Calibration apparatus, calibration method, and measuring system

1. A calibration apparatus for calibrating a magnetostrictive sensor, which is configured to measure an object and comprises a sensing element positioned adjacent to the object, the calibration apparatus comprising:

an estimation device, including a data processor, for estimating at least one of a gap between the sensing element and the object and a temperature of the object to obtain at least one of an estimated gap and an estimated temperature, based on geometric information of the magnetostrictive sensor, an excitation signal and an output signal of the magnetostrictive sensor, and geometric information of the object; and

a calibrator for reducing an effect on the output signal of the magnetostrictive sensor imposed by variations in the at least one of the gap and the temperature based on the at least one of the estimated gap and the estimated temperature, to obtain a calibrated output signal.

2. The calibration apparatus according to claim 1 , wherein the calibrator comprises a first adjuster for adjusting the excitation signal based on the at least one of the estimated gap and the estimated temperature.

3. The calibration apparatus according to claim 2 , wherein the first adjuster comprises:

a first compensation calculator for calculating an excitation signal compensation based on the at least one of the estimated gap and the estimated temperature; and

a first compensator for compensating the excitation signal according to the excitation signal compensation.

4. The calibration apparatus according to claim 1 , wherein the magnetostrictive sensor comprises a plurality of sensing elements which output a plurality of output signals respectively, the estimation device is configured to estimate at least one of gaps between the sensing elements and the object and the temperature of the object to obtain at least one of estimated gaps and the estimated temperature, and the calibration apparatus further comprises a common-mode suppression device for suppressing a common-mode part of the plurality of output signals based on a common-mode suppression signal.

5. The calibration apparatus according to claim 4 , wherein the calibrator comprises a second adjuster for adjusting the common-mode suppression signal based on the at least one of the estimated gaps and the estimated temperature.

6. The calibration apparatus according to claim 5 , wherein the second adjuster comprises:

a second compensation calculator for calculating a common-mode suppression signal compensation based on the at least one of the estimated gaps and the estimated temperature; and

a second compensator for compensating the common-mode suppression signal according to the common-mode suppression signal compensation.

7. The calibration apparatus according to claim 1 , wherein the estimation device is configured to estimate the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises a linear model, a nonlinear model, a physical model-based algorithm, a data-driven algorithm, or any combination thereof.

8. The calibration apparatus according to claim 1 , wherein the estimation device is configured to estimate the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises an optimal regression model, a neural network based model, an inversed physical model, an extended kalman filter (EKF), an unscented kalman filter (UKF), or any combination thereof.

9. A measuring system for measuring an object, comprising:

a magnetostrictive sensor comprising a sensing element positioned adjacent to the object;

an estimation device, including a data processor, for estimating at least one of a gap between the sensing element and the object and a temperature of the object to obtain at least one of an estimated gap and an estimated temperature, based on geometric information of the magnetostrictive sensor, an excitation signal and an output signal of the magnetostrictive sensor, and geometric information of the object;

a calibrator for reducing an effect on the output signal of the magnetostrictive sensor imposed by variations in the at least one of the gap and the temperature based on the at least one of the estimated gap and the estimated temperature, to obtain a calibrated output signal; and

a calculator for calculating at least one of a stress, a torque and a rotation speed of the object, based on the calibrated output signal.

10. The measuring system according to claim 9 , wherein the estimation device is configured to estimate the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises a linear model, a nonlinear model, a physical model-based algorithm, a data-driven algorithm, or any combination thereof.

11. The measuring system according to claim 9 , wherein the estimation device is configured to estimate the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises an optimal regression model, a neural network based model, an inversed physical model, an extended kalman filter (EKF), an unscented kalman filter (UKF), or any combination thereof.

12. A calibration method for calibrating a magnetostrictive sensor, which is configured to measure an object and comprises a sensing element adjacent to the object, the calibration method comprising:

estimating at least one of a gap between the sensing element and the object and a temperature of the object to obtain at least one of an estimated gap and an estimated temperature, based on geometric information of the magnetostrictive sensor, an excitation signal and an output signal of the magnetostrictive sensor, and geometric information of the object; and

reducing an effect on the output signal of the magnetostrictive sensor imposed by variations in the at least one of the gap and the temperature based on the at least one of the estimated gap and the estimated temperature, to obtain a calibrated output signal.

13. The calibration method according to claim 12 , wherein the reducing the effect comprises adjusting the excitation signal based on the at least one of the estimated gap and the estimated temperature.

14. The calibration method according to claim 13 , wherein the adjusting the excitation signal comprises:

calculating an excitation signal compensation based on the at least one of the estimated gap and the estimated temperature; and

compensating the excitation signal according to the excitation signal compensation.

15. The calibration method according to claim 12 , wherein the magnetostrictive sensor comprises a plurality of sensing elements which output a plurality of output signals respectively, the step of estimating comprises estimating at least one of gaps between the plurality of sensing elements and the object and the temperature of the object to obtain at least one of estimated gaps and the estimated temperature, and the method further comprises suppressing a common-mode part of the plurality of output signals based on a common-mode suppression signal.

16. The calibration method according to claim 15 , wherein the reducing the effect comprises adjusting the common-mode suppression signal based on the at least one of the estimated gaps and the estimated temperature.

17. The calibration method according to claim 16 , wherein the adjusting the common mode suppression signal comprises:

calculating a common-mode suppression signal compensation based on the at least one of the estimated gaps and the estimated temperature; and

compensating the common-mode suppression signal according to the common mode suppression signal compensation.

18. The calibration method according to claim 12 , wherein the estimating comprises estimating the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises a linear model, a nonlinear model, a physical model-based algorithm, a data-driven algorithm, or any combination thereof.

19. The calibration method according to claim 12 , wherein the estimating comprises estimating the at least one of the gap and the temperature based on an algorithm, and the algorithm comprises an optimal regression model, a neural network based model, an inversed physical model, an extended kalman filter (EKF), an unscented kalman filter (UKF), or any combination thereof.