Dynamic measurement errors correction adaptive to noises of a sensor

Author(s):  
E. V. Yurasova ◽  
A. S. Volosnikov
2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Qilong Xue ◽  
Ruihe Wang ◽  
Baolin Liu ◽  
Leilei Huang

In the oil and gas drilling engineering, measurement-while-drilling (MWD) system is usually used to provide real-time monitoring of the position and orientation of the bottom hole. Particularly in the rotary steerable drilling technology and application, it is a challenge to measure the spatial attitude of the bottom drillstring accurately in real time while the drillstring is rotating. A set of “strap-down” measurement system was developed in this paper. The triaxial accelerometer and triaxial fluxgate were installed near the bit, and real-time inclination and azimuth can be measured while the drillstring is rotating. Furthermore, the mathematical model of the continuous measurement was established during drilling. The real-time signals of the accelerometer and the fluxgate sensors are processed and analyzed in a time window, and the movement patterns of the drilling bit will be observed, such as stationary, uniform rotation, and stick–slip. Different signal processing methods will be used for different movement patterns. Additionally, a scientific approach was put forward to improve the solver accuracy benefit from the use of stick–slip vibration phenomenon. We also developed the Kalman filter (KF) to improve the solver accuracy. The actual measurement data through drilling process verify that the algorithm proposed in this paper is reliable and effective and the dynamic measurement errors of inclination and azimuth are effectively reduced.


Automatica ◽  
2015 ◽  
Vol 62 ◽  
pp. 208-212 ◽  
Author(s):  
Steffi Knorn ◽  
Alejandro Donaire ◽  
Juan C. Agüero ◽  
Richard H. Middleton

2015 ◽  
Vol 23 (4) ◽  
pp. 1114-1121 ◽  
Author(s):  
孙世政 SUN Shi-zheng ◽  
彭东林 PENG Dong-lin ◽  
郑方燕 ZHENG Fang-yan ◽  
武亮 WU Liang

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5030-5037 ◽  
Author(s):  
Minlan Jiang ◽  
Jingyuan Luo ◽  
Dingde Jiang ◽  
Jiping Xiong ◽  
Houbing Song ◽  
...  

2019 ◽  
Vol 3 (1) ◽  
pp. 72-82
Author(s):  
E. G. Mironov ◽  
◽  
G. Zh. Ordyuants ◽  

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 24 ◽  
Author(s):  
Andrei Sergeevich Volosnikov ◽  
Aleksandr L. Shestakov

<p>The neural network inverse model of a sensor with filtration of the sequentially recovered signal is considered. This model effectively reduces the dynamic measurement errors due to deep mathematical processing of measurement data. The result of the experimental data processing of a dynamic temperature measurement validates the efficiency of the proposed neural network approach to reduce dynamic measurement errors.</p>


Author(s):  
Minlan Jiang ◽  
Lan Jiang ◽  
Dingde Jiang ◽  
Fei Li ◽  
Houbing Song

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.


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