Automated fault detection and diagnostics in a building management system

• US 8,731,724 B2
• Filed: 09/27/2011
• Issued: 05/20/2014
• Est. Priority Date: 06/22/2009
• Status: Active Grant

• Current Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Drees, Kirk H., Kummer, James P.
• Primary Examiner(s): Padmanabhan, Kavita
• Assistant Examiner(s): STEVENS, THOMAS H
• Application Number:
  US13/246,644
• Publication Number:
  US 20120022700A1
• Time in Patent Office: 966 Days
• Field of Search: None
• US Class Current: 700/276
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 30/13 Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00001 characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
• CPC Class Codes: H02J 13/00002 characterised by monitoring
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 13/00016 using a wired telecommunication network or a data transmission bus
• CPC Class Codes: H02J 2310/16 The load or loads being an Information and Communication Technology [ICT] facility
• CPC Class Codes: Y02B 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 20/20 End-user application control systems
• CPC Class Codes: Y04S 20/221 General power management systems
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 40/124 using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 50/14 Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Abstract

Systems and methods for building automation system management are shown and described. The systems and methods relate to fault detection via abnormal energy monitoring and detection. The systems and methods also relate to control and fault detection methods for chillers. The systems and methods further relate to graphical user interfaces for use with fault detection features of a building automation system.

Claims

1. A controller for presenting an estimate of fault cost in a building management system comprising:

a processing circuit configured to maintain a history of energy usage values for the building management system and a threshold parameter establishing an energy usage value limit relative to the history of energy usage values; and

wherein the processing circuit is further configured to identify energy outlier days by comparing energy use for a day to the history of energy usage values and the threshold parameter;

wherein the processing circuit is further configured to subtract an estimated average daily cost, calculated with the outlier days, from an estimated average daily cost calculated excluding the outlier days to estimate an additional cost of the energy outlier days;

wherein the processing circuit is configured to assign the estimated additional cost of the energy outlier days to the estimate of the fault cost in the building management system; and

wherein the processing circuit is configured to output the estimated fault cost to at least one of a remote device and a local electronic display.

2. The controller of claim 1, wherein energy usage comprises at least one of demand and consumption and wherein energy cost comprises an energy metric in terms of financial cost, energy consumption, energy demand, or a blend of energy consumption and energy demand.

3. The controller of claim 1, wherein the processing circuit is configured to group like days to create a plurality of day-type groups;

wherein the processing circuit is further configured to calculate an adjusted threshold parameter of daily peak energy demand for each day-type group.

4. The controller of claim 3, wherein the processing circuit is configured to meter peak energy demand during a day;

and

wherein the processing circuit is configured to use the adjusted threshold parameter calculated for each day-type group to determine whether the metered peak energy demand for the day is an outlier relative to the adjusted threshold parameter calculated for the day-type group associated with the day.

5. The controller of claim 4, wherein the processing circuit is configured to forecast energy consumption and demand for a remainder of the day;

and

wherein the processing circuit is configured to use at least one of the forecasted energy consumption and demand for the remainder of the day to determine whether the adjusted threshold parameter is expected to be exceeded;

generating a notification and causing the notification to be transmitted to a remote device in response to a determination that the adjusted threshold parameter is expected to be exceeded.

6. The controller of claim 4, wherein the processing circuit is configured to generate an alarm and to cause the alarm to be transmitted to a remote device in response to a determination that the metered peak energy demand for the day is an outlier relative to the adjusted threshold parameter calculated for the day-type group associated with the day.

7. The controller of claim 6, wherein the processing circuit is configured to generate the alarm and to cause the alarm transmission during the day of the metered peak energy demand.

8. The controller of claim 6, wherein the processing circuit is configured to generate the alarm and to cause the alarm transmission in near real-time relative to the metered peak of the day.

9. The controller of claim 1, wherein the processing circuit is configured to identify weather outlier days and to remove the identified energy outlier days corresponding to identified weather outlier days prior to conducting a cost analysis.

10. The controller of claim 9, wherein the processing circuit is configured to identify weather outlier days by comparing a weather parameter for the day to a history of weather parameters.

