Particle Filter-Based Fault Detection for Toolface Measurement of Rotary Steerable Systems

In this article, the problem of accelerometer fault detection is studied for the rotary steerable drilling tool system (RSDTS) which is modeled by a nonlinear stochastic system. Owing to the strong vibration in the drilling process, RSDTSs are inevitably influenced by strong measurement noise, which brings substantial difficulties in fault detection. First, a modified particle filtering algorithm is proposed to improve the accuracy of state estimation. Next, by means of the moving average method, the residual which approximatively obeys the
$\chi ^{2}$
distribution is designed when the estimation error is small enough. According to the property of the
$\chi ^{2}$
distribution, the window length of the moving average method is determined. Moreover, a fault detection algorithm is presented, and false alarm rate (FAR) and missed detection rate (MDR) are quantitatively analyzed for nonlinear stochastic systems. Finally, the effectiveness of the developed fault detection method is verified by simulation and experiment on the prototype of the RSDTS.