Method and system for remotely managing communication of data used for predicting malfunctions in a plurality of machines

• US 7,051,044 B1
• Filed: 10/10/2000
• Issued: 05/23/2006
• Est. Priority Date: 10/28/1999
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Puri, Ashish, Fera, Gregory John, Loncher, Steven, O'Camb, Thomas E., Pander, James E., Pierro, Michael J., Hedlund, Eric Harold, Lovelace, John H.
• Primary Examiner(s): Mofiz, Apu
• Assistant Examiner(s): Wu, Yicun
• Application Number:
  US09/685,288
• Time in Patent Office: 2,051 Days
• Field of Search: 290/52, 246/169.R, 187/393, 340/10.3, 340/584, 340/521, 345/709, 455/456.1, 358/1.14, 700/1, 700/286, 702/57, 702/183, 702/181, 702/184, 702/179, 702/186, 701/115, 701/19, 701/29, 701/33, 701/35, 707/4, 707/104.1, 707/110, 707/100, 709/224, 709/203, 713/1, 714/2, 714/48, 714/31, 714/25, 714/26, 714/45, 399/8, 376/215
• US Class Current: 1/1
• CPC Class Codes: B61L 2205/04 Satellite based navigation systems, e.g. global positioning system [GPS]
• CPC Class Codes: B61L 27/57 for vehicles or trains, e.g. trackside supervision of train conditions
• CPC Class Codes: G05B 2223/06 Remote monitoring
• 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 11/2257 using expert systems
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing
• CPC Class Codes: Y10S 707/99948 Application of database or data structure, e.g. distributed, multimedia, or image

Abstract

A method and system for managing communication of electronic data between a diagnostic service center and a plurality of mobile assets (10) generally remote relative to each other is provided. The electronic data comprises at least respective mobile asset data indicative of respective performance of the plurality of mobile assets. The mobile asset data used for detecting the presence of potential malfunctions which, if left uncorrected, would likely result in respective mission failures of the assets. The method allows for storing in a database (104) a list of respective cases to be processed. The method further allows for assigning to each case a respective download priority. A determining action allows for determining each case to be populated next with new mobile asset data based upon the assigned download priority. Respective executing actions allow for executing a download of the new mobile asset data between the diagnostic service center and a respective one of the mobile assets, and for executing predetermined analysis on the downloaded data for detecting the presence of respective potential malfunctions in the mobile assets. The download of data may be executed automatically whenever critical faults occur in the mobile assets.

Claims

1. A computer-implemented method for identifying critical faults from unranked fault data collected from a fleet of locomotives, the method comprising:

a) collecting from a group of the fleet of locomotives respective locomotive data indicative of each fault logged over a predetermined period of time;

b) classifying respective faults in the collected locomotive data based on the following criteria;

1) relative frequency of fault occurrence;

2) number of locomotives affected in the group; and

3) expected level of reduction in locomotive operational performance;

wherein any of the three criteria comprises a first basis of classification, and a second classification is based on the results of the first classification so that any faults found to be critical include properties in at least two of the classifications, and further wherein all three criteria are separately considered in sequence and further wherein each classification is based on the results of any previous classification so that the faults found to be critical include properties in all three classifications; and

c) storing any faults found to he critical in a database comprising critical faults.

2. A computer-implemented method for identifying critical faults from unranked fault data collected from a fleet of locomotives, the method comprising:

a) collecting from a group of the fleet of locomotives respective locomotive data indicative of each fault logged over a predetermined period of time;

b) classifying respective faults in the collected locomotive data based on the following criteria;

1) relative frequency of fault occurrence;

2) number of locomotives affected in the group; and

3) expected level of reduction in locomotive operational performance;

wherein any of the three criteria comprises a first basis of classification, and a second classification is based on the results of the first classification so that any faults found to be critical include properties in at least two of the classifications; and

c) storing any faults found to be critical in a database comprising critical faults, and wherein the database of critical faults is used in a process for assigning priorities to communications of electronic data between a diagnostic service center and a plurality of locomotives generally remote relative to each other, the assigned priorities being used for managing the handling of such communications, the electronic data comprising at least respective new locomotive data from selected locomotives, the process comprising;

storing in a database a list of respective cases to be processed;

assigning to each case a respective download priority based on the existence of critical faults in the case; and

determining each case to be populated next with new locomotive data based at least upon the assigned download priority.

