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 Title: |
US6442542:
Diagnostic system with learning capabilities
[ Derwent Title ]
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| Country: |
US United States of America
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| Inventor: |
Ramani, Vipin Kewal; Niskayuna, NY
Shah, Rasiklal Punjalal; Latham, NY
Ramachandran, Ramesh; Overland Park, KS
Bonissone, Piero Patrone; Schenectady, NY
Chen, Yu-To; Niskayuna, NY
Steen, Phillip Edward; Delafield, WI
Johnson, John Andrew; Delafield, WI
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| Assignee: |
General Electric Company, Niskayuna, NY
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| Published / Filed: |
2002-08-27
/ 1999-10-08
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| Application Number: |
US1999000415408
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| IPC Code: |
Advanced:
A61B 5/00;
G01R 31/28;
G06F 11/22;
G06F 11/25;
G06N 3/00;
Core:
more...
IPC-7:
G06F 17/30;
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| ECLA Code: |
G06F11/25E; G01R31/28F4F;
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| U.S. Class: |
707/003;
707/104.1;
706/047;
714/026;
714/025;
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| Field of Search: |
707/001,5,6,104,3,4
702/035
706/014,16,25,47
714/025-26,37
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| Priority Number: |
| 1999-10-08 |
US1999000415408 |
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| Abstract: |
A diagnostic system is provided for identifying faults in a machine (e.g., CT scanner, MRI system, x-ray apparatus) by analyzing a data file generated thereby. The diagnostic system includes a trained database containing a plurality of trained data, each trained data associated with one of plurality of known fault types. Each trained data is represented by a trained set of feature values and corresponding weight values. Once a data file is generated by the machine, a current set of feature values are extracted from the data file by performing various analyses (e.g., time domain analysis, frequency domain analysis, wavelet analysis). The current set of feature values extracted is analyzed by a fault detector which produces a candidate set of faults based on the trained set of feature values and corresponding weight values for each of the fault types. The candidate set of faults produced by the fault detector is presented to a user along with a recommend repair procedure. In cases where no fault is identified or in response to a misdiagnosis produced by the diagnostic system, the user may interactively input a faulty condition associated with the machine being diagnosed (e.g., based on his/her experience). The diagnostic system further includes a learning subsystem which automatically updates the plurality of trained data based on the faulty condition input by the user.
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| Attorney, Agent or Firm: |
Goldman, David C. ;
Breedlove, Jill M. ;
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| Primary / Asst. Examiners: |
Alam, Hosain T.;
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| Maintenance Status: |
CC Certificate of Correction issued
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| INPADOC Legal Status: |
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Family Legal Status Report
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| Designated Country: |
DE FR
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| Family: |
Show 3 known family members
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First Claim:
Show all 18 claims |
What is claimed is:
1. A system for diagnosing a machine by analyzing a data file generated by the machine, comprising:
- a trained database which contains a plurality of trained data associated with a plurality of fault types;
- a feature extractor which extracts a plurality of feature values from the data file;
- a fault detector which receives said plurality of feature values extracted and produces a candidate set of faults based on said plurality of trained data;
- a user interface which presents said candidate set of faults produced by said fault detector to a user and allows said user to interactively input a faulty condition associated with the machine; and
- a learning subsystem which updates said plurality of trained data based on said faulty condition input by said user.
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| Background / Summary: |
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| Drawing Descriptions: |
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| Description: |
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| Forward References: |
Show 15 U.S. patent(s) that reference this one
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| Foreign References: |
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Publication |
Date |
IPC Code |
Assignee |
Title |
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DE9829640
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1999-03 |
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| Other Abstract Info: |
DERABS G2001-357733
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| Other References: |
"Generic Software Tool to Improve Diagnostic Systems by Feedback of Field Experience Data," Setaruddin et al., Proceedings of the 1990 IEEE conference on AUTOTESTCON, Sep. 17-21, 1990, pp. 485-490.*
"Wavelet Analysis for Diagnostic Problems," Contu et al., Proceedings of the 1996 MELECON Conference, IEEE, vol. 3, May 13-16 1996, pp. 1571-1574.*
"Pattern-Based Fault Diagnosis Using Neural Networks," Dietz et al., Proceedings of the First International Conference of Industrial & Engineering Applications of Artificial Intelligence & Expert System, 1988, ACM Press, pp. 13-23.
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