Mohamed Boumehraz

Detection of broken rotor bar faults in induction motor at low load using neural network.

The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions.

Improved object tracking via joint color-LPQ texture histogram based mean shift algorithm

In this paper, a new robust mean shift tracker is proposed by utilizing the joint color and texture histogram. The contribution of our work is to take local phase quantization (LPQ) operator advantage of texture features representation, and to combine it with a color histogram mean shift tracking algorithm. The LPQ technique can be applied to obtain the texture features which represent the object. In texture classification, The LPQ operator is much robust to blur than the well-known local binary pattern operator (LBP). Compared with traditional color histogram mean shift algorithm which considers only color statistical information of the object, the joint color-LPQ texture histogram is more robust and overcome some difficulties of the traditional color histogram mean shift algorithm. Comparative experimental results on numerous challenging image sequences show that the proposed algorithm obtains considerably better performance than several state-of-the-art methods, especially traditional mean shift tracker. The algorithm is evaluated by numerical parameters: the center location and the average overlap, it proved the tracking robustness in presence of similar target appearance and background, motion blurring.

Path Following Behavior for an Autonomous Mobile Robot using Neuro-Fuzzy Controller

This paper presents a navigation method for an autonomous mobile robot. In order to equip the robot by capability of autonomy and intelligence in its environment, the control system must perform many complex information processing tasks in real time and it is well suited to use the soft-computing techniques. The objective of this paper is to elaborate an intelligent control system for the path following behavior by mobile robot using a neuro-fuzzy controller. The hybrid approach refers to the way of applying learning techniques offered by neural networks for fuzzy systems parameter identification. The proposed controller is used for pursuing a moving target. Simulation results show the effectiveness of the designed controller.

A novel method for induction motors stator inter-turn short circuit fault diagnosis based on wavelet energy and neural network

This paper presents a wavelet neural network technique for the inter turn short-circuit fault detection and location of the induction machine at non stationary state. This technique is used in order to remedy the problem from using the classical signal-processing (FFT). This method is based from using the discrete wavelet energy (DWE) as the input for the neural network (NN). The fault detection and location are achieved by a feed-forward multilayer-perceptron neural network trained by back propagation. Simulation results are presented to illustrate the effectiveness of the proposed method.