Machine Anomaly Detection and Diagnosis Incorporating Operational Data

• US 20130060524A1
• Filed: 11/21/2011
• Published: 03/07/2013
• Est. Priority Date: 12/01/2010
• Status: Abandoned Application

• Current Assignee: Siemens Corp. (Siemens AG)
• Original Assignee: Siemens Corp. (Siemens AG)
• Inventors: Liao, Linxia
• Application Number:
  US13/301,157
• Publication Number:
  US 20130060524A1
• Time in Patent Office: Days
• US Class Current: 702/184
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A method for detecting an anomaly in a machine under test includes monitoring operational data from a control unit of the machine under test. An operational state of the machine under test is identified based on the monitored operational data. Sensor data is monitored from one or more sensors installed within or near to the machine under test. A model corresponding to the identified operational state of the machine under test is consulted to identify one or more key parameters and corresponding normal operating ranges for each determined key parameter. It is determined when a key parameter of the one or more key parameters is not within its corresponding normal operating range based on the monitored sensor data.

Claims

1. A method for detecting an anomaly in a machine under test, comprising:

monitoring operational data from a control unit of the machine under test;

identifying an operational state of the machine under test based on the monitored operational data;

monitoring sensor data from one or more sensors installed within or near to the machine under test;

consulting a model corresponding to the identified operational state of the machine under test to identify one or more key parameters and corresponding normal operating ranges for each determined key parameter; and

determining when a key parameter of the one or more key parameters is not within its corresponding normal operating range based on the monitored sensor data.

2. The method of claim 1, wherein determining when the key parameter of the one or more key parameters is not within its corresponding normal operating range is based on monitored operational data in addition to the monitored sensor data.

3. The method of claim 1, wherein the one or more key parameters comprise a single operational indicator that is calculated from the sensor data and expresses an overall operational condition of the machinery under test and the corresponding normal operating range comprises an acceptable level of deviation from an expected value of the operational indicator.

4. The method of claim 1, wherein the machine under test comprises a machine tool, a gas turbine, or a high-speed train.

5. The method of claim 1, additionally comprising automatically initiating a diagnostic routine to identify a malfunction within the machine under test when it is determined that a key parameter is not within its corresponding normal operating range.

6. The method of claim 1, additionally comprising generating an alert when it is determined that a key parameter is not within its corresponding normal operating range.

7. The method of claim 1, wherein the operational data includes operating instructions for the machine under test.

8. The method of claim 1, wherein the operational data include a desired operational speed or a desired degree of engagement that has been sent to the control unit.

9. The method of claim 1, wherein identifying the operational state of the machine under test based on the operational data includes determining which of a set of discrete clusters of data values the operating data falls within.

10. The method of claim 1, wherein when the identified operational state of the machine under test has no existing corresponding model, a new model is generated for the operating state.

11. The method of claim 10, wherein generating the model for the corresponding operating state comprises:

extracting one or more features from the monitored sensor;

identifying one or more key parameters from the extracted one or more features; and

determining normal operating ranges for each of the one or more key parameters.

12. The method of claim 11, wherein prior to identifying the one or more key parameters, feature selection or feature reduction is performed on the one or more extracted features.

13. A system for detecting an anomaly in a machine under test, comprising a condition based maintenance (CBM) module for receiving machine data or sensor data from one or more sensors installed within or near the machine under test and for receiving operational data from a control module of the machine under test, the CBM module comprising:

an operational state monitoring and determining unit for receiving the operational data from the control module and identifying an operational state of the machine under test based on the operational data;

a sensor data monitoring and matching unit for receiving the machine data or sensor data from the one or more sensors and determining when a key parameter of the sensor data is beyond a normal operating range defined for the identified operational state; and

a remediation and alert module for taking remedial action or generating an alert when the key parameter of the sensor data is beyond the normal operating range for the identified operational state.

14. The system of claim 13, wherein the control module includes a computer numerical control, a control unit with a programmable logic controller (PLC), or a control unit with a human machine interface (HMI).

15. The system of claim 13, wherein the remediation and alert module automatically executes one or more diagnostic utilities for identifying a malfunction in the machine under test when the key parameter of the sensor data is beyond the normal operating range for the identified operational state.

16. The system of claim 13, wherein the remediation and alert module generates a maintenance work order when the key parameter of the sensor data is beyond the normal operating range for the identified operational state.

17. The system of claim 13, wherein the operational data includes operating instructions for the machine under test.

18. The system of claim 13, wherein the operational data includes a desired operational speed or a desired degree of engagement that has been sent to the control unit.

19. The system of claim 13, wherein identifying the operational state of the machine under test based on the operational data includes determining which of a set of discrete clusters of data values the operating data falls within.

20. The system of claim 13, wherein the CBM module additionally includes a model generation unit for generating a new model for the identified operating state when no corresponding model exists for the identified operating state.

21. The system of claim 20, wherein the CBM module additionally includes a feature extraction unit for:

extracting one or more features from the monitored sensor;

identifying one or more key parameters from the extracted one or more features; and

determining normal operating ranges for each of the one or more key parameters.

22. The system of claim 21, wherein the CBM module additionally includes a feature selection/reduction unit for performing feature selection or feature reduction on the one or more extracted features prior to identifying the one or more key parameters.

24. The system of claim 13, wherein the control unit includes a computer numerical control, a control unit with a programmable logic controller (PLC), or a control unit with a human machine interface (HMI).

23. A computer system comprising:

a processor; and

a non-transitory, tangible, program storage medium, readable by the computer system, embodying a program of instructions executable by the processor to perform method steps for detecting an anomaly in a machine under test, the method comprising;

monitoring operational data from a control unit of the machine under test;

identifying an operational state of the machine under test based on the monitored operational data;

monitoring sensor data from one or more sensors installed within or near to the machine under test;

calculating an operational indicator for expressing an overall operational condition of the machinery under test from the sensor data;

consulting a model corresponding to the identified operational state of the machine under test to identify an expected value of the operational indicator and an acceptable measure of deviation therefrom;

determining when the operational indicator is not within the acceptable measure of deviation from the expected value based on the monitored sensor data; and

automatically initiating a diagnostic routine to identify a malfunction within the machine under test when it is determined that a key parameter is not within its corresponding normal operating range.