Analyzer for modeling and optimizing maintenance operations

• US 6,246,972 B1
• Filed: 05/27/1999
• Issued: 06/12/2001
• Est. Priority Date: 08/23/1996
• Status: Expired due to Term

• Current Assignee: Aspentech Corporation
• Original Assignee: Aspen Technology (Telefonaktiebolaget LM Ericsson)
• Inventors: Klimasauskas, Casimir C.
• Primary Examiner(s): Teska, Kevin J.
• Assistant Examiner(s): FREJD, RUSSELL WARREN
• Application Number:
  US09/321,145
• Time in Patent Office: 747 Days
• Field of Search: 703/2, 700/28, 700/29, 700/30, 700/31, 700/48, 706/16, 706/45, 706/23, 376/215, 717/1, 702/123, 704/244, 714/799
• US Class Current: 703/2
• CPC Class Codes: G05B 13/0275 using fuzzy logic only

Abstract

A first model or first analyzer having a series of filters is provided to represent time-varying effects of maintenance events. The first model or analyzer further enhances the selection of derived variables which are used as inputs to the first analyzer. Additionally, a combination of fuzzy logic and statistical regression analyzers are provided to better model the equipment and the maintenance process. An optimizer with a bi-modal optimization process which integrates discrete maintenance events with continuous process variables is also provided. The optimizer determines the time and the type of maintenance activities which are to be executed, as well as the extent to which the maintenance activities can be postponed by changing other process variables. Thus, potential modifications to process variables are determined to improve the current performance of the processing equipment as it drifts out of tolerance.

Claims

1. A method for modeling a task having input parameters and one or more activities, said method comprising the steps of:

collecting said input parameters;

generating derived variables, including the steps of;

performing a search on said input parameters using one of more functional forms;

correlating said functional forms to the results of the performed search; and

ranking said functional forms based on the results of the step of correlating; and

applying a primary analyzer to said input parameters and said derived variables to generate outputs corresponding to said activities.

2. The method of claim 1, wherein said collecting step further comprises the steps of receiving and modifying time-varying input parameters.

3. The method of claim 2, wherein said modifying step filters said time-varying input parameters.

4. The method of claim 2, wherein said collecting step collects operating periods between said activities.

5. The method of claim 1, wherein said collecting step includes transforming the input parameters into representations of events occurring in the task.

6. The method of claim 1, wherein said collecting step filters said input parameters.

7. The method of claim 1, wherein said correlating step comprises the step of applying a uniformly distributed sub-set of examples of said input parameters.

8. The method of claim 1, wherein said applying step applies a partial least squares analyzer having a linear inner model.

9. The method of claim 1, wherein said applying step applies a partial least squares analyzer having a fuzzy single-input-single-output (FSISO) model.

10. The method of claim 1, wherein said applying step applies a partial least squares analyzer having a kernel density estimator.

11. The method of claim 1, wherein said applying step applies a partial least squares analyzer having a generalized regression neural network.

12. The method of claim 1, further comprising the step of optimizing said analyzer.

13. The method of claim 12, wherein said input parameters include continuous variables and wherein said optimizing step further comprises the step of integrating discrete events representative of said activities with said continuous variables.

14. The method of claim 12, wherein said optimizing step further comprises the steps of:

generating all combinations of said discrete activities into enumerated sets; and

deriving a common objective function for each of said enumerated sets, including the steps of;

evaluating the cost of said set of discrete activities;

optimizing said continuous activities based on said cost; and

merging the results of said evaluating step and said continuous activities optimizing step into said common objective function.