A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal
Abstract
:1. Introduction
2. Materials and Methods
2.1. North-Seeking Principles of the GAT
2.2. HSA-KS Method
2.2.1. Main Principle of HSA Applied to the GAT Signal
2.2.2. Determination of the Categorical Stationary Subsequence
2.2.3. Two-Sample Kolmogorov-Smirnov Test
2.2.4. Specific Steps of the HSA-KS Method
2.3. Experimental Design
2.3.1. Field Experiment
2.3.2. Three Schemes for Processing GAT Signals
3. Results and Discussion
3.1. Typical GAT Signals
3.2. Signal Processing Results
3.2.1. Signal 1 Processing Result
3.2.2. Signal 2 Processing Result
3.3. Results of the D-Values
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACF | autocorrelation function |
CSS | categorical stationary subsequence |
EMD | empirical mode decomposition |
GAT | maglev gyro total station |
GPS | global positioning system |
HHT | Hilbert-Huang transform |
HSA | heuristic segmentation algorithm |
IMF | intrinsic mode function |
KS | Kolmogorov-Smirnov |
RMSE | root mean square error |
WT | wavelet transform |
gyro north azimuth | |
N | total number of the current samples |
collected real-time rotor current value of the torquer | |
collected real-time stator current value of the torquer | |
Ti(t) | statistics sequence in the HSA-KS method of the i-th segmentation |
Tmax(i) | maximum value of Ti(t) |
P(Tmax) | the statistical significance |
l | the total number of samples of the signal to be segmented |
I0(t) | signal sequence of the CSS |
Ih(t) | subsequence with CSS for the two-sample KS test |
KS statistic | |
PKS | significance level of |
α | significance level |
m | number of the subsequences segmented from the original signal |
A1, B1, C1, …, and Q1 | subsequences segmented by the signal 1 |
A2, B2, C2, …, and K2 | subsequences segmented by the signal 2 |
D-value | absolute difference between the gyro and high-precision GPS north azimuths |
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Scheme | Original Signal | HSA-KS Method | Optimized HHT Method | Optimized WT Method |
---|---|---|---|---|
Signal 1 | 9.2 | 3.5 | 10.1 | 3.3 |
Signal 2 | 3.5 | 1.4 | 4.0 | 7.5 |
Scheme | Original Signal | HSA-KS Method | Optimized HHT Method | Optimized WT Method |
---|---|---|---|---|
Mean value | 7.1 | 3.3 | 6.8 | 5.1 |
Improvement | — | 53.5% | — | 28.2% |
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Wang, Y.; Yang, Z.; Ma, J.; Shi, Z.; Liu, D.; Shi, L.; Li, H. A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal. Sensors 2023, 23, 2763. https://doi.org/10.3390/s23052763
Wang Y, Yang Z, Ma J, Shi Z, Liu D, Shi L, Li H. A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal. Sensors. 2023; 23(5):2763. https://doi.org/10.3390/s23052763
Chicago/Turabian StyleWang, Yiwen, Zhiqiang Yang, Ji Ma, Zhen Shi, Di Liu, Ling Shi, and Hang Li. 2023. "A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal" Sensors 23, no. 5: 2763. https://doi.org/10.3390/s23052763
APA StyleWang, Y., Yang, Z., Ma, J., Shi, Z., Liu, D., Shi, L., & Li, H. (2023). A Novel Method for Automatic Detection and Elimination of the Jumps Caused by the Instantaneous Disturbance Torque in the Maglev Gyro Signal. Sensors, 23(5), 2763. https://doi.org/10.3390/s23052763