Sydney. He focused on fundamental research on mechanical vibration, multi-body system dynamics and its applications to complex machines and vehicular systems. He developed advanced models and numerical schemes for simulating gear shift in powertrains with AT, MT and CVTs and for dynamic analysis of vehicles fitted with advanced suspensions.

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In order to reduce operation and maintenance costs, reliability, and quick response capability of multi-fault intelligent diagnosis for the wind turbine system are becoming more important. This paper proposes a rapid data-driven fault diagnostic method, which integrates data pre-processing and machine learning techniques. In terms of data pre-processing, fault features are extracted by using the proposed modified Hilbert-Huang transforms (HHT) and correlation techniques. Then, time domain analysis is conducted to make the feature more concise. A dimension vector will then be constructed by including the intrinsic mode function energy, time domain statistical features, and the maximum value of the HHT marginal spectrum. On the other hand, as the architecture and the learning algorithm of pairwise-coupled sparse Bayesian extreme learning machine (PC-SBELM) are more concise and effective, it could identify the singleand simultaneous-fault more quickly and precisely when compared with traditional identification techniques such as pairwise-coupled probabilistic neural networks (PC-PNN) and pairwise-coupled relevance vector machine (PC-RVM). In this case study, PC-SBELM is applied to build a real-time multi-fault diagnostic system. To verify the effectiveness of the proposed fault diagnostic framework, it is carried out on a real wind turbine gearbox system. The evaluation results show that the proposed framework can detect multi-fault in wind turbine gearbox much faster and more accurately than traditional identification techniques. INDEX TERMS Wind turbine, gearbox, multi-fault diagnosis, Hilbert-Huang transform, pairwise-coupled, sparse Bayesian extreme learning machine.

A reliable fault diagnostic system for gas turbine generator system (GTGS), which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

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Robustness against parameter mismatches and position-sensorless operation are two important research topics for permanent magnet synchronous motor (PMSM) drives. In this paper, a sliding mode disturbance observer (SMDO) is proposed to achieve either of two functions for different application environments: 1) if a position sensor is equipped, accurate current regulation can be achieved by deadbeat predictive current control (DBPC) despite mismatched motor parameters; 2) if the position sensor is not equipped but with a good estimation of motor parameters, the observer can serve as a back electromotive force (EMF) estimator. On this basis, the rotor position can be extracted for positionsensorless control. Usually, low-pass filter is required to suppress high frequency noises in conventional sliding mode observer. This inevitably leads to phase delay in the estimation, which cannot be directly used for disturbance compensation. While in the proposed method, a complex coefficient filter is inherently embedded, which can provide accurate estimation without phase or magnitude error. Experimental results obtained from a 2.4 kW PMSM drive platform indicate that high performance current control can be achieved with good robustness for position sensor based operation. And, rotor position can be accurately estimated with good steady and dynamic performance for position-sensorless operation.

In this paper, a grid voltage sensorless model predictive control is proposed based on a sliding mode virtual flux observer (SMVFO). The proposed SMVFO shows good inherent filtering capacity, and thus there is no high-frequency chattering problem. In addition, the proposed SMVFO is designed based on the closed-loop current estimation. Not only is DC-drift issue solved but also dynamic response is enhanced when compared with the prior open-loop virtual flux observer. To verify the effectiveness of the presented SMVFO, it is further integrated into finite control set-model predictive control (FCS-MPC) for pulse width modulator (PWM) rectifiers. The whole control algorithm features simplicity and improved costeffectiveness due to the absence of modulator and grid voltage sensors. As the SMVFO can predict current at the next sampling instant while estimating virtual flux accurately, the proposed SMVFO assisted FCS-MPC is comparable to its counterpart using measured grid voltage. The simulation and experimental tests were carried out on a two-level voltage source PWM rectifier to validate the effectiveness of the proposed method.INDEX TERMS Predictive power control, voltage sensorless, sliding mode observer, PWM rectifier.

This paper presents a robust deadbeat predictive power control (DPPC) for PWM rectifiers with consideration of parameter mismatches under unbalanced grid conditions. Firstly, conventional DPPC is modified to extend its application to both ideal and unbalanced grid conditions. Secondly, tracking error of the modified DPPC with inaccurate grid-side impedance is analyzed. Thirdly, a discrete-time power disturbance observer (DPDO) is designed to achieve accurate power control with mismatched parameters. The designed DPDO can predict complex power at the next sampling instant and estimate system disturbance simultaneously. Therefore, the DPDO can contribute to eliminate steady-state tracking error resulting from disturbances caused by inaccurate parameters and compensate onestep delay in digital implementation. Although satisfactory steady-state performance can be obtained with modified DPPC and DPDO, transient performance still deteriorates significantly with inaccurate value of grid-side inductance. Thus, an online adaptive method to estimate mismatched inductance is finally developed based on the proposed DPDO. Both DPPC and DPDO are implemented in the stationary reference frame without coordinate transformation. Theoretical analysis confirms that the proposed DPDO can track disturbance without phase lag or magnitude error. Experimental tests and comparative studies with a prior DPPC on a two-level PWM rectifier validate the effectiveness of the proposed scheme.