Accurate remaining useful life prognosis of bearings in wind turbines can effectively help to schedule maintenance strategy and reduce operational costs at wind farms. Unscented particle filter is good at state tracking in nonlinear problem. A robust model-based approach based on improved unscented particle filter is presented to deal with bearing life prognosis in wind turbines, which involves: (1) The mean of sigma points after unscented Kalman transform is regarded as the particles in particle filter to guarantee the particles aggregation; (2) Several past measurements are utilized to estimate the likelihood function of current step; (3) Uniform distribution is adopted for resampling particles to make them diversity. The presented remaining useful life prognosis approach depends more on the measurement, rather than the initial parameters of degradation model, which makes it practicable for the on-site wind turbines. Three life-cycle bearings from wind turbine high-speed shafts demonstrate the effectiveness of the proposed approach.

Although magnetic bearings are generally reliable, an important concern for machine applications is fault tolerance. The fault-tolerant operation of a magnetic bearing system can be realized through the identification and isolation of fault actuators and subsequent support reconstruction via residual actuators. In this study, a novel fault-diagnosis algorithm for electromagnetic actuators (EMAs) is proposed. The equivalent slope of the load current is defined as a fault-diagnosis threshold by theoretically analyzing the variation characteristics of the current in the modulation. The synchronous sampling of the current and its processing algorithm are designed. By combining these algorithms with the generalized bias current linearization theory reported previously, a fault-tolerant control (FTC) system for magnetic bearings is developed, and control of multi-position and multi-current loops is realized by using two digital signal processors (DSP). The FTC of the rotor motion and the fault-diagnosis of actuators are executed in parallel. The proposed control system is verified by the experiments, and experimental results demonstrate that the proposed system can cope with EMA fault in an extremely short time of less than 5 ms. Thus, the support can be reconstructed by employing the FTC to maintain rotor stability.

In this paper, the dynamics-based high-performance robot motion control technology has been mainly studied, and the overall structure is controlled via dynamics forward, given the nonlinearity, strong coupling and time-variability of robots. Considering the unavailability of precise robot model parameters and the uncertain disturbance in real operation, we put forward an active disturbance rejection control (ADRC) strategy based on dynamic feedforward, aiming to improve the control robustness and combining the simple structure, strong anti- disturbance ability, and no restriction from the control model of ADRC. Given the multi-joint coupling of robots, controlled decoupling is conducted by using dynamic characteristics. The ADRC cascade control structure and algorithm based on dynamic feedforward have been studied and the closed-loop stability of the system is investigated by analyzing the system dynamic linearization compensation and the anti-disturbance ability of the extended state observer. Experiments have shown the new strategy is more robust over uncertain disturbance than the conventional proportional-integral-derivative control strategy.