Application of Fast Dynamic Allan Variance for the Characterization of FOGs-Based Measurement While Drilling

Abstract

The stability of a fiber optic gyroscope (FOG) in measurement while drilling (MWD) could vary with time because of changing temperature, high vibration, and sudden power failure. The dynamic Allan variance (DAVAR) is a sliding version of the Allan variance. It is a practical tool that could represent the non-stationary behavior of the gyroscope signal. Since the normal DAVAR takes too long to deal with long time series, a fast DAVAR algorithm has been developed to accelerate the computation speed. However, both the normal DAVAR algorithm and the fast algorithm become invalid for discontinuous time series. What is worse, the FOG-based MWD underground often keeps working for several days; the gyro data collected aboveground is not only very time-consuming, but also sometimes discontinuous in the timeline. In this article, on the basis of the fast algorithm for DAVAR, we make a further advance in the fast algorithm (improved fast DAVAR) to extend the fast DAVAR to discontinuous time series. The improved fast DAVAR and the normal DAVAR are used to responsively characterize two sets of simulation data. The simulation results show that when the length of the time series is short, the improved fast DAVAR saves 78.93% of calculation time. When the length of the time series is long (6×105 samples), the improved fast DAVAR reduces calculation time by 97.09%. Another set of simulation data with missing data is characterized by the improved fast DAVAR. Its simulation results prove that the improved fast DAVAR could successfully deal with discontinuous data. In the end, a vibration experiment with FOGs-based MWD has been implemented to validate the good performance of the improved fast DAVAR. The results of the experience testify that the improved fast DAVAR not only shortens computation time, but could also analyze discontinuous time series.

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

“…Firstly, inspired by the time sequence processing in data science community, Allan variance (AV) was employed to analyze the MEMS IMU error components, and then ARMA models are employed for modeling and representing the noise [22][23][24][25][26][27][28][29][30][31]. After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM).…”

“…After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM). With the rapid development of the semiconductor technology and computing capacity, recently, deep learning (DL) gained a boom in data science community [22][23][24][25][26][27][28][29][30][31]. Artificial intelligence (AI) methods were employed in sequence data processing and obtained great advances while compared with the conventional machine learning methods [22][23][24][25][26][27][28][29][30][31].…”

“…In this paper, inspired by the excellent capability of deep neural networks in sequence data processing, the advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed and tested for reducing the MEMS gyroscope noise (only the gyroscope was tested using the proposed method, and the accelerometers processing was similar to that of gyroscope). A commercial MEMS IMU STIM300 was employed in the experiments [22][23][24][25][26][27][28][29][30][31]. e sampling frequency was 125 Hz, and the testing time was approximately 800 seconds.…”

Analysis of Resonance Asymmetry Phenomenon in Resonant Fiber Optic Gyro

“…Gyro is an inertial sensor for the rotation rate measurement [ 1 ]. At present, fiber optic gyro, micro-optic gyro, laser gyro has great development potential because of its own advantages [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Resonant fiber optic gyro (RFOG) based on the Sagnac effect has the advantages of high sensitivity and large dynamic range [ 2 ].…”

Application of Improved Fast Dynamic Allan Variance for the Characterization of MEMS Gyroscope on UAV

“…For MEMS sensor analyses, DAVAR could track and describe the dynamic characteristics of time series, and it is advantageous to analyze the process of gyroscope errors. The DAVAR is a cluster of Allan variance; therefore, the computational burden is very high because the DAVAR requires the computation of an Allan variance at every time instant [22,23]. A recursive algorithm for DAVAR is proposed in [24].…”