System and method for predicting device deterioration

• US 20070088570A1
• Filed: 02/28/2006
• Published: 04/19/2007
• Est. Priority Date: 10/18/2005
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Shetty, Pradeep, Ekambaram, Thirumaran, Mylaraswamy, Dinkar
• Application Number:
  US11/363,621
• Publication Number:
  US 20070088570A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: F05B 2260/80 Diagnostics
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0289 Reconfiguration to prevent failure, e.g. usually as a reaction to incipient failure detection
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change

Abstract

A system and method is provided for predicting deterioration in a mechanical device. The deterioration prediction system and method includes a dynamic model of the mechanical device and a state estimator to predict deterioration in a mechanical device. The dynamic model includes a plurality of evolving health states that describe the performance of the mechanical device. The dynamic model can be implemented such that several distinct factors contribute the evolution of the health states. These factors can include damage accumulation, interaction between components in the device, deviation from design conditions, and the influence of discrete events. In one embodiment, the dynamic model uses a Poisson distribution to model the rate of damage accumulation in the mechanical device.

Claims

1. A deterioration prediction system for predicting deterioration in an mechanical device, the system comprising:

a health model for the mechanical device, the health model including a plurality of health states adapted for modeling the mechanical device, wherein the health model uses a Poisson distribution to model a rate of intrinsic damage accumulation in the mechanical device; and

a state estimator, the state estimator adapted to receive periodic device data for the mechanical device, the state estimator further adapted to estimate values for the plurality of health states based on the device data, wherein the estimated values for the plurality of health states provides a prediction of deterioration in the mechanical device.

2. The system of claim 1 wherein the health model is implemented to capture effects of damage accumulation and influence of discrete events on the mechanical device as additive variables.

3. The system of claim 2 wherein the influence of discrete events is captured by a combination of a sensitivity matrix and a binary variable that indicates when a discrete event has occurred.

4. The system of claim 1 wherein health model includes a system matrix to describe interactions between subsystems and wherein the system matrix is determined using maximum likelihood estimation.

5. The system of claim 1 wherein health model includes a system matrix to describe interactions between subsystems and wherein the system matrix is determined using least squares regression.

6. The system of claim 1 wherein the health model includes health states that evolve in linear state space described by the damage accumulation element, deviation from design conditions at each operating mode, and occurrence of discrete events.

7. The system of claim 1 wherein the state estimator implements a Kalman filter, wherein the Kalman filter is adapted to calculate the mean and the variance of health states within the health model.

8. The system of claim 1 wherein the state estimator is derived by partially specifying the distribution of the damage accumulation term in terms of two moments.

9. The system of claim 1 wherein the state estimator estimates the mean and the variance of the health states within the health model using periodic observations from the mechanical device.

10. The system of claim 9 wherein the periodic observations from the mechanical device include sensor measurements superimposed by Gaussian noise.

11. The system of claim 1 wherein the state estimator estimates future states of the health model by integrating the health model to a select future time.

12. The system of claim 1 wherein the state estimator includes a Kalman filter, wherein the Kalman filter is implemented to estimate future states of the health model based on the Poisson distribution of damage accumulation in the mechanical device.

13. A method of predicting deterioration in a mechanical device, the method comprising the steps of:

a) providing a health model for the mechanical device, the health model including a plurality of health states for modeling the mechanical device, wherein the health model uses a Poisson distribution to model damage accumulation in the mechanical device;

b) receiving device data for the mechanical device;

c) estimating values for the plurality of health states based on the device data; and

d) generating a prediction of deterioration in the mechanical device from the estimated values for the plurality of health states.

14. The method of claim 13 wherein the health model is implemented to capture effects of damage accumulation and interaction between discrete events on the mechanical device.

15. The method of claim 13 wherein the health model includes a first system matrix that defines how a current state in model depends on a previous state, a second system matrix that defines sensitivity of the states to deviation in health states, and a third system matrix that defines sensitivity of the health vectors to discrete events.

16. The method of claim 13 wherein the step of estimating values for the plurality of health states based on the device data comprises estimating future states of the health model by integrating the health model to a select future time.

17. The method of claim 13 wherein the step of estimating values for the plurality of health states based on the device data comprises using a Kalman filter to estimate future states of the health model based on the Poisson distribution of damage accumulation in the mechanical device.

18. A program product comprising:

a) a deterioration prediction program for predicting deterioration in a mechanical device, the program including;

a health model for the mechanical device, the health model including a plurality of health states adapted for modeling the mechanical device, the health model using a Poisson distribution to model damage accumulation in the mechanical device; and

a state estimator, the state estimator adapted to receive periodic device data for the mechanical device, the state estimator further adapted to estimate values for the plurality of health states based on the device data, wherein the estimated values for the plurality of health states provides a prediction of deterioration in the mechanical device; and

b) computer-readable signal bearing media bearing said program.

19. The program product of claim 18 wherein the state estimator includes a Kalman filter, wherein the Kalman filter is implemented to estimate future states of the health model based on the Poisson distribution of damage accumulation in the mechanical device.

20. The program product of claim 18 wherein the health model includes health states that evolve in linear state space described by the damage accumulation element, deviation from design conditions at each operating mode, and occurrence of discrete events.