Dynamic event-triggered fault detection for rotary steerable systems with unknown time-varying noise covariances

This paper is concerned with the fault detection problem for the rotary steerable drilling tool system under unknown vibrations and limited computational resources. Firstly, the drilling tool system can be modeled by a nonlinear stochastic system with unknown time-varying noise covariances. Then, the dynamic event-triggered mechanism is introduced to save computational resources, and the caused transmission error is completely decoupled by nonuniform sampling. Subsequently, a novel unscented Kalman filter is proposed by combining the expectation maximization method to estimate states when noise covariances are unknown. A residual and an evaluation function are constructed to detect faults. Finally, a numerical simulation and an experiment on a drilling tool prototype validate the superior performance of the proposed fault detection scheme, which has lower missed alarm rates and consumes less time than existing methods.