Automated fault detection and diagnostics in a building management system

• US 9,639,413 B2
• Filed: 05/08/2014
• Issued: 05/02/2017
• 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): Fennema, Robert
• Assistant Examiner(s): Stevens, Thomas
• Application Number:
  US14/273,381
• Publication Number:
  US 20140245071A1
• Time in Patent Office: 1,090 Days
• Field of Search: 345629-637, 714 31, 700276, 707721, 707736
• 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 detecting energy usage faults in a building management system, the controller comprising:

a processing circuit configured to maintain a history of energy usage values for the building management system;

wherein the processing circuit is configured to calculate a threshold parameter using a first subset of the history of energy usage values defined by a sliding time window, the threshold parameter establishing an energy usage value limit relative to the history of energy usage values;

wherein the processing circuit is configured to identify energy outlier days by comparing energy use for a first day with the threshold parameter;

wherein the processing circuit is configured to dynamically update the threshold parameter by sliding the time window forward in time to define a second subset of the history of energy usage values and using the second subset of the history of energy usage values to calculate an updated threshold parameter;

wherein the processing circuit is configured to identify energy outlier days by comparing energy use for a second day with the updated threshold parameter.

2. The controller of claim 1, wherein the sliding time window defines a time period of a predetermined duration ending at a current date;

wherein the processing circuit is configured to slide the time window forward in time and to calculate the updated threshold parameter in response to updating the current date.

3. The controller of claim 1, wherein the processing circuit is configured to calculate a first average daily cost using the history of energy usage values with the outlier days and a second average daily cost using the history of energy usage values excluding the outlier days;

wherein the processing circuit is configured to estimate an additional cost of the energy outlier days by calculating a difference between the first average daily cost and the second average daily cost; and

wherein the processing circuit is configured to assign the estimated additional cost of the energy outlier days to a cost of energy usage faults in the building management system.

4. The controller of claim 3, wherein the processing circuit is configured to output the cost of the energy usage faults to at least one of a remote device and a local electronic display.

5. The controller of claim 1, wherein energy usage comprises at least one of demand and consumption of an energy utility;

wherein the energy utility comprises at least one of an electric utility, a gas utility, a steam utility, and a water utility.

6. A controller for detecting energy usage faults in a building management system, the controller comprising:

a processing circuit configured to maintain a history of energy usage values for the building management system;

wherein the processing circuit is configured to use the history of energy usage values to identify a daily energy usage feature for each of a plurality of days represented in the history of energy usage values;

wherein the processing circuit is configured to group like days based on the identified daily energy usage features to create a plurality of day-type groups;

wherein the processing circuit is configured to calculate a threshold parameter of daily energy usage for each day-type group using the history of energy usage values for the days in the day-type group for which the threshold parameter is calculated;

wherein the processing circuit is configured to identify energy outlier days by comparing energy use for a day with one of the calculated threshold parameters.

7. The controller of claim 6, wherein comparing energy usage for a day with the threshold parameter comprises:

metering energy usage during the day;

identifying a day-type group associated with the day; and

comparing the metered energy usage with the threshold parameter calculated for the identified day-type group to determine whether the day is an energy outlier relative to other days in the same day-type group.

8. The controller of claim 6, wherein energy usage comprises at least one of demand and consumption of an energy utility;

wherein the energy utility comprises at least one of an electric utility, a gas utility, a steam utility, and a water utility.

9. The controller of claim 6, wherein calculating the threshold parameter for each day-type group comprises:

determining a peak energy demand for the days in the day type group for which the threshold parameter is calculated; and

using the peak energy demand as the threshold parameter for the day-type group.

10. The controller of claim 6, 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, by the processing circuit, that the day for which energy usage is metered is an energy outlier relative to other days in the same day-type group.

11. The controller of claim 10, wherein the processing circuit is configured to generate the alarm and to cause the alarm transmission during the day of the metered energy usage.

12. The controller of claim 10, wherein the processing circuit is configured to generate the alarm and to cause the alarm transmission in near real-time in response to the metered energy usage exceeding the threshold value.

13. The controller of claim 6, 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.

14. The controller of claim 13, wherein the processing circuit is configured to determine whether the energy outlier day is also a weather outlier day;

wherein the processing circuit is configured to exclude the energy outlier day from consideration as a fault in response to a determination that the energy outlier day is also a weather outlier day.

15. The controller of claim 6, wherein the processing circuit is configured to forecast energy usage during a day and to use the forecasted energy usage to determine whether the forecasted energy usage exceeds the threshold parameter calculated for the day-type group associated with the day.

16. A computerized method for detecting faults in building equipment, the method comprising:

comparing an actual performance of building equipment to an expected performance of the building equipment, 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 maintaining an exponentially weighting moving average of the error and comparing the exponentially weighted moving average to a threshold;

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

generating an output signal indicative of an equipment 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 building equipment and the expected performance of the building equipment to obtain a cost difference;

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

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

determining whether the actual performance of the building equipment for a period of time is better than a previously determined best performance

storing the actual performance as a new best performance parameter in response to a determination that the actual performance of the building equipment for the period of time is better than a previously determined best performance;

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