In this paper, 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 in substantial difficulties in the fault detection. Firstly, 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 χ ² distribution is designed when the estimation error is small enough. According to the property of the χ ² distribution, the window length of the moving average method is determined. Moreover, a fault detection algorithm is presented, and false alarm rate and missed detection rate 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.

In drilling engineering, Toolface is an angle used to describe bit direction. It is a challenging task to accurately estimate Toolface while drilling because of the downhole harsh conditions, but it is a primary step for the dynamic point-the-bit rotary steerable system (DPRSS). A new dynamic Toolface estimator is present, which fuses measurements from two accelerometers and one gyro. A dual-accelerometer Toolface measuring method is designed to compensate the circumferential acceleration of DPRSS. A nonlinear Complementary Filter (CF) is used to suppress the effect of vibration and axial acceleration. The frequency-domain characteristics of nonlinear CF are analyzed and its natural frequency is determined adaptively based on real time drilling conditions. This new estimator is validated on a DPRSS prototype under typical drilling modes; it is demonstrated with high robustness and follows the references satisfactorily.

Inter-turn short circuit (ITSC) fault is the most common and harmful fault of permanent magnet synchronous motor (PMSM). Preventing from subsequent severe damage to the motor and related systems, detection of ITSC becomes an important issue. In order to detect the ITSC problem efficiently and accurately, particularly when fault data is scarce, this paper proposes an effective method, which can eliminate redundant information and extract valuable features of the ITSC current signal of PMSM using a data processing method. Subsequently, Pearson coefficient optimized Wasserstein generative adversarial network (PWGAN) is dsigned to generate fault data and filter out low-quality data to construct a hierarchical training strategy, which allows siamese convolutional neural network (SCNN) have better detection of ITSC problem in the case of a small size of fault samples and reduces substantial computational burden. Due to large number of healthy data being put into model in actual use, a soft threshold method is adopted to pre-classify most of healthy data to improve stability of diagnosis system. Experimental test is performed to substantiate that the hierarchically trained model, when furnished with a substantial number of fault samples, is capable of achieving an accuracy rate of 98.75%. In comparison to other machine learning methodologies, SCNN demonstrates superior accuracy, particularly in scenarios where ITSC fault data is limited. The application of the soft threshold method allows for the preliminary diagnosis of up to 94.7% of healthy data inputs, thereby enabling the overall diagnostic system to attain an accuracy rate of 98.97%.