Accelerometer Fault Detection for Rotary Steerable Drilling Tool Systems Under Strong Noises
- 12 September 2022
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 71 (00189456) , 1-11
Abstract
This article is concerned with the problem of accelerometer fault detection for dynamic point-the-bit rotary steerable drilling tool systems (DPRSDTSs). The essential difficulty of this problem lies primarily in the fact that the accelerometer fault is usually submerged in strong noises induced by the mechanical vibration during the drilling process. First, DPRSDTS is modeled by a time-varying system with multiplicative noises. Subsequently, the fault detection filter and residual generator are designed by means of the Kalman filtering algorithm and the weighted moving average (WMA) method. By choosing suitable weight matrices and window length, the detectability of accelerometer fault can be ensured in a probabilistic sense. In such a case, the real false alarm rate (FAR) and missed detection rate (MDR) are reduced below the allowable level. Finally, the simulations and experiments performed on the DPRSDTS prototype are given to show the feasibility and effectiveness of the developed method.Keywords
Funding Information
- National Natural Science Foundation of China (62173343, 62073339, 62033008)
- Natural Science Foundation of Shandong Province of China (ZR2020YQ49)
This publication has 29 references indexed in Scilit:
- Directional Control and Rathole Elimination While Underreaming Depleted Formations With a Rotary-Steerable SystemSPE Drilling & Completion, 2018
- Analysis and optimization of control algorithms for RSSTSP for horizontal well drillingJournal of Petroleum Exploration and Production Technology, 2018
- A Thrust Model Aided Fault Diagnosis Method for the Altitude Estimation of a QuadrotorIEEE Transactions on Aerospace and Electronic Systems, 2017
- Recursive transformed component statistical analysis for incipient fault detectionAutomatica, 2017
- Influence of Rotary-Steerable-System Design on Borehole SpiralingSPE Journal, 2016
- Minimum-Variance Recursive Filtering Over Sensor Networks With Stochastic Sensor Gain Degradation: Algorithms and Performance AnalysisIEEE Transactions on Control of Network Systems, 2015
- Kullback–Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stabilityAutomatica, 2014
- Model-Based Fault Diagnosis Techniques2013
- Fault Detection Schemes for Continuous-Time Stochastic Dynamical SystemsIEEE Transactions on Automatic Control, 2009
- Inequalities for noncentral chi-square distributionsStatistics & Probability Letters, 1990