SYSTEMS AND METHODS FOR AUTOMATICALLY CREATING AND USING ADAPTIVE PCA MODELS TO CONTROL BUILDING EQUIPMENT

• US 20180087790A1
• Filed: 09/28/2016
• Published: 03/29/2018
• Est. Priority Date: 09/28/2016
• Status: Abandoned Application

• Current Assignee: Johnson Controls Tyco IP Holdings LLP (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Perez, Carlos Felipe Alcala
• Application Number:
  US15/279,336
• Publication Number:
  US 20180087790A1
• Time in Patent Office: Days
• CPC Class Codes: F24F 11/30 for purposes related to the operation of the system, e.g. for safety or monitoring
• CPC Class Codes: F24F 11/32 Responding to malfunctions or emergencies
• CPC Class Codes: F24F 11/46 Improving electric energy efficiency or saving
• CPC Class Codes: F24F 11/52 Indication arrangements, e.g. displays
• CPC Class Codes: F24F 11/56 Remote control
• CPC Class Codes: F24F 11/58 using Internet communication
• CPC Class Codes: F24F 11/62 characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
• CPC Class Codes: F24F 11/63 Electronic processing
• CPC Class Codes: F24F 11/64 using pre-stored data
• CPC Class Codes: F24F 2110/00 Control inputs relating to air properties
• CPC Class Codes: G05B 2219/25011 Domotique, I-O bus, home automation, building automation
• CPC Class Codes: G05B 2219/2614 HVAC, heating, ventillation, climate control
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0283 Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Abstract

A building management system includes connected equipment and a predictive diagnostics system. The connected equipment is configured to measure a plurality of monitored variables. The predictive diagnostics system includes a communications interface, a principal component analysis (PCA) modeler, a controller. The communications interface is configured to receive samples of the monitored variables from the connected equipment. The PCA modeler is configured to automatically assign each of the samples of the monitored variables to one of a plurality of operating states of the connected equipment and to construct a PCA model for each operating state using the samples assigned to the operating state. The controller is configured to use the PCA models to adjust an operation of the connected equipment.

Claims

1. A building management system comprising:

connected equipment configured to measure a plurality of monitored variables; and

a predictive diagnostics system comprising;

a communications interface configured to receive samples of the monitored variables from the connected equipment;

a principal component analysis (PCA) modeler configured to automatically assign each of the samples of the monitored variables to one of a plurality of operating states of the connected equipment and to construct a PCA model for each operating state using the samples assigned to the operating state; and

a controller configured to use the PCA models to adjust an operation of the connected equipment.

2. The building management system of claim 1, wherein the predictive diagnostics system further comprises a sample indexer configured to generate a fault detection index for each of the samples;

wherein the PCA modeler is configured to compare the fault detection index to a control limit and determine that the connected equipment is switching between the operating states in response to the fault detection index exceeding the control limit.

3. The building management system of claim 2, wherein the PCA modeler is configured to:

determine whether multiple consecutive values of the fault detection index exceed the control limit; and

determine that the connected equipment is switching between the operating states in response to a determination that the multiple consecutive values of the fault detection index exceed the control limit.

4. The building management system of claim 1, wherein the PCA modeler is configured to:

recursively update a variance of the samples each time a new sample is received; and

determine whether the connected equipment is switching between the operating states based on the variance of the samples.

5. The building management system of claim 4, wherein the PCA modeler is configured to:

identify a new value of the variance and one or more previous values of the variance;

calculate a filtered variance using the new value of the variance and the one or more previous values of the variance; and

determine whether the connected equipment is switching between the operating states based on the filtered variance.

6. The building management system of claim 5, wherein the PCA modeler is configured to:

calculate the filtered variance by averaging the new value of the variance with the one or more previous values of the variance;

recursively update the filtered variance each time a new sample is received.

