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This paper proposes a dc voltage incipient sensor fault isolation method for single-phase three-level rectifier devices in high-speed railway electrical traction systems. Different incipient fault modes characterizing locations and incipient fault types are parameterized nonlinearly by unknown fault parameters. A new incipient fault isolation method is developed by combining sliding mode technique with nonlinear parameterization adaptive estimation technique. A bank of particular adaptive sliding mode estimators is proposed, which facilitates to derive new isolation residuals and adaptive threshold intervals. The isolability is studied, and the isolable sufficient condition is derived using new functions. For the practical electrical traction system in China Railway High-Speed 2, simulation and experiment based on TDCS-FIB (a software) are presented to verify the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This study gives the mathematic model of a quadrotor unmanned aerial vehicle (UAV) and then proposes a robust nonlinear controller which combines the sliding-mode control technique and the backstepping control technique. To achieve Cartesian position trajectory tracking capability, the construction of the controller can be divided into two stages: a regular SMC controller for attitude subsystem (inner loop) is first developed to guarantee fast convergence rapidity of Euler angles and the backstepping technique is applied to the position loop until desired attitudes are obtained and then the ultimate control laws. The stability of the closed-loop system is guaranteed by stabilizing each of the subsystems step by step and the robustness of the controller against model uncertainty and external disturbances is investigated. In addition, an adaptive observer-based fault estimation scheme is also considered for taking off mode. Simulations are conducted to demonstrate the effectiveness of the designed robust nonlinear controller and the fault estimation scheme. [ABSTRACT FROM PUBLISHER]

This paper introduces a data-driven approach for fault diagnosis in the presence of incomplete monitor data. The expectation–maximization (EM) algorithm is applied to handle missing data in order to obtain a maximum-likelihood solution for the discrete (or categorical) distribution. Because of the nature of categorical distributions, the maximization step of the EM algorithm is shown in this paper to have an easily calculated analytical solution, making this method computationally simple. An experimental study on a ball-and-tube system is investigated to demonstrate advantages of the proposed approach. [ABSTRACT FROM PUBLISHER]