Method and system for diagnostics and monitoring of electric machines

• US 10,564,204 B2
• Filed: 07/28/2017
• Issued: 02/18/2020
• Est. Priority Date: 07/28/2017
• Status: Active Grant

• Current Assignee: GE Energy Power Conversion Technology Limited (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Solomon, Luke Anthony, Gray, Stuart, Doucette, Michael Richard, Dominguez, Sergio, Sieman, Alexander Richard
• Primary Examiner(s): Kuan, John C
• Application Number:
  US15/663,697
• Publication Number:
  US 20190033360A1
• Time in Patent Office: 935 Days
• Field of Search: None
• CPC Class Codes: G01R 31/50 Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections testing of sparking plugs H01T13/58
• CPC Class Codes: G01R 31/56 Testing of electric apparatus testing of transformers G01R31/62; testing of connections G01R31/66
• 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 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model
• 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
• CPC Class Codes: G05B 23/0297 Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results
• CPC Class Codes: G06F 1/28 Supervision thereof, e.g. detecting power-supply failure by out of limits supervision
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 3/02 Neural networks

Abstract

A system for use with an electric machine is provided. The system includes a processor and a memory comprising a set of memory modules, which, when executed by the processor, cause the processor to perform certain operations. The operations include receiving operational data from the electric machine, and generating, based on the operational data, a first set of diagnostic data, by executing a first memory module from the set of memory modules. The operations further include generating, based on the operational data, a second set of diagnostic data, by executing a second memory module from the set of memory modules, the second memory module including a set of parameters associated with a diagnostics model of the electric machine. Furthermore, the operations include effecting, based on the operational data, the first set of diagnostic data, and the second set of diagnostic data, a change in at least one parameter.

Claims

1. A system for performing diagnostic testing on an electric machine, the system comprising:

a diagnostic unit coupled to the electric machine and configured to retrieve from the electric machine retrieve or to instruct the electric machine to send a set of sensor measurements to determine a state of the electric machine, and including;

a processor; and

a memory stored thereon a set of memory modules, which when executed by the processor, cause the processor to perform operations including;

receiving the set of sensor measurements being operational data, from the electric machine,

generating, based on the operational data, a first set of diagnostic data, by executing a first memory module being a non-adaptive module from the set of memory modules,

generating, based on the operational data, a second set of diagnostic data, by executing a second memory module being an adaptive module from the set of memory modules, the second memory module including an adaptive routine including a set of parameters associated with a diagnostics model of the electric machine, and

effecting, based on the operational data, the first set of diagnostic data, and the second set of diagnostic data, in parallel, a change in at least one parameter from the set of parameters, wherein an existence of a fault is determined using both the first set of diagnostic data and the second set of diagnostic data.

2. The system of claim 1, wherein the sensor measurements include at least one of voltage, current, temperature, vibration signature, and insulation integrity of the electric machine.

3. The system of claim 1, wherein the effecting is further based on data received by the processor from a remote terminal, the data received from the remote terminal being associated with an assessment from an operator.

4. The system of claim 1, wherein non-adaptive processing performed by the non-adaptive module is based on a physics-based model of the electric machine.

5. The system of claim 1, wherein non-adaptive processing performed by the non-adaptive module is based on a finite-element-analysis (FEA)-based model of the electric machine.

6. The system of claim 1 wherein non-adaptive processing performed by the non-adaptive module is based on a non-learning-based model of the electric machine.

7. A method for performing diagnostic testing on an electric machine, the method comprising:

coupling a diagnostic unit to the electric machine and receiving from the electric machine or instructing the electric machine, by the diagnostic unit, to send a set of sensor measurements being operational data to determine a state of the electric machine;

generating a first set of diagnostic data by the diagnostic unit, by execution of a first memory module being a non-adaptive module of the diagnostic unit and based on the operational data;

generating a second set of diagnostic data by the diagnostic unit, by execution of a second memory module being an adaptive module of the diagnostic unit and including an adaptive routine including a set of parameters associated with a diagnostic module of the electric machine and based on the operational data; and

effecting, based on the operational data, the first set of diagnostic data, and the second set of diagnostic data, in parallel, a change in at least one parameter of a diagnostic model of the diagnostic unit used to generate the second set of diagnostic data wherein an existence of a fault is determined using both the first set of diagnostic data and the second set of diagnostic data.

8. The method of claim 7, wherein the sensor data measurements include at least one of voltage, current, temperature, vibration signature, and insulation integrity of the electric machine.

9. The method of claim 7, wherein the effecting is further based on data received by the diagnostic unit from a remote terminal, the data received from the remote terminal being associated with an assessment from an operator.

10. The method of claim 7, wherein non-adaptive processing performed by the non-adaptive module is based on a physics-based model of the electric machine.

11. The method of claim 7, wherein non-adaptive processing performed by the non-adaptive module is based on a finite element analysis (FEA)-based model of the electric machine.