INTELLIGENT LIGHTING SYSTEM WITH PREDICTIVE MAINTENANCE SCHEDULING AND METHOD OF OPERATION THEREOF

• US 20170231071A1
• Filed: 02/17/2015
• Published: 08/10/2017
• Est. Priority Date: 03/06/2014
• Status: Active Grant

• Current Assignee: Signify Holding B.V. (Signify N.V.)
• Original Assignee: Philips Lighting Holding BV (Signify N.V.)
• Inventors: ELINSENBERG, INGRID CHRISTINA MARIA, DE BONT, JOHANNA MARIA, BAGHERI, SAFED REZA, SHAH, PARIKSHIT, SINITSYN, ALEXANDRE GEORGIEVICH
• Granted Patent: US 9,894,741 B2
• Time in Patent Office: Days
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: H05B 47/19 via wireless transmission
• CPC Class Codes: H05B 47/20 Responsive to malfunctions or to light source life; for protection
• CPC Class Codes: Y02B 20/40 Control techniques providing energy savings, e.g. smart controller or presence detection

Abstract

Alighting system may include: at least one controller which may be configured to: obtain lighting logging data including feature information related to features of the lighting system and obtained from a plurality of feature spaces; determine lighting prediction data which predicts at least one component failure in the lighting system at a future time in accordance with the lighting logging data and include at least one complex feature; model predicted component failures which are predicted to occur at a future time in accordance with the lighting prediction data and maintenance cost; and/or store the predicted component failures model in a memory.

Claims

1. A lighting system, comprising:

at least one controller which is configured to;

obtain lighting logging data comprising feature information related to features of the lighting system and obtained from a plurality of information sources;

determine a lighting system failure trend using the logging data;

derive using the lighting system failure tread;

at least one complex feature to predict at least one component failure in the lighting system at a future time;

select logging data from the plurality of information sources to calculate the predicted component failure;

model predicted component failur in accordance with the at least one complex feature; and

store the model in a memory.

2. The system of claim 1, wherein the controller is further configured to render the modeled predicted component failures on a display of the lighting system.

3. The system of claim 1, wherein the controller is further configured to obtain the lighting logging data from at least one of a lamp electrical portion, a system operating information portion, sensors, an installation configuration portion, a weather information portion, a traffic portion, and a crime portion.

4. The system of claim 1, wherein the lighting system further comprises a plurality of lamps, and the modeled predicted component failures comprise predicted lamp failures of two or more lamps of the plurality of lamps and corresponding predicted failure times, wherein the controller is further configured to form a cluster of at least two of the predicted lamp failures in accordance with geophysical location and predicted failure times of corresponding lamps, and wherein the controller is further configured to schedule maintenance to replace the lamps of the cluster at a determined time that is determined to be optimal with respect to the cluster.

23. The lighting system of claim 1, wherein the polynomial has a degree of 2 and the at least one complex feature includes at least one of a quadratic voltage trend of the lamp at specific points in time, a quadratic trend of the number of times the lamp attempts to ignite before another ignite instruction is given, and quadratic trend of the number of times the lamp spontaneously extinguishes.

5. (canceled)

6. (canceled)

7. A method of determining performance of a lighting system, the method performed by at least one controller of the lighting system and comprising acts of:

obtaining lighting logging data comprising feature information related to features of the lighting system and obtained from a plurality of information sources;

determining lighting system failure trend using the logging data;

deriving, using the lighting system failure trend, at least one complex feature to predict at least one component failure in the lighting system at a future time;

selecting logging data from the plurality of information sources to calculate the predicted component failure;

modeling predicted component failures in accordance with the at least one complex feature; and

storing the model in a memory.

8. The method of claim 7, further comprising an act of rendering the predicted component failures on a display of the lighting system.

9. The method of claim 7, further comprising an act of obtaining the lighting logging data from at least one of a lamp electrical portion, a system operating information portion, sensors, an installation configuration portion, a weather information portion, a traffic portion, and a crime portion.

10. The method of claim 7, wherein the lighting system further comprises a plurality of lamps, and the modeled predicted component failures comprise predicted lamp failures of two or more lamps of the plurality of lamps and corresponding predicted failure times, the method further comprising acts of:

clustering at least two of the predicted lamp failures into a cluster in accordance with geophysical locations and predicted failure times of corresponding lamps; and

scheduling maintenance to replace the lamps of the cluster at a determined time that is determined to be optimal with respect to the cluster in accordance with the model.

24. The method of claim 7, wherein the polynomial has a degree of 2 and the at least one complex feature includes at least one of a quadratic voltage trend of the lamp at specific points in time, a quadratic trend of the number of times the lamp attempts to ignite before another ignite instruction is given, and quadratic trend of the number of times the lamp spontaneously extinguishes.

25. The computer program of claim 24, wherein the polynomial has a degree of 2 and the at least one complex feature includes at least one of a quadratic voltage trend of the lamp at specific points in time, a quadratic trend of the number of times the lamp attempts to ignite before another ignite instruction is given, and quadratic trend of the number of times the lamp spontaneously extinguishes.

11. (canceled)

12. (canceled)

13. (canceled)

14. A computer program stored on a computer readable memory medium, the computer program configured to determine performance of a lighting system, the computer program comprising:

a program portion configured to;

obtain lighting logging data comprising feature information related to features of the lighting system and obtained from a plurality of information sources;

determine a lighting system failure trend using the logging data;

derive, using the lighting system failure tread;

at least one complex feature to predict at least one component failure in the lighting system at a future time;

select logging data from the plurality of information sources to calculate the predicted component failure;

model predicted component failures in accordance with the at least one complex feature; and

store the model in a memory.

15. The computer program of claim 14, wherein the program portion is further configured to render the predicted component failures on a display of the lighting system.

16. The computer program of claim 14, wherein the program portion is further configured to obtain the lighting logging data from at least one of a lamp electrical portion, a system operating information portion, sensors, an installation configuration portion, a weather information portion, a traffic portion, and a crime portion.

17. The computer program of claim 14, wherein the lighting system further comprises a plurality of lamps, and the modeled predicted component failures comprise predicted lamp failures of two or more lamps of the plurality of lamps and corresponding predicted failure times, wherein the program portion is further configured to cluster at least two of the predicted lamps failures to form a cluster in accordance with geophysical locations and predicted failure times of corresponding lamps, wherein the program portion is further configured to schedule maintenance to replace the lamps of the cluster at a determined time that is determined to be optimal with respect to the cluster in accordance with the model.

18. (canceled)

19. (canceled)

20. (canceled)

21. (canceled)

22. (canceled)