FLEET ANOMALY DETECTION SYSTEM AND METHOD

• US 20140058615A1
• Filed: 08/21/2012
• Published: 02/27/2014
• Est. Priority Date: 08/21/2012
• Status: Abandoned Application

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Hatch, Charles Terrance, Weiss, Adam Anthony, Hadley, Steven Ross
• Application Number:
  US13/590,974
• Publication Number:
  US 20140058615A1
• Time in Patent Office: Days
• US Class Current: 701/29.3
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G05B 23/0213 Modular or universal configuration of the monitoring system, e.g. monitoring system having modules that may be combined to build monitoring program; monitoring system that can be applied to legacy systems; adaptable monitoring system; using different communication protocols
• CPC Class Codes: G05B 23/0235 based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold
• CPC Class Codes: G05B 23/0237 based on parallel systems, e.g. comparing signals produced at the same time by same type systems and detect faulty ones by noticing differences among their responses
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Systems and methods for detecting anomalous behavior in one of a fleet of machines are provided. Data regarding a single characteristic representative of operation of the mechanical system is collected from each machine in a fleet. The systems and methods are configured for processing of the data to determine and indicate when significant deviations from normal operating conditions are occurring that represent the departure from normal operation condition by one or more of the machines in the fleet.

Claims

1. A system for use in detecting anomalous behavior in at least one of a fleet of machines, wherein each of the machines includes at least one component in common with all other machines in the fleet, said system comprising:

a plurality of sensors, at least one of said plurality of sensors coupled to each machine in the fleet; and

a control system configured to;

receive data transmitted from said plurality of sensors during operation of the fleet of machines, wherein the data is representative of at least one operating characteristic of the at least one component;

collect data representative of the operating characteristic from each of the machines in the fleet, wherein the data is collected under similar operating conditions for each machine;

calculate a set of mean values from the operating characteristics of each of the machines in the fleet;

calculate a set of deviations corresponding to the set of mean values relative to a median value of the set of mean values; and

determine if anomalous behavior exists based on the set of deviations.

2. A system in accordance with claim 1 wherein said control system is further configured to:

calculate a characteristic value representative of the set of deviations; and

calculate a set of normalized deviations for the calculated characteristic value for each of the machines in the fleet.

3. A system in accordance with claim 2 wherein said control system is further configured to compare the set of normalized deviations to a predefined threshold.

4. A system in accordance with claim 2 wherein to calculate a characteristic value representative of the set of deviations said control system is further configured to divide the sum of the absolute values of the deviations from the set of deviations by (n−

1), wherein n is the number of members in the set of means.

5. A system in accordance with claim 4 wherein to calculate a characteristic value representative of the set of deviations said control system is further configured to:

calculate the square root of the average of the absolute values of the set of deviations;

determine a difference between each of the set of mean values and the median; and

divide each of the determined differences by the square root of the average of the absolute values of the set of deviations.

6. A system in accordance with claim 2 wherein said control system is further configured to identify any normalized deviations exceeding a predefined threshold as being associated with anomalous behavior.

7. A system in accordance with claim 6 wherein the threshold is 1.5.

8. A system in accordance with claim 1 wherein said control system is further configured to cluster the data according to operating conditions under which the fleet of machines are operating during collection of the data.

9. A system in accordance with claim 1 wherein said control system is further configured to gather the data using a moving window defined by one of a predefined period of time and a predefined number of instances of sampling.

10. A system in accordance with claim 1 wherein the at least one operating characteristic is one of a mechanical characteristic;

and an electrical characteristic of the machine being monitored.

11. A method for use in detecting anomalous behavior in at least one of a fleet of machines, wherein each of the machines includes at least one component in common with all other machines in the fleet, said method comprising:

coupling at least one of a plurality of sensors to each machine in the fleet, wherein each sensor is configured to;

detect at least one operating characteristic of the at least one component; and

transmit data representative of the detected characteristic to a control system; and

collecting data representative of the detected characteristic from each of the machines in the fleet, wherein the data is collected under similar operating conditions for each machine;

calculating a set of mean values from the detected characteristics of each of the machines in the fleet;

calculating a set of deviations corresponding to the set of mean values relative to a median value of the set of mean values; and

determining if anomalous behavior exists based on the set of deviations.

12. A method in accordance with claim 11 further comprising:

calculating a characteristic value representative of the set of deviations; and

calculating a set of normalized deviations for the calculated characteristic value for each of the machines in the fleet.

13. A method in accordance with claim 12 further comprising comparing the set of normalized deviations to a predefined threshold.

14. A method in accordance with claim 12 wherein calculating a characteristic value representative of the set of deviations further comprises dividing the sum of the absolute values of the deviations from the set of deviations by (n−

1), wherein n is the number of members in the set of means.

15. A method in accordance with claim 14 wherein calculating a characteristic value representative of the set of deviations further comprises:

calculating the square root of the average of the absolute values of the set of deviations;

determining a difference between each of the set of mean values and the median; and

dividing each of the determined differences by the square root of the average of the absolute values of the set of deviations.

16. A method in accordance with claim 12 further comprising identifying any normalized deviations exceeding a predefined threshold as being associated with anomalous behavior.

17. A method in accordance with claim 16 wherein the threshold is 1.5.

18. A method in accordance with claim 11 further comprising clustering the data according to operating conditions under which the fleet of machines are operating during collection of the data.

19. A method in accordance with claim 11 further comprising gathering the data using a moving window defined by one of a predefined period of time and a predefined number of instances of sampling.

20. A method in accordance with claim 11 wherein the at least one operating characteristic is one of a mechanical characteristic;

and an electrical characteristic of the machine being monitored.