Demands for various products, higher qualities, reduction of costs and competitiveness, have resulted in the use of intelligent fault detection systems. Bearing fault diagnosis as a major component of the electric motors has had an essential role in the operation of production units' reliability. In addition, vibration analysis is one of the most powerful tools in diagnostics. Advances in signal processing technology and electrical equipment have developed a machinery condition monitoring for defect detection. This study has used the extracted features of vibration signals and the adaptive neuro-fuzzy interface system (ANFIS) network proposing a structure for fault detection and diagnosis of rolling bearings. Time-domain and frequency-domain statistical characteristics have been extracted fault information from vibration signals. Besides, the test data sets are presented to the ANFIS network. Simulation results indicated that the performance of the ANFIS network is acceptable. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.

This paper proposes a novel approach for bilateral teleoperation systems with a multi degreesof-freedom (DOF) nonlinear robotic system on the master and slave side with constant time delay in a communication channel. We extend the passivity based architecture to improve position and force tracking and consequently transparency in the face of offset in initial conditions, environmental contacts and unknown parameters such as friction coefficients. The proposed controller employs a stable neural network on each side to approximate unknown nonlinear functions in the robot dynamics, thereby overcoming some limitations of conventional controllers such as PD or adaptive controllers and guaranteeing good tracking performance. Moreover, we show that this new neural network controller preserves the control passivity of the system. Simulation results show that NN con-A. Forouzantabar ( ) troller tracking performance is superior to that of conventional controllers.

In this work we want to propose a control strategy to maximize the wind energy captured in a variable speed wind turbines, for this goal the speed of turbine should keep in optimum speed when the wind speed is changing. Many control approach has been suggested that is base on approximate models that it causes unsuitable behavior of system because of Uncertainty parameters of the system. Hence at this work we use adaptive robust control approach that it can to compensate Uncertain of the parameters and present a smooth system with maximum energy production. Numerical simulations are given to illustrate the effectiveness and validity of the proposed approach.

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