Rotating equipment diagnostic system and adaptive controller

• US 20020013664A1
• Filed: 05/29/2001
• Published: 01/31/2002
• Est. Priority Date: 06/19/2000
• Status: Abandoned Application

• Current Assignee: Andreas Schubert, Dietrich Behr, Jens Strackeljan, Werner Wendt
• Original Assignee: Andreas Schubert, Dietrich Behr, Jens Strackeljan, Werner Wendt
• Inventors: Schubert, Andreas, Strackeljan, Jens, Wendt, Werner, Behr, Dietrich
• Application Number:
  US09/867,085
• Publication Number:
  US 20020013664A1
• Time in Patent Office: Days
• US Class Current: 702/34
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks

Abstract

A system and method for control and monitoring of rotating equipment through the use of machine status classification where, in one embodiment, adaptive control measures responsive to the machine status are implemented. The invention provides a computer-implemented method for monitoring a mechanical component using either a neural network or weighted distance classifier. The method references a predetermined set of candidate data features for a sensor measuring an operational attribute of the component and derives a subset of those features which are then used in real-time to determine class affiliation parameter values. The classification database is updated when an anomalous measurement is encountered, even as monitoring of the mechanical component continues in real-time. The invention also provides a dimensionless peak amplitude data feature and a dimensionless peak separation data feature for use in classifying. An organized datalogical toolbox for operational component status classification is also described.

Claims

1. A computer-implemented monitoring system, comprising:

a toolbox of machine analysis data feature tools, each data feature tool having a predetermined set of candidate data features for a type of sensor and related machine component in a unified mechanical component assembly;

means for designating one said data feature tool for classifying use respective to at least one defined class;

means for measuring an input signal from said sensor;

means for collecting a plurality of said measured input signals as a measured input signal set;

means for obtaining a human-determined class affiliation parameter value for each measured input signal in said measured input signal set;

means for calculating a feature value set respective to each measured input signal and respective to at least one data feature from said set of candidate data features;

means for deriving a classifier reference parameters instance from the feature value set and associated human-determined class affiliation parameter values respective to said measured input signal set and from a plurality of said candidate data features;

a classifier for defining a computer-determined class affiliation parameter value for a measured input signal respective to each class defined, said classifier in data communication with said classifier reference parameters instance to define each computer-determined class affiliation parameter value;

means for selecting a subset of data features from said candidate data features, said means for selecting in data communication with said measured input signal set, said associated human-determined class affiliation parameter values, said means for deriving a classifier reference parameter instance, and said classifier;

means for retaining the classifier reference parameters instance respective to said selected subset of features as a real-time reference parameter set;

means for graphically displaying at least one computer-determined class affiliation parameter value respective to an input signal measured in real-time from said assembly and respective to said real-time reference parameter set; and

a real-time executive means for directing the operation of said means for measuring input signals, said means for calculating a feature value set, said classifier, and said means for graphically displaying so that a graphical display of at least one computer-determined class affiliation parameter value is implemented in real-time respective to an input signal measured in real-time from said assembly.

3. The monitoring system of claim 1 wherein said classifier is a weighted-distance classifier, said means for selecting implements progressive extraction of data feature subsets and tests each subset through use of said weighted-distance classifier to define a performance measure for that subset, and said monitoring system further comprises a stack database for holding a predetermined plurality of data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

4. The monitoring system of claim 1, further comprising:

neural network training logic in said means for deriving for deriving a neural network parameters instance as said classifier reference parameters instance;

a neural network classifier as said classifier, said neural network classifier in data communication with said neural network parameters instance; and

a stack database;

wherein said means for selecting implements progressive extraction of data feature subsets and tests each subset through use of said neural network classifier to define a performance measure for that subset, and said stack database holds a predetermined plurality of the data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

5. The monitoring system of claim 1, further comprising:

neural network training logic in said means for deriving for deriving a neural network parameters instance as said classifier reference parameter instance;

a neural network classifier as said classifier, said neural network classifier in data communication with said neural network parameters instance; and

a stack database;

wherein said means for selecting randomly identifies data features for a plurality of data feature subsets and tests each subset through use of said neural network classifier to define a performance measure for that subset, and said stack database holds a predetermined plurality of the data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

