SYSTEMS AND METHODS FOR MONITORING SENSORS

• US 20140091811A1
• Filed: 09/28/2012
• Published: 04/03/2014
• Est. Priority Date: 09/28/2012
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (GE Vernova, Inc.)
• Original Assignee: General Electric Company
• Inventors: Potyrailo, Radislav Alexandrovich, Ashe, Jeffrey Michael, Rao, Naresh Kesavan, Sundaresan, Krishnakumar, Hasan, Sm Shajed
• Granted Patent: US 9,147,144 B2
• Time in Patent Office: Days
• US Class Current: 324/602
• CPC Class Codes: G06K 19/0717 the sensor being capable of sensing environmental conditions such as temperature history or pressure
• CPC Class Codes: G06K 19/0723 the record carrier comprising an arrangement for non-contact communication, e.g. wireless communication circuits on transponder cards, non-contact smart cards or RFIDs
• CPC Class Codes: G06K 19/07749 the record carrier being capable of non-contact communication, e.g. constructional details of the antenna of a non-contact smart card

Abstract

A method for multivariable measurements using a single-chip impedance analyzer includes providing a sensor, exposing the sensor to an environmental parameter, determining a complex impedance of the sensor over a measured spectral frequency range of the sensor, and monitoring at least three spectral parameters of the sensor.

Claims

1. A method for multivariable measurements using a single-chip impedance analyzer, the method comprising

providing a sensor;

exposing the sensor to an environmental parameter;

determining a complex impedance of the sensor over a measured spectral frequency range of the sensor; and

monitoring at least three spectral parameters of the sensor.

2. The method of claim 1, further comprising down converting a radio frequency signal to a baseband signal.

3. The method of claim 1, further comprising reducing a number of bits for analog to digital conversion to reduce the total power consumption.

4. The method of claim 1, further comprising filtering out undesirable signals and noise sources.

5. The method of claim 1, further comprising:

regulating an excitation amplitude of the sensor, and

detecting a phase of a sensor signal.

6. A method, comprising:

generating an excitation signal for a plurality of sensors, comprising digital to analog conversion, wherein a transfer function for the digital to analog conversion is programmable;

monitoring a plurality of responses from the sensors; and

transmitting sensor data.

7. The method of claim 6, further comprising wirelessly transmitting the sensor data.

8. The method of claim 6, comprising transmitting data from one or more sensors of the plurality of sensors to a sensor network.

9. The method of claim 6, further comprising harvesting energy from an ambient environment, wherein the energy comprises a thermal energy, a radio frequency energy, a light energy, a vibration energy, a motion energy, or a combination thereof.

10. The method of claim 6, further comprising harvesting energy from a remote source.

11. The method of claim 6, comprising monitoring the sensor in real-time.

12. The method of claim 6, comprising monitoring a plurality of spectral responses from the sensor.

13. The method of claim 6, comprising using principal components analysis (PCA) to produce a multivariate signature from a single multivariable sensor.

14. A method for re-calibration of individual multivariable sensor nodes in a sensor network, the method comprising:

identifying one or more environmental parameters of interest that are monitored by the multivariable sensor nodes;

identifying one or more interference parameters of interest that produce an interference response by the multivariable sensor nodes;

measuring responses from the multivariable sensor nodes in response to exposure to one or more environmental parameters of interest;

determining a response relation between the environmental parameters of interest and response of the multivariable sensor nodes;

determining a response relation between the one or more interference parameters and response of the multivariable sensor nodes; and

determining a response relation between the environmental parameters of interest in the presence of the one or more interference parameters and response of the multivariable sensor nodes

15. The method of claim 14, further comprising reducing a contribution of the interference parameters from the response of the multivariable sensor nodes to obtain the response of the multivariable sensor nodes due to the environmental parameters.

16. The method of claim 14, further comprising transforming the value of the sensor response to a value of the parameters of interest using stored analytical fit coefficients.

17. The method of claim 14, further comprising delivering computed values of the parameters to a central computing center of the network cloud.

18. The method of claim 17, further comprising applying the computed values of the analytical fit coefficients from the cloud to other multivariable sensors of the network to obtain analytical fit coefficients of the individual sensor nodes of the network.

19. The method of claim 17, further comprising applying the values of the calibration coefficients obtained from one multivariable sensor of the network to correct multivariable responses of other sensors of the network through scaling coefficients for individual sensors.

20. The method of claim 14, comprising determining new analytical fit coefficients that relate multivariable sensor response, a parameter value, and an interference value from the re-calibration procedure.

21. The method of claim 20, comprising comparing the new analytical fit coefficients with the coefficients previously stored in a sensor or in a central computing center of a network cloud.

22. A method for re-calibration of a sensor network, the method comprising:

identifying one or more environmental parameters of interest that are monitored by the sensor network;

identifying one or more interference parameters of interest that produce an interference response by the sensor network;

measuring responses from the multivariable sensor nodes in response to exposure to one or more environmental parameters of interest;

determining a response relation between the environmental parameters of interest and response of the sensor network;

determining a response relation between the one or more interference parameters and response of the sensor network; and

determining a response relation between the environmental parameters of interest in the presence of the one or more interference parameters and response of the sensor network.