Smart motor driver architecture with built-in MEMS sensor based early diagnosis of faults
A system in package encloses a sensor and motor driver circuit. In an implementation, the sensor is an integrated circuit micro-electro-mechanical-systems (MEMS) sensor and the driver circuit is a motor driver circuit. Non-motor winding data information is sensed by the MEMS sensor and processed for the purpose of characterizing known fault patterns for motors; characterizing normal operation of the motor; and evaluating continued operation of the motor to detect abnormal motor behavior and instances of motor fault. The motor is driven using PWM control and the information output by the MEMS sensor is sampled at sampling times having a fixed timing relationship relative to the PWM control signals.
1. A method, comprising:
operating an application motor;
sensing non-motor winding data information relating to the application motor using one or more MEMS sensors mounted to the application motor;
comparing the sensed non-motor winding data information to a library of non-motor winding data information correlated to non-faulty motor operation; and
if there is a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-faulty motor operation, then continuing operation of the application motor; and
if there is not a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-faulty motor operation, then comparing the sensed non-motor winding data information to a library of non-motor winding data information correlated to known fault patterns for faulty motors; and
if there is a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to known fault patterns, then issuing a fault warning for the known fault pattern that matches.
2. The method of claim 1 , comprising generating the library of non-motor winding data information correlated to known fault patterns, wherein generating comprises:
operating a plurality of faulty motors having different known fault patterns over a range of motor operating speeds;
sensing non-motor winding data information relating to each of the plurality of faulty motors using at least one MEMS sensor; and
processing the non-motor winding data information to generate the library of non-motor winding data information correlated to the known fault patterns.
3. The method of claim 2 , wherein operating the plurality of faulty motors comprises:
generating pulse width modulation (PWM) control signals for application to the plurality of faulty motors; and
sampling the non-motor winding data information generated by the at least one MEMS sensor at sample times that have a fixed timing relationship relative to the PWM control signals.
4. The method of claim 3 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
5. The method of claim 2 , wherein sensing non-motor winding data information is performed at a factory testing and calibration location where the plurality of faulty motors are located.
6. The method of claim 1 , wherein if there is not a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-fault motor operation, then issuing a warning as to possible motor fault.
7. The method of claim 1 , wherein operating the motor comprises:
generating pulse width modulation (PWM) control signals for application to the motor; and
sampling the non-motor winding data information generated by the one or more MEMS sensors at sample times that have a fixed timing relationship relative to the PWM control signals.
8. The method of claim 7 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
9. The method of claim 1 , comprising generating the library of non-motor winding data information correlated to non-faulty motor operation, wherein generating comprises:
operating a test motor that does not have a fault pattern over a range of motor operating speeds;
sensing non-motor winding data information relating to the test motor using at least one MEMS sensor; and
processing the non-motor winding data information to generate the library of non-motor winding data information correlated to non-faulty motor operation.
10. The method of claim 9 , wherein operating the test motor comprises:
generating pulse width modulation (PWM) control signals for application to the test motor; and
sampling the non-motor winding data information generated by the at least one MEMS sensor at sample times that have a fixed timing relationship relative to the PWM control signals.
11. The method of claim 10 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
12. The method of claim 9 , wherein the test motor and the application motor are the same motor.
13. The method of claim 12 , wherein sensing non-motor winding data information is performed at a location where the application motor is installed for operation.
14. The method of claim 1 , wherein sensing non-motor winding data information comprises sensing ambient air flow.
15. The method of claim 1 , wherein sensing non-motor winding data information comprises sensing motor vibration.
16. The method of claim 1 , wherein sensing non-motor winding data information comprises sensing ambient sound.
17. The method of claim 1 , wherein sensing non-motor winding data information comprises sensing ambient temperature.
18. A method, comprising:
operating an application motor;
sensing non-motor winding data information relating to the application motor using one or more MEMS sensors mounted to the application motor;
comparing the sensed non-motor winding data information to a library of non-motor winding data information correlated to non-faulty motor operation; and
if there is a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-faulty motor operation, then continuing operation of the application motor; and
if there is not a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-fault motor operation, then issuing a warning as to possible motor fault.
19. The method of claim 18 , wherein operating the motor comprises:
generating pulse width modulation (PWM) control signals for application to the motor; and
sampling the non-motor winding data information generated by the one or more MEMS sensors at sample times that have a fixed timing relationship relative to the PWM control signals.