3. The method of claim 2 further comprising executing a download of new locomotive data wherein said download of new locomotive data is triggered upon a call from a respective locomotive to the service center, the call identifying occurrence in the respective locomotive of one or more faults of the type stored in the critical fault database.

4. The method of claim 3 further comprising prioritizing analysis of locomotive data including critical faults.

5. A system for identifying critical faults from unranked fault data collected from a fleet of locomotives, the system comprising:

memory configured to collect from a group of the fleet of locomotives respective locomotive data indicative of each fault logged over a predetermined period of time;

a first classifier configured to classify in the collected locomotive data respective faults most frequently occurring relative to one another;

a second classifier configured to classify in the most frequently occurring faults from the first classifier, respective faults that, relative to one another, affect a higher number of locomotives in the group;

a third classifier configured to classify the faults from the second classifier based on an expected level of I reduction in locomotive operational performance; and

a database coupled to the third classifier to store any faults classified as likely to result in an imminent locomotive mission failure, the stored faults comprising the plurality of critical faults, and wherein the database of critical faults is used in a processor for managing communication of electronic data between a diagnostic service center and the plurality of locomotives situated generally remote relative to each other, the electronic data comprising new locomotive data used from selected locomotives, the processor comprising;

a module configured to execute a download of the new locomotive data wherein said download of new locomotive data is triggered upon a call from a respective locomotive to the service center, the call identifying occurrence in the respective locomotive of one or more faults of the type stored in the critical fault database.

6. The system of claim 5 wherein the call to the service center is automated upon detection in the locomotive of one or more of the faults of the type stored in the critical fault database.

7. A system for identifying critical faults in unranked fault data collected from a fleet of locomotives, the system comprising:

memory configured to collect from a group of the plurality of locomotives respective locomotive data indicative of each fault logged over a predetermined period of time;

a processor configured to classify respective faults in the collected locomotive data based on the following criteria;

1) relative frequency of fault occurrence;

2) number of locomotives affected in the group; and

3) expected level of reduction in locomotive operational performance;

wherein any of the three criteria comprises a first basis of classification, and a second classification is based on the results of the first classification so that any faults found to be critical include properties in at least two of the classifications, and further wherein all three criteria are separately processed by the processor in sequence and further wherein each classification is based on the results of any previous classification so that the faults found to be critical include properties in all three classifications.

9. The system of claim 7 further comprising a download module configured to execute a download of new locomotive data wherein said download of new locomotive data is triggered upon a call from a respective locomotive to the service center, the call identifying occurrence in the respective locomotive of one or more faults of the type stored in the critical fault database.

8. A system for identifying critical faults in unranked fault data collected from a fleet of locomotives, the system comprising:

memory configured to collect from a group of the plurality of locomotives respective locomotive data indicative of each fault lagged over a predetermined period of time;

a processor configured to classify respective faults in the collected locomotive data based on the following criteria;

1) relative frequency of fault occurrence;

2) number of locomotives affected in the group; and

3) expected level of reduction in locomotive operational performance;

wherein any of the three criteria comprises a first basis of classification, and a second classification is based on the results of the first classification so that any faults found to be critical include properties in at least two of the classifications, and further wherein all three criteria are separately processed by the processor in sequence; and

a database for storing faults found to be critical, wherein the database of critical faults is used by a priority-assigner processor configured to assign priorities to communications of electronic data between a diagnostic service center and a plurality of locomotives generally remote relative to each other, the assigned priorities being used for managing the handling of such communications, the electronic data comprising at least respective new locomotive data from selected locomotives, the priority-assigner processor comprising;

a database configured to store a list of respective cases to be processed;

a module configured to assign to each case a respective download priority based on the existence of critical faults in the case; and

a module configured to determine each case to be populated next with new locomotive date based at least upon the assigned download priority.