7. The building management system of claim 4, wherein the PCA modeler is configured to:

calculate a variance slope based on multiple consecutive values of the variance;

determine whether the variance slope exceeds a threshold value; and

determine that the connected equipment is switching between the operating states in response to a determination that the variance slope exceeds the threshold value.

8. The building management system of claim 7, wherein the PCA modeler is configured to:

recursively update the variance slope each time a new sample is received;

determine whether multiple consecutive values of the variance slope are less than the threshold value; and

determine that the connected equipment has reached a new operating state in response to a determination that the multiple consecutive values of the variance slope are less than the threshold value.

9. The building management system of claim 4, wherein the PCA modeler is configured to:

determine whether the connected equipment has reached a new operating state based on the variance of the samples;

generate a new PCA model for the new operating state in response to a determination that the connected equipment has reached the new operating state; and

store the new PCA model in a state library.

10. The building management system of claim 9, wherein the PCA modeler is configured to:

determine whether the new PCA model overlaps with an existing PCA model stored in the state library; and

in response to a determination that the new PCA model overlaps the existing PCA model;

create a merged PCA model by merging the new PCA model with the existing PCA model; and

replace the existing PCA model with the merged PCA model in the state library.

11. A method for monitoring and controlling connected equipment in a building management system, the method comprising:

measuring a plurality of monitored variables at the connected equipment;

receiving samples of the monitored variables at a predictive diagnostics system;

automatically assigning each of the samples of the monitored variables to one of a plurality of operating states of the connected equipment;

constructing a PCA model for each operating state using the samples assigned to the operating state; and

using the PCA models to adjust an operation of the connected equipment.

12. The method of claim 11, further comprising:

generating a fault detection index for each of the samples;

comparing the fault detection index to a control limit; and

determining that the connected equipment is switching between the operating states in response to the fault detection index exceeding the control limit.

13. The method of claim 12, further comprising:

determining whether multiple consecutive values of the fault detection index exceed the control limit; and

determining that the connected equipment is switching between the operating states in response to a determination that the multiple consecutive values of the fault detection index exceed the control limit.

14. The method of claim 11, further comprising:

recursively updating a variance of the samples each time a new sample is received; and

determining whether the connected equipment is switching between the operating states based on the variance of the samples.

15. The method of claim 14, further comprising:

identifying a new value of the variance and one or more previous values of the variance;

calculating a filtered variance using the new value of the variance and the one or more previous values of the variance; and

determining whether the connected equipment is switching between the operating states based on the filtered variance.

16. The method of claim 15, further comprising:

calculating the filtered variance by averaging the new value of the variance with the one or more previous values of the variance;

recursively updating the filtered variance each time a new sample is received.

17. The method of claim 14, further comprising:

calculating a variance slope based on multiple consecutive values of the variance;

determining whether the variance slope exceeds a threshold value; and

determining that the connected equipment is switching between the operating states in response to a determination that the variance slope exceeds the threshold value.

18. The method of claim 17, further comprising:

recursively updating the variance slope each time a new sample is received;

determining whether multiple consecutive values of the variance slope are less than the threshold value; and

determining that the connected equipment has reached a new operating state in response to a determination that the multiple consecutive values of the variance slope are less than the threshold value.

19. A heating, ventilation, or air conditioning (HVAC) device comprising:

sensors configured to measure a plurality of monitored variables; and

a predictive diagnostics system configured to receive samples of the monitored variables from the sensors, the predictive diagnostics system comprising a principal component analysis (PCA) modeler configured to automatically assign each of the samples of the monitored variables to one of a plurality of operating states of the HVAC device and to construct a PCA model for each operating state using the samples assigned to the operating state; and

a controller configured to use the PCA models to adjust an operation of the HVAC device.

20. The HVAC device of claim 19, wherein the PCA modeler is configured to:

recursively update a variance of the samples each time a new sample is received; and

determine whether the HVAC device is switching between the operating states based on the variance of the samples.