6. The monitoring system of claim 1, wherein:

said means for deriving derives a weighted-distance classifier reference parameters instance;

said means for deriving further comprises neural network training logic for deriving a neural network parameters instance;

said classifier comprises a weighted-distance classifier in data communication with said weighted-distance classifier reference parameters instance, and said classifier further comprises a neural network classifier in data communication with said neural network parameters instance;

said means for selecting implements progressive extraction of data feature subsets wherein each subset is tested through use of said weighted-distance classifier to define a performance measure for that subset;

said means for selecting randomly identifies data features for a plurality of data feature subsets and tests each subset through use of said neural network classifier to define a performance measure for that subset;

said monitoring system further comprises a stack database for holding a predetermined plurality of the data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested;

said monitoring system further comprises means for retaining the neural network parameters instance respective to said selected subset of features as a real-time neural network reference parameter set;

said monitoring system further comprises means for retaining the weighted-distance classifier reference parameters instance respective to said selected subset of features as a real-time weighted-distance reference parameter set;

said monitoring system further comprises means for specifying use of either of said weighted-distance classifier and said neural network classifier; and

said means for graphically displaying displays at least one computer-determined class affiliation parameter respective to either of the real-time neural reference network parameter set and real-time weighted-distance reference parameter set, respective to the specified classifier.

7. The monitoring system of claim 1, further comprising:

means for determining class affiliation parameter values of any said input signal respective to said candidate data features through use, in the alternative, of either of a weighted-distance classifier and a neural network classifier, said weighted-distance classifier selected for use when said predetermined set of candidate data features contains a plurality of data features numbering less than a predetermined threshold value, and said neural network classifier selected for use when said predetermined set of candidate data features contains a plurality of data features numbering not less than said predetermined threshold value.

9. The system of any of claims 1, 2, and 8, further comprising:

output means for transmitting command signals which include at least one manipulated parameter variable that is used to govern said assembly; and

means for deriving said manipulated parameter variable from said computer-determined class affiliation parameter value;

wherein said real-time executive means directs the operation of said means for deriving said manipulated parameter variable so that said monitoring system is a process control system implementing control of said assembly in real-time.

10. The system of any of claims 1, 2, and 8 wherein said means for measuring further comprises a multiple-stage band-pass galvanic-isolation filter circuit.

2. A computer-implemented monitoring system, comprising:

a toolbox of machine analysis data feature tools, each data feature tool having a predetermined set of candidate data features for a type of sensor and related machine component in a unified mechanical component assembly;

means for designating one said data feature tool for classifying use respective to at least one defined class and a particular sensor;

means for measuring an input signal from said sensor;

means for determining at least-one computer-determined class affiliation parameter value for any said input signal respective to said candidate data features;

means for graphically displaying said class affiliation parameter value respective to said input signal when measured in real-time from said assembly; and

a real-time executive means for directing the operation of said means for measuring, said means for determining, and said means for graphically displaying so that a graphical display of at least one computer-determined class affiliation parameter value is implemented in real-time respective to an input signal measured in real-time from said assembly.

8. A computer-implemented monitoring system for monitoring a sensor and related machine component in a mechanical component assembly, comprising:

a predetermined set of candidate data features for classifying said sensor respective to at least two defined classes;

means for real-time measurement of an input signal from said sensor;

means for determining a first computer-determined class affiliation parameter value for said input signal from said candidate data feature set in reference to a first classifying parameter set respective to a first class, and a second computer-determined class affiliation parameter value for said input signal from said candidate data feature set in reference to a second classifying parameter set respective to a second class;

means for deriving, during real-time measurement and class affiliation parameter value determination, a third classifying parameter set for said input signal respective to said first class and a fourth classifying parameter set for said input signal respective to said second class when all computer-determined class affiliation parameter values respective to an input signal measurement in real-time have a quantity less than a predetermined threshold value, said third and fourth classifying parameter sets incorporating the influence of said input signal measurement; and

means for replacing said first and second classifying parameter sets respectively with said third and fourth classifying parameter sets so that said third and fourth classifying parameter sets respectively become new said first and second classifying parameter sets when said third and fourth classifying parameter sets have been derived.

