Integrated control and diagnostics system

• US 6,646,397 B1
• Filed: 07/23/2001
• Issued: 11/11/2003
• Est. Priority Date: 12/15/1999
• Status: Expired due to Term

• Current Assignee: Reliance Electric Company (ABB Limited), Reliance Electric Technologies LLC (ABB Limited)
• Original Assignee: Rockwell Automation Technologies Incorporated (Rockwell Automation, Inc.)
• Inventors: Discenzo, Frederick M.
• Primary Examiner(s): Nappi, Robert E.
• Assistant Examiner(s): Smith, Tyrone W
• Application Number:
  US09/911,130
• Time in Patent Office: 841 Days
• Field of Search: 318/138, 318/139, 318/254, 318/432, 318/434, 318/609, 318/811, 701/29, 701/34, 701/35, 340/825.06, 340/825.16, 361/23, 388/906, 388/909
• US Class Current: 318/400.07
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model
• 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: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: Y10S 388/906 Proportional-integral system
• CPC Class Codes: Y10S 388/909 Monitoring means

Abstract

An integrated control and diagnostics system for a controlled system (e.g., a motor) includes a diagnostics module and a controller coupled to the motor. To optimize operation, the diagnostics information signal is used to modify the control provided by the controller as required. Moreover, the output of the control module is coupled to the diagnostics module so that the health assessment made by the diagnostics module can be based at least in part on the output of the controller. The invention uses a model-based diagnostics approach that allows integration of control algorithms with diagnostics algorithms to intelligently trade off optimizing performance to avert or accommodate failures, and to meet performance requirements in a wide range of applications.

Claims

1. An integrated control and diagnostics system for a motor, the system comprising:

a diagnostics module to generate a health assessment signal indicative of the health of the motor, wherein the diagnostics module employs a control algorithm based at least in part on systematic use of small controls or low-gain design techniques that exploit inherent dominant nonlinearities of a dynamic system; and

a controller coupled to the motor, said controller outputting a driving output based on said health assessment signal, wherein said driving output is applied to the motor.

2. The control and diagnostics system according to claim 1, wherein said diagnostics module generates said health assessment signal based on said driving output.

3. The control and diagnostics system according to claim 1, wherein said controller is associated with at least one controllable parameter, said parameter being controllable in response to said health assessment signal.

4. The control and diagnostics system according to claim 3, wherein said controller includes an inverter.

5. The control and diagnostics system according to claim 1, further including at least one sensor, said sensor generating a signal indicative of a parameter associated with the motor, wherein the health assessment signal is based on the sensor signal.

6. The control and diagnostics system according to claim 5, wherein said controller includes a velocity feedback loop and a torque feedback loop.

7. The control and diagnostics system according to claim 6, wherein said velocity feedback loop generates a current reference signal in response to the sensor signal, and said torque feedback loop generates the driving output in response to the current reference signal.

8. The control and diagnostics system according to claim 7, wherein said velocity feedback loop includes a P-I controller to generate the current reference signal.

9. The control and diagnostics system according to claim 5, wherein said motor parameter is one of a group consisting of velocity and vibration.

10. The control and diagnostics system according to claim 1, further including an enhancement module that generates an evolving set of design rules based on a plurality of health assessment signals so as to facilitate designing an improved version of the system.

11. The control and diagnostics system according to claim 10, wherein each health assessment signal is generated in response to at least one signal generated by a sensor that monitors at least one parameter associated with the motor.

12. The control and diagnostics system according to claim 10, further including a model embedded in a memory of the enhancement module to generate the evolving set of design rules.

13. The control and diagnostics system according to claim 1, wherein said diagnostics module includes an ASIC that generates the health assessment signal based on a process constraint.

14. The control and diagnostics system according to claim 2, wherein said health assessment signal is indicative of whether the motor is deviating from a normal operating characteristic.

15. The control and diagnostics systems according to claim 1, further comprising a coordination module coupled to a plurality of the control and diagnostics systems, wherein said coordination module alters the driving output associated with one of the control and diagnostics systems based on the driving output of another one of the control and diagnostics systems.

16. The system of claim 1, the system being stimulated via actuation of the control and thereafter system response observed in connection with diagnostics optimization.

17. The system of claim 1, the diagnostics module being modular.

18. The system of claim 1 integrating at least one of:

Discrete Fourier Transform (DFT), Gabor Transform, Wigner-Ville distribution;

with at least one of;

wavelet decomposition, non-linear filter-based statistical techniques, and analysis of time series data using non-linear signal processing tools such as Poincare'"'"' maps or Lyapunov spectrum.

19. The system of claim 1 employing chaos-tracking techniques.

20. The system of claim 19 further employing chaos tracking templates for respective detection and diagnostic scenarios.

21. The system of claim 19 employing pattern matching.