This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on modelling the motor in the dq frame for both health and fault cases to facilitate recognition of motor current. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide an efficient fault diagnosis tool. An artificial intelligence network determines the fault severity values using the stator current history. The performance of the developed fault analysis method is investigated using Matlab/Simulink® software. Stator turns faults are detected through current monitoring of a 2 Hp three phase induction motor under various loading conditions. Fault history is calculated under various loading conditions, and a wide range of fault severity.
Abstract-In this paper, use of Proportional-Integral (PI) controller to monitor and control liquid level in an interconnected CE 105 model coupled tank is investigated. To achieve a system which can instantaneously and accurately control the liquid level in a coupled tank, two different PI controllers have been tested. The LabVIEW library for the PI controller is used to measure liquid levels in the coupled tank. The PI SubVI already exists in the LabVIEW library that gives reasonable performance but to get a better system performance and monitor the liquid levels more accurately another SubVI is derived from the PI controller mathematical equations. The practical results and the system performance of the second SubVI show a faster response and more accurate instantaneous data which minimises the error in the measurements to ±1 mm. Furthermore, the robustness of the controller to change in the system's parameters is also investigated and established.
Perspectives of using sliding mode control in e-learning are discussed. The concepts of variable structure systems and sliding mode control are given. Analysis of convergence based on the second Lyapunov's method is presented. The analysis presented is based on the dynamic models of learning available in the literature. The suitability of the use of sliding mode to adaptation of level of challenge of the tasks in e-learning is demonstrated. It is shown that with frequent enough evaluation of tasks, optimal level of task challenge can be ensured.
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