11. A computer-implemented system for classifying a type of sensor and related machine component in a unified mechanical component assembly, comprising:

means for deriving a dimensionless peak amplitude data feature;

means for measuring an input signal from said sensor;

means for obtaining a class affiliation parameter value for said measured input signal respective to said dimensionless peak amplitude feature.

12. A computer-implemented system for classifying a type of sensor and related machine component in a unified mechanical component assembly, comprising:

means for deriving a dimensionless peak separation feature;

means for measuring an input signal from said sensor;

means for obtaining a class affiliation parameter value for said measured input signal respective to said dimensionless peak separation feature.

13. A computer-implemented method, comprising the steps of:

providing a toolbox of machine analysis data feature tools, each data feature tool having a predetermined set of candidate data features for a type of sensor and related machine component in a unified mechanical component assembly;

designating one said data feature tool for classifying use respective to at least one defined class;

measuring an input signal from said sensor;

collecting a plurality of said measured input signals as a measured input signal set;

obtaining a human-determined class affiliation parameter value for each measured input signal in said measured input signal set;

calculating a feature value set respective to each measured input signal and respective to at least one data feature from said set of candidate data features;

deriving a classifier reference parameters instance from the feature value set and associated human-determined class affiliation parameter values respective to said measured input signal set and from a plurality of said candidate data features;

using a classifier in defining a computer-determined class affiliation parameter value from said classifier reference parameters instance for a measured input signal respective to each class defined;

selecting a subset of data features from said candidate data features, said measured input signal set, said associated human-determined class affiliation parameter values, a plurality of said derived classifier reference parameter instances, and said classifier by evaluating a plurality of data feature combinations until acceptable classification is achieved;

retaining the classifier reference parameters instance respective to said selected subset of features as a real-time reference parameter set;

classifying in real-time said measured input signal from said real-time reference parameter set to establish a real-time computer-determined class affiliation parameter value; and

graphically displaying in real-time said real-time computer-determined class affiliation parameter value so that a graphical display of at least one computer-determined class affiliation parameter value is implemented in real-time respective to an input signal measured in real-time from said assembly.

15. The method of claim 13 wherein said classifier is a weighted-distance classifier, said selecting step progressively extracts data feature subsets and tests each subset using said weighted-distance classifier to define a performance measure for that subset, and said method further comprises the step of:

holding, in a stack database, a predetermined plurality of data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

16. The method of claim 13 wherein a neural network is said classifier, a neural network parameters instance is derived in said deriving step as said classifier reference parameter instance, and, in said selecting step, progressively extracted data feature subsets are each tested using said neural network to define a performance measure for that subset, and said method further comprises the step of:

holding, in a stack database, a predetermined plurality of data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

17. The method of claim 13 wherein a neural network is said classifier, a neural network parameters instance is derived in said deriving step as said classifier reference parameter instance, and, in said selecting step, randomly identified data feature subsets are each tested using said neural network to define a performance measure for that subset, and said method further comprises the step of:

holding, in a stack database, a predetermined plurality of data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested.

18. The monitoring method of claim 13, wherein said classifier comprises both a weighted-distance classifier and a neural network classifier, said selecting step progressively extracts data feature subsets and tests each subset through use of said weighted-distance classifier when specified in defining a performance measure for that subset, said selecting step randomly identifies data features for a plurality of data feature subsets and tests each subset using said neural network classifier when specified in defining a performance measure for that subset, and said method further comprises the steps of:

specifying use of either of said weighted-distance classifier and said neural network classifier; and

holding, in a stack database, a predetermined plurality of the data feature subsets demonstrating the most favorable performance measures among all data feature subsets tested;

wherein

said deriving step derives a weighted-distance classifier reference parameters instance using said weighted-distance classifier when specified;

said deriving step derives a neural network parameters instance using said neural network classifier when specified;

said retaining step retains the neural network parameters instance respective to said selected subset of features as a real-time neural network reference parameter set when said neural network classifier is specified; and

said retaining step retains the weighted-distance classifier reference parameters instance respective to said selected subset of features as a real-time weighted-distance reference parameter set when said weighted-distance classifier is specified.

19. The method of claim 13, further comprising the step specifying for use, in the alternative, of either of a weighted-distance classifier and a neural network classifier, said weighted-distance classifier specified for use when said predetermined set of candidate data features contains a plurality of data features numbering less than a predetermined threshold value and said neural network classifier specified for use when said predetermined set of candidate data features contains a plurality of data features numbering not less than said predetermined threshold value.

