Affiliations: [a] School of Application Engineering, Anhui Business and Technology College, Hefei, Anhui, China | [b] School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, Henan, China | [c] Faculty of Engineering and Computer Sciences, Concordia University, Montreal, QC, Canada
Correspondence:
[*]
Corresponding author: Lian Zhu, School of Application Engineering, Anhui Business and Technology College, Hefei, Anhui, China. E-mail: zhu_lian1981@163.com.
Abstract: Traditional denoising methods for Micro-electromechanical Systems (MEMS) gyro signal are required to obtain a priori noise statistical properties, which result in poor denoising performance in MEMS gyro utilized in Micro-Inertial Measurement While Drilling (MWD), due to the unknown and complex noise characteristics in MWD. According to this problem, a kind of gyro signal denoising method based on sparse decomposition without a requirement of the priori noise characteristics, utilizing a newly designed atom dictionary, is proposed. Firstly, the MEMS gyro output differential equation is established on the basis of the physical mechanism of the MEMS gyro, then the real MEMS gyro output signal characteristics are analyzed according to the solution of the differential equation. Secondly, the characteristic wave atom most similar to the gyro output signal is designed. Finally, the gyro signal sparse decomposition denoising experiments based on the designed atom dictionary are conducted, compared with the wavelet threshold method and Kalman filter. The experiment results show that the proposed denoising method based on sparse decomposition utilizing the newly designed atom dictionary outperforms wavelet threshold method and Kalman filter in MEMS gyro signal processing of MWD, especially when the noise statistical properties of gyro signal are completely unknown.
Keywords: Measurement while drilling, MEMS gyro, sparse decomposition, signal denoising