20. The method of claim 19 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
21. The method of claim 18 , comprising generating the library of non-motor winding data information correlated to non-faulty motor operation, wherein generating comprises:
operating a test motor that does not have a fault pattern over a range of motor operating speeds;
sensing non-motor winding data information relating to the test motor using at least one MEMS sensor; and
processing the non-motor winding data information to generate the library of non-motor winding data information correlated to non-faulty motor operation.
22. The method of claim 21 , wherein operating the test motor comprises:
generating pulse width modulation (PWM) control signals for application to the test motor; and
sampling the non-motor winding data information generated by the at least one MEMS sensor at sample times that have a fixed timing relationship relative to the PWM control signals.
23. The method of claim 22 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
24. The method of claim 21 , wherein the test motor and the application motor are the same motor.
25. The method of claim 24 , wherein sensing non-motor winding data information is performed at a location where the application motor is installed for operation.
26. The method of claim 18 , wherein sensing non-motor winding data information comprises sensing ambient air flow.
27. The method of claim 18 , wherein sensing non-motor winding data information comprises sensing motor vibration.
28. The method of claim 18 , wherein sensing non-motor winding data information comprises sensing ambient sound.
29. The method of claim 18 , wherein sensing non-motor winding data information comprises sensing ambient temperature.
30. A method, comprising:
operating an application motor by:
generating pulse width modulation (PWM) control signals for application to the motor; and
sampling the non-motor winding data information generated by the one or more MEMS sensors at sample times that have a fixed timing relationship relative to the PWM control signals;
sensing non-motor winding data information relating to the application motor using one or more MEMS sensors mounted to the application motor;
comparing the sensed non-motor winding data information to a library of non-motor winding data information correlated to non-faulty motor operation; and
if there is a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-faulty motor operation, then continuing operation of the application motor.
31. The method of claim 30 , further comprising:
if there is not a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-faulty motor operation, then comparing the sensed non-motor winding data information to a library of non-motor winding data information correlated to known fault patterns for faulty motors; and
if there is a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to known fault patterns, then issuing a fault warning for the known fault pattern that matches.
32. The method of claim 31 , comprising generating the library of non-motor winding data information correlated to known fault patterns, wherein generating comprises:
operating a plurality of faulty motors having different known fault patterns over a range of motor operating speeds;
sensing non-motor winding data information relating to each of the plurality of faulty motors using at least one MEMS sensor; and
processing the non-motor winding data information to generate the library of non-motor winding data information correlated to the known fault patterns.
33. The method of claim 32 , wherein operating the plurality of faulty motors comprises:
generating pulse width modulation (PWM) control signals for application to the plurality of faulty motors; and
sampling the non-motor winding data information generated by the at least one MEMS sensor at sample times that have a fixed timing relationship relative to the PWM control signals.
34. The method of claim 33 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
35. The method of claim 32 , wherein sensing non-motor winding data information is performed at a factory testing and calibration location where the plurality of faulty motors are located.
36. The method of claim 30 , further comprising:
if there is not a match between the sensed non-motor winding data information to the library of non-motor winding data information correlated to non-fault motor operation, then issuing a warning as to possible motor fault.
37. The method of claim 30 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
38. The method of claim 30 , comprising generating the library of non-motor winding data information correlated to non-faulty motor operation, wherein generating comprises:
operating a test motor that does not have a fault pattern over a range of motor operating speeds;
sensing non-motor winding data information relating to the test motor using at least one MEMS sensor; and
processing the non-motor winding data information to generate the library of non-motor winding data information correlated to non-faulty motor operation.
39. The method of claim 38 , wherein operating the test motor comprises:
generating pulse width modulation (PWM) control signals for application to the test motor; and
sampling the non-motor winding data information generated by the at least one MEMS sensor at sample times that have a fixed timing relationship relative to the PWM control signals.
40. The method of claim 39 , wherein the PWM control signals comprise pulses sharing a common pulse line of symmetry, and said sample times have a fixed timing relationship relative to the common pulse line of symmetry for the PWM control signals.
41. The method of claim 38 , wherein the test motor and the application motor are the same motor.
42. The method of claim 41 , wherein sensing non-motor winding data information is performed at a location where the application motor is installed for operation.
43. The method of claim 30 , wherein sensing non-motor winding data information comprises sensing ambient air flow.
44. The method of claim 30 , wherein sensing non-motor winding data information comprises sensing motor vibration.
45. The method of claim 30 , wherein sensing non-motor winding data information comprises sensing ambient sound.
46. The method of claim 30 , wherein sensing non-motor winding data information comprises sensing ambient temperature.