21. The monitoring system of any of claims 13, 14, and 20, further comprising the steps of:

deriving a manipulated parameter variable from said computer-determined class affiliation parameter value; and

governing said assembly with said manipulated parameter variable;

so that said assembly is controlled in real-time.

14. A computer-implemented method, comprising the steps of:

providing a toolbox of machine analysis data feature tools, each data feature tool having a predetermined set of candidate data features for a type of sensor and related machine component in a unified mechanical component assembly;

designating one said data feature tool for classifying use respective to at least one defined class and a particular sensor;

measuring an input signal from said sensor;

determining at least-one computer-determined class affiliation parameter value for any said input signal respective to said candidate data features;

graphically displaying said class affiliation parameter value respective to said input signal when measured in real-time from said assembly; and

directing the operation of said steps of measuring, determining, and graphically displaying so that a graphical display of at least one computer-determined class affiliation parameter value is implemented in real-time respective to an input signal measured in real-time from said assembly.

20. A computer-implemented method for monitoring a sensor and related machine component in a mechanical component assembly, comprising the steps of:

providing a predetermined set of candidate data features for classifying said sensor respective to at least two defined classes;

measuring in real-time an input signal from said sensor;

determining a first computer-determined class affiliation parameter value for said input signal from said candidate data feature set in reference to a first classifying parameter set respective to a first class;

determining a second computer-determined class affiliation parameter value for said input signal from said candidate data feature set in reference to a second classifying parameter set respective to a second class;

deriving, during said real-time measuring and determining steps, a third classifying parameter set for said input signal respective to said first class and a fourth classifying parameter set for said input signal respective to said second class when all computer-determined class affiliation parameter values respective to an input signal measurement in real-time have a quantity less than a predetermined threshold value, said third and fourth classifying parameter sets incorporating the influence of said input signal measurement; and

replacing said first and second classifying parameter sets respectively with said third and fourth classifying parameter sets so that said third and fourth classifying parameter sets respectively become new said first and second classifying parameter sets when said third and fourth classifying parameter sets have been derived.

22. A computer-implemented method for classifying a type of sensor and related machine component in a unified mechanical component assembly, comprising:

deriving a dimensionless peak amplitude data feature;

measuring an input signal from said sensor;

obtaining a class affiliation parameter value for said measured input signal respective to said dimensionless peak amplitude feature.

23. A computer-implemented method for classifying a type of sensor and related machine component in a unified mechanical component assembly, comprising:

deriving a dimensionless peak separation feature;

measuring an input signal from said sensor;

obtaining a class affiliation parameter value for said measured input signal respective to said dimensionless peak separation feature.

24. A computer-implemented method for classifying a type of sensor and related machine component in a unified mechanical component assembly, comprising the steps of:

defining a feature set for classification from a set of candidate features and a learning database using evolutionary selection, said learning database having a set of evaluated instances, said evolutionary selection having the sequential operations of;

defining a population size for a population of feature combination instances;

defining a set of evaluation features for said population from said set of candidate features;

defining an evaluation feature set size;

randomly selecting, from said candidate features, a population instance of feature set instances of said evaluation feature set size, said population instance having said population size;

training a classifier according to said population instance and said learning database;

evaluating the prediction capability of each feature set instance using said trained classifier;

designating said feature set instance as a real-time classification feature set if said evaluating fulfills a criteria;

selecting, if said criteria is unfulfilled, a subset group of said feature set instances according to said evaluated prediction capabilities;

generating a child subset group of said feature set instances by randomly selecting one of said features from each of two randomly chosen feature set instances and combining each of said selected features into a new feature set instance;

mutating said new feature set instance by randomly selecting one of said features in said new feature set instance and replacing said selected feature with a randomly selected feature from said set of evaluation features for said population with the proviso that said replacement feature is other than either of said features in said new feature set instance prior to initiation of said mutating operation;

defining a new population instance from said subset group and at least one said mutated feature set instance with the proviso that said mutating operating is executed until said new population instance achieves said population size; and

returning to said training operation;

acquiring a set of features in real-time from said sensor; and

classifying said acquired set of features by using said real-time classification feature set.