The enhancement of the machine condition monitoring process is a key issue for reliability improvement. In fact, in order to produce quickly, economically, with high quality while decreasing the risk of production break due to a machine stop, it is necessary to maintain the equipment in a good operational condition. This requirement can be satisfied by implementing appropriate maintenance strategies such as Condition Based Maintenance (CBM) and using updated condition monitoring technologies for faults detection and classification. In this context, a new method for machinery condition monitoring based on Mel-Frequency Cepstral Coefficients (MFCCs) and Support Vector Machine (SVM) is proposed to automatically detect the mechanical faults by maximized the generalization ability. Hence, the purpose is to design an automatic detection system for mechanical components defects based on supervised classification by trained to maximize the margin. The proposed approach consists in a sequence of binary classifications after extracting a set of relevant features such as temporal indicators and MFCC coefficients. The diagnosis accuracy assessment is carried out by conducting various experiments on acceleration signals collected from a rotating machinery under different operating conditions.
This article discusses the dynamic modeling for control of Gough–Stewart platform manipulator with special emphasis on universal–prismatic–spherical leg kinematics. Inverse dynamic model of these six degrees of freedom parallel manipulator robots is reviewed, while complete dynamics with true kinematics of universal–prismatic–spherical legs is compared with several models found in the literature. Most existing models have not taken into account some of the legs kinematical effects, namely the legs angular velocity around their axes and the internal singularities due to passive joints; some other used a simplified parameterization to describe the leg kinematics. Furthermore, some kinetic assumption can be used to reduce the computational burden. This article shows the effect of all these simplifications on the driving forces by simulating the different dynamic models for a commercial manipulator and for different sets of geometric and dynamic parameters of manipulator.
This article deals with the problem of planning optimal trajectories for a GOUGH parallel robot. The planning process consists of searching for a motion ensuring the accomplishment of the assigned task, minimizing a cost function and satisfying various constraints inherent to the robot kinematics and dynamics. This problem is treated via (i) an adequate parameterization of the operational coordinates of the mobile platform and (ii) the use of the sequential quadratic programming method for solving the resulting nonlinear optimization problem. The proposed approach is applied to tasks involving either point-to-point motions or motions along specified paths.
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