring algorithm module, based on signals representing vibration at the roof top unit, total power consumption by the roof top unit indicated by the one or more current sensors, and/or power consumption of each of one or more components indicated by a difference of readings of power consumption at the main meter caused by each of the respective components being turned on and off.

5. The appliance of claim 2, wherein the computer further comprises a load disaggregation module that determines per-load energy consumptions from current data representing total power consumption by the roof top unit.

6. The appliance of claim 2, wherein the fault detection and diagnostic and efficiency monitoring algorithm module uses differences in actual consumption of the equipment versus model-predicted consumption to detect potential issues with the roof top unit or a performance degradation of the roof top unit over a period of time.

7. The appliance of claim 2, wherein the computer further comprises a data logger module that stores time series data from the vibration sensor and the one or more current sensors at a power feed to the roof top unit, for access and analysis by the fault detection and diagnostic and efficiency monitoring algorithm module.

8. The appliance of claim 1, wherein:

if the signals representing the vibration exhibit a pattern change, then the pattern change can be compared with known pattern changes representing certain types of degradation and malfunction of the roof top unit; and

if the pattern change matches a known pattern change, then the roof top unit can be considered to have a certain type of degradation or malfunction as represented by the known pattern change.

9. A virtual submetering system for a commercial building comprising:

a library of electrical power robust models representing virtually all types of equipment encountered in commercial buildings; and

a set of generic device models in the library for achieving a generality for particular devices constituting a type of equipment so that a generic device model is developed to incorporate terms that lead to an electrical power consumption associated with a generic device model representing a device.

10. The system of claim 9, wherein:

virtual disaggregation of an electrical power load of a commercial building is achieved with the library of electrical power robust models and the set of generic device models for virtually all types of equipment;

contextual information of the types of equipment is exploited for selecting electrical power robust models from the library, representing virtually all types of equipment connected on an output end of a main meter having an input end for connection to an electrical power feed, the main meter indicating actual total electrical power consumption by a commercial building; and

an estimate of a disaggregation of an electrical power load of the commercial building is then obtained by estimating parameters for virtually all models of devices receiving electrical power, from a total electrical power consumption indicated at the main meter and a successive partitioning of the total electrical power consumption of the devices represented by the models, to electrical power consumption by each device.

11. The system of claim 10, wherein:

the optional contextual information of varying quality and/or degree is input into an optimizer in a form of constraints;

the optimizer is provided with initial values and expected ranges of the parameters derived from available contextual information; and

an optimization task can be further adjusted according to contextual information available.

12. The system of claim 11, wherein disintegration of a commercial building load is performed using recursive versions of an algorithm of the optimizer.

13. The system of claim 11, wherein parameters comprise at least one or more of supply and return air temperatures, outside air temperature, humidity, supply and return chilled water temperatures, control signals, operating schedules, energy consumption of equipment, and other points used for electricity consumption modeling.

14. The system of claim 11, wherein:

contextual information comprises mandatory contextual information and optional contextual information;

the mandatory contextual information comprises devices, and type and class of devices connected beyond a measurement point that can be derived from a building ontology, wiring hierarchy data or wiring schemes, and data from a measurement point; and

the optional contextual information comprises one or more items comprising power consuming equipment information, nominal power consumption, cooling tonnage, control strategy, and other like information.

15. The system of claim 14, wherein a manual entry of the optional contextual information can be enabled from a wizard.

16. The system of claim 10, wherein disintegration of a commercial building load is performed in a batch mode or in an online mode.

17. A system for virtual submetering power consumption by a building, comprising:

a library of electricity consumption models; and

an algorithm which accesses contextual information of devices of a building being virtually submetered; and

wherein;

disaggregation of power consumption by the building relative to electricity consumption devices is achieved by using the library of electricity consumption models and contextual information;

electricity consumption models represent electricity consumption devices in buildings; and

the contextual information is exploited when electricity consumption models are selected from the library, that represent virtually every electricity consumption device connected at an output end of a main electrical meter for the building, and the main electrical meter having an input end for connection to a power feed.

18. The system of claim 17, wherein:

contextual information comprises one or more items comprising nominal power requirements, tonnage, device type, wiring hierarchy, building ontology, model structure, device class, data points, electricity consumption, specs, and other information about an electricity consumption device represented by an electricity consumption model; and

the contextual information pertaining to an electricity consumption model is updated upon receipt of information about a device that is represented by the electricity consumption model.

19. The system of claim 17, wherein:

electricity consumption of a device is indicated by a difference of readings of power consumption at the main electrical meter caused by one device at a time being turned on or off; and

having only one device being turned on or off at once is achieved by a system controller that triggers a training control sequence at a time when a customer operation cannot necessarily be corrupted; and

the training control sequence substantially helps an optimizer to quickly and accurately identify individual system sub-parts electricity consumption models.

20. The system of claim 17, wherein:

electricity consumption models for virtually all electricity consumption devices in the system are accessed from the library of electricity consumption models;

electricity consumption for each electricity consumption model, if available, is obtained from the contextual information pertaining to the electricity consumption models representing the electricity consumption devices of the system; and

individual electricity consumptions are obtained by a model identification process performed by an optimizer.