This paper presents a discrete-time variablestructure-based control and a speed estimator designed for a permanent-magnet synchronous motor (PMSM). A cascade control scheme is proposed which provides accurate speed tracking performance. In this control scheme the speed estimator is a robust digital differentiator that provides the first derivative of the encoder position measurement. The analysis of the control stability is given and the ultimate boundedness of the speed tracking error is proved. The control scheme is experimentally tested on a commercial PMSM drive. Reported experimental evidence shows that the proposed solution produces good speed trajectory tracking performance and it is robust in the presence of disturbances affecting the system.
This paper deals with the problem of fault detection and diagnosis of induction motor based on motor current signature analysis. Principal component analysis is used to reduce the three-phase current space to a 2-D space. Kernel density estimation (KDE) is adopted to evaluate the probability density functions of each healthy and faulty motor, which can be used as features in order to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two probability distributions, and it allows automatic fault identification. The aim is also to improve computational performance in order to apply online a monitoring system. KDE is improved by fast Gaussian transform and a points reduction procedure. Since these techniques achieve a remarkable computational cost reduction with respect to the standard KDE, the algorithm can be used online. Experiments are carried out using two alternate current motors: an asynchronous induction machine and a single-phase motor. The faults considered to test the developed algorithm are cracked rotor, out-of-tolerance geometry rotor, and backlash. Tests are carried out at different load and voltage levels to show the proposed method performance.
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