Next Article in Journal
Aerodynamic Drag Analysis of 3-DOF Flex-Gimbal GyroWheel System in the Sense of Ground Test
Previous Article in Journal
Standardization, Calibration, and Evaluation of Tantalum-Nano rGO-SnO2 Composite as a Possible Candidate Material in Humidity Sensors
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(12), 2078;

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

Key Laboratory of Inertial Technology, Institute of Opto-electronics Technology, School of Instrument Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
Beijing Aerospace Times Laser Inertial Technology Company, Ltd., Beijing 100094, China
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 12 August 2016 / Revised: 7 November 2016 / Accepted: 10 November 2016 / Published: 7 December 2016
(This article belongs to the Section Physical Sensors)
PDF [4288 KB, uploaded 7 December 2016]


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 × 10 5 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. View Full-Text
Keywords: dynamic stability; FOGs-MWD; dynamic Allan variance; fast algorithm; discontinuous data dynamic stability; FOGs-MWD; dynamic Allan variance; fast algorithm; discontinuous data

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, L.; Zhang, C.; Gao, S.; Wang, T.; Lin, T.; Li, X. Application of Fast Dynamic Allan Variance for the Characterization of FOGs-Based Measurement While Drilling. Sensors 2016, 16, 2078.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top