11. The controller of claim 10, wherein the processing circuit compares the weather parameter for the day to a history of weather parameters using a generalized extreme studentized distribution of the history of weather parameters.

12. The controller of claim 1, wherein the processing circuit is further configured to cause a graphical user interface to be displayed on the local electronic display device, wherein the graphical user interface comprises indicia that a fault has occurred;

wherein the graphical user interface is configured to at least one of;

(a) graphically represent fault days relative to non-fault days,

(b) allow a user to click on a graphical indicator for the fault to cause additional information for the fault to be displayed,

(c) allow a user to click on a graphical indicator for the fault to cause raw data associated with the fault to be displayed,

(d) graphically represent a distribution of fault data relative to non-fault data,

(e) graphically represent a distribution of faulty equipment relative to non-faulty equipment,

(f) view a magnitude of the fault relative to other data or equipment,

(g) view an additional cost associated with an existence of the fault relative to a non-fault condition,

(h) view an expected energy or monetary cost relative to an actual energy cost or monetary cost.

13. The controller of claim 1, wherein the processing circuit is configured to cause a graphical user interface to generate a graph of daily energy consumption for a period of time, and wherein the processing circuit causes an indicia that a fault has occurred to be graphically associated with at least one of a day and an energy consumption value for the day.

14. The controller of claim 13, wherein the processing circuit is configured to allow selection of the indicia;

and wherein the processing circuit is configured to generate additional graphical information regarding the fault in response to selection of the indicia.

15. The controller of claim 13, wherein the additional graphical information regarding the fault comprises a table including at least an additional cost estimated to be associated with the fault.

16. A computerized method for detecting chiller faults, comprising:

comparing an actual performance of a chiller to an expected performance of the chiller, wherein the comparison comprises calculating an error between the actual performance and the expected performance;

applying results of a statistical analysis to the comparison to identify a deviation from the expected performance, wherein identifying a deviation from the expected performance comprises;

(1) maintaining an exponentially weighting moving average of the error, and (2) comparing the exponentially weighted moving average to a threshold;

determining whether to identify the error as associated with a chiller fault based on whether the exponentially weighted moving average exceeds the threshold; and

generating an output signal indicative of a chiller fault in response to the exponentially weighted moving average exceeding the threshold.

17. The computerized method of claim 16, further comprising:

applying utility rate information to the actual performance of the chiller and the expected performance of the chiller to obtain a cost difference;

outputting an indication the cost difference as attributed to the chiller fault in response to a determination that the error should be identified as associated with the chiller fault.

18. The computerized method of claim 17, further comprising:

determining whether the actual performance of the chiller for a period of time is better than a previously determined best performance, and in response to a determination that the actual performance of the chiller for the period of time is better than the previously determined best performance, storing the actual performance as a new best performance parameters;

using the new best performance parameters to build a new regression model for calculating expected performance in a subsequent period of time.

19. The computerized method of claim 18, wherein the determination of whether the actual performance of the chiller for the period of time is better than the previously determined best performance is completed by comparing integrated part load values (IPLV) of the actual performance of the chiller and the previously determined best performance.

20. The computerized method of claim 16, further comprising:

testing parameters of the actual performance to determine whether the parameters satisfy a steady state energy balance;

identifying the parameters of the actual performance as inaccurate data in response to a determination that the parameters do not satisfy a steady state energy balance;

refraining from conducting further fault detection analysis using the parameters of the actual performance in response to the identification as inaccurate data.

21. The computerized method of claim 16, further comprising:

calculating the expected performance using a linear regression model having coefficients calculated based on variables of evaporator and condenser water flow rates;

receiving a sensor value of a water pressure drop across the evaporator;

receiving a sensor value of a water pressure drop across the condenser;

estimating the evaporator water flow rate using a proportional relationship between the evaporator pressure drop and the evaporator flow rate raised to a power determined by testing; and

estimating the condenser water flow rate using a proportional relationship between the condenser pressure drop and the condenser flow rate raised to a power determined by testing.