Electronics

Induction motors are critical components for most industries and the condition monitoring has become necessary to detect faults. There are several techniques for fault diagnosis of induction motors and analyzing the startup transient vibration signals is not as widely used as other techniques like motor current signature analysis. Vibration analysis gives a fault diagnosis focused on the location of spectral components associated with faults. Therefore, this paper presents a comparative study of different time-frequency analysis methodologies that can be used for detecting faults in induction motors analyzing vibration signals during the startup transient. The studied methodologies are the time-frequency distribution of Gabor (TFDG), the time-frequency Morlet scalogram (TFMS), multiple signal classification (MUSIC) and Fast Fourier transform (FFT). The analyzed vibration signals are: one broken rotor bar, two broken bars, unbalance and bearing defects. The obtained results shown the feasibility of detecting faults in induction motors using the time-frequency spectral analysis applied to vibration signals, and the proposed methodology is applicable when it does not have current signals and only has vibration signals. Also, the methodology has applications in motors that are not fed directly to the supply line, in such cases the analysis of current signals is not recommended due to poor current signal quality.

Automatic curve fitting using splines has been widely used in data analysis and engineering applications. An important issue associated with data fitting by splines is the adequate selection of the number and location of the knots, as well as the calculation of the spline coefficients. Typically, these parameters are estimated separately with the aim of solving this non-linear problem. In this paper, we use a hierarchical genetic algorithm (HGA) to tackle the B-spline curve fitting problem. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots, and the B-spline coefficients automatically and simultaneously. Our approach is able to find optimal solutions with the fewest parameters within the Bspline basis functions. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth functions and comparison with a successful method from the literature have been included.

Data fitting by splines in noise presence, has been widely used in data analysis and engineering applications. In this regard, an important problem associated with data fitting by splines is the adequate selection of the number and location of the knots, as well as the calculation of the splines coefficients. Typically, these parameters are separately estimated in the aim of solving this non-linear problem. In this paper, we use a hierarchical genetic algorithm to tackle the data fitting problem by B-splines. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, thus, allowing us to determine the number and location of the knots, and the B-spline coefficients automatically and simultaneously. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, numerical results from tests on smooth functions have been included.

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This paper presents a new image segmentation scheme based on active contours guided by the optimization techniques Particle Swarm Optimization (PSO) and Differential Evolution (DE), independently. The scheme uses the optimization methods over a polar coordinate system to perform the segmentation task increasing the energyminimizing capability regarding the traditional active contour model. This proposed model is applied in the segmentation of the human heart from datasets of sequential Computed Tomography images. In addition, to obtain a quantitative and qualitative evaluation of the segmentation results compared to regions outlined by experts, different similarity metrics have been adopted. The experimental results demonstrate that by using PSO or DE, the proposed scheme outperforms the traditional implementation of active contour model in terms of stability and efficiency, achieving a high accuracy segmentation regarding the ground truth.