SYSTEM AND METHOD FOR DATA ENTITY IDENTIFICATION AND ANALYSIS OF MAINTENANCE DATA

• US 20140277921A1
• Filed: 03/14/2013
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Gujjar, Vineel Chandrakanth, Bal, Debasis, Subramanian, Gopi, Larder, Brian David, Smith, Andrew James, Harrington, Mark Thomas
• Application Number:
  US13/829,619
• Publication Number:
  US 20140277921A1
• Time in Patent Office: Days
• US Class Current: 701/32.9
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• 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: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06Q 10/20 Administration of product repair or maintenance

Abstract

A method for identifying and analyzing data entities from (maintenance, repair, and overhaul (MRO)) data is provided. The method includes obtaining MRO data comprising unstructured text information. The method also includes performing named entity recognition on the MRO data to extract entities from the unstructured text information and label the entities with a tag. The method further includes analyzing the labeled entities via a heuristic to estimate an effectiveness of a fix for a specific issue or to estimate a reliability of a component.

Claims

1. A method for identifying and analyzing data entities from (maintenance, repair, and overhaul (MRO)) data comprising:

obtaining MRO data comprising unstructured text information;

performing named entity recognition on the MRO data to extract entities from the unstructured text information and label the entities with a tag; and

analyzing the labeled entities via a heuristic to estimate an effectiveness of a fix for a specific issue or to estimate a reliability of a component.

2. The method of claim 1, wherein the tag indicates if the entity is a part, an issue, or a corrective-action.

3. The method of claim 1, comprising correcting spelling errors within the MRO data using a spell correction model prior to performing the named entity recognition.

4. The method of claim 3, comprising generating the spell correction model by training a machine learning algorithm using MRO data different from the obtained MRO data.

5. The method of claim 4, wherein the spell correction model comprises a decision tree.

6. The method of claim 1, comprising normalizing synonymous terms within the MRO data using a synonym identification model prior to performing the named entity recognition.

7. The method of claim 6, comprising generating the synonym identification model by constructing a context-based thesaurus.

8. The method of claim 1, wherein performing named entity recognition is performed using a hidden Markov model.

9. The method of claim 8, comprising generating the hidden Markov model prior to performing the named entity recognition by training the hidden Markov model on manually labeled MRO data different from the obtained MRO data, wherein labels of the manually labeled MRO data indicate parts, issues, or corrective-actions.

10. The method of claim 1, comprising generating and displaying a fix effectiveness chart based on the analyzed entities wherein the fix effectiveness chart illustrates a symptom that includes a specific part and corresponding issue, co-operating parts that have received fixes or corrective-actions, and an indicator of the effectiveness of the fixes or corrective actions on the co-operating parts.

11. The method of claim 1, comprising generating and displaying a component reliability chart based on the analyzed entities.

12. The method of claim 1, comprising clustering the analyzed entities into symptom clusters using a clustering algorithm and displaying the symptom clusters in relation to each other, wherein each symptom cluster groups specific parts and corresponding issues for the specific parts under a common symptom.

13. A system for identifying and analyzing data entities from (maintenance, repair, and overhaul (MRO)) data comprising:

a memory structure encoding one or more processor-executable routines, wherein the routines, when executed, cause acts to be performed comprising;

performing named entity recognition on MRO data to extract entities and to label the entities with a tag, wherein the MRO data comprises unstructured text information, and the tag indicates if the entity is a part, an issue, or a corrective-action; and

analyzing the labeled entities via a heuristic to estimate an effectiveness of a fix for a specific issue or to estimate a reliability of a component; and

a processing component configured to access and execute the one or more routines encoded by the memory structure.

14. The system of claim 13, wherein performing named entity recognition is performed using a hidden Markov model.

15. The system of claim 14, wherein the routines, when executed by the processing component, cause further acts to be performed comprising:

generating the hidden Markov model prior to performing the named entity recognition by training the hidden Markov model on manually labeled MRO training data, wherein labels of the manually labeled MRO training data indicate parts, issues, or corrective-actions.

16. The system of claim 13, wherein the routines, when executed by the processing component, cause further acts to be performed comprising:

generating a fix effectiveness chart for display based on the analyzed entities, wherein the fix effectiveness chart illustrates a symptom that includes a specific part and corresponding issue, co-operating parts that have received fixes or corrective-actions, and an indicator of the effectiveness of the fixes or corrective actions on the co-operating parts.

17. The system of claim 13, wherein the routines, when executed by the processing component, cause further acts to be performed comprising:

generating a component reliability chart for display based on the analyzed entities.

18. The system of claim 13, wherein the routines, when executed by the processing component, cause further acts to be performed comprising:

clustering the analyzed entities into symptom clusters using a clustering algorithm and displaying the symptom clusters in relation to each other, wherein each symptom cluster groups specific parts and corresponding issues for the specific parts under a common symptom.

19. The system of claim 13, wherein the routines, when executed by the processing component, cause further acts to be performed comprising:

correcting spelling errors within the MRO data using a spell correction model prior to performing the named entity recognition; and

normalizing synonymous terms within the spell corrected MRO data using a synonym identification model prior to performing the named entity recognition.

20. One or more non-transitory computer-readable media encoding one or more processor-executable routines, wherein the one or more routines, when executed by a processor, cause acts to be performed comprising:

performing named entity recognition on (maintenance, repair, and overhaul (MRO)) data to extract entities and to label the entities with a tag, wherein the MRO data comprises unstructured text information, and the tag indicates if the entity is a part, an issue, or a corrective-action; and

analyzing the labeled entities via a heuristic to estimate an effectiveness of a fix for a specific issue or to estimate a reliability of a component.

21. The one or more non-transitory computer-readable media of claim 20, wherein performing named entity recognition is performed using a hidden Markov model.

22. The one or more non-transitory computer-readable media of claim 21, wherein the one or more-routines, when executed by the processor, cause further acts to be performed comprising:

generating the hidden Markov model prior to performing the named entity recognition by training the hidden Markov model on manually labeled MRO data different from the obtained MRO data, wherein labels of the manually labeled MRO data indicate parts, issues, or corrective-actions.

23. The one or more non-transitory computer-readable media of claim 20, wherein the one or more-routines, when executed by the processor, cause further acts to be performed comprising:

generating a fix effectiveness chart for display based on the analyzed entities wherein the fix effectiveness chart illustrates a symptom that includes a specific part and corresponding issue, co-operating parts that have received fixes or corrective-actions, and an indicator of the effectiveness of the fixes or corrective actions on the co-operating parts.

24. The one or more non-transitory computer-readable media of claim 20, wherein the one or more-routines, when executed by the processor, cause further acts to be performed comprising:

generating a component reliability chart for display based on the analyzed entities.

25. The one or more non-transitory computer-readable media of claim 20, wherein the one or more-routines, when executed by the processor, cause further acts to be performed comprising:

clustering the analyzed entities into symptom clusters using a clustering algorithm and displaying the symptom clusters in relation to each other, wherein each symptom cluster groups specific parts and corresponding issues for the specific parts under a common symptom.