Hayde Peregrina-Barreto

FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology

Broken bars detection on induction motors has been a topic of interest in recent years. Its detection is important due to the fact that the failure is silent and the consequences it produces as power consumption increasing, vibration, introduction of spurious frequencies in the electric line, among others, can be catastrophic. In this paper, the use of motor current signature analysis and mathematical morphology to detect broken bars on induction motors under different mechanical load condition is analyzed. The proposed algorithm first identifies the motor load and then the motor condition. The statistical analysis of several tests under different motor loads (100%, 75%, 50%, and 25%) and motor condition (healthy, one broken bar, and two broken bars) is presented. The proposed method has been implemented in a field programmable gate array, to be used in real-time online applications. The algorithm obtained in average a 95% accuracy of failure detection.

Quantitative Estimation of Temperature Variations in Plantar Angiosomes: A Study Case for Diabetic Foot

Thermography is a useful tool since it provides information that may help in the diagnostic of several diseases in a noninvasive and fast way. Particularly, thermography has been applied in the study of the diabetic foot. However, most of these studies report only qualitative information making it difficult to measure significant parameters such as temperature variations. These variations are important in the analysis of the diabetic foot since they could bring knowledge, for instance, regarding ulceration risks. The early detection of ulceration risks is considered an important research topic in the medicine field, as its objective is to avoid major complications that might lead to a limb amputation. The absence of symptoms in the early phase of the ulceration is conceived as the main disadvantage to provide an opportune diagnostic in subjects with neuropathy. Since the relation between temperature and ulceration risks is well established in the literature, a methodology that obtains quantitative temperature differences in the plantar area of the diabetic foot to detect ulceration risks is proposed in this work. Such methodology is based on the angiosome concept and image processing.

Thermal image processing for quantitative determination of temperature variations in plantar angiosomes

Thermal image analysis has been extended to a wide variety of application in the medicine field. Thermography and image processing are useful tools because they facilitate a noninvasive and fast diagnostic. In the study of the diabetic foot, it is important the early detection of ulceration risks. Such detection becomes difficult, due to the lack of symptoms in the early phase. A late detection may lead to an extremity amputation. In the literature it has been established a relation between temperature and ulceration regions and, in recent works, some qualitative solutions for risk ulceration diagnostic based on a thermal analysis of the plantar foot have been proposed. In this work, an analysis and quantitative comparison of the temperature between the feet by using the concept of angiosome on diabetic patients is proposed. The goal of this study is to provide useful information in the early foot ulcers detection in patients with diabetic neuropathy.

Vibration Analysis of Partially Damaged Rotor Bar in Induction Motor under Different Load Condition Using DWT

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but also how early these failures can be detected. The purpose of this paper is to present an experimental study of partially damaged rotor bar in induction motor under different load conditions based on discrete wavelet transform analysis. The approach is based on the extraction of features from vibration signals at different level of damage and three mechanical load conditions. The proposed analysis is reliable for tracking the damage in rotor bar. The paper presents an analysis and extraction of vibration features for partially damaged rotor bar in induction motors. The experimental analysis shows the change in behavior of vibration due to load condition and progressive damage.

Parameter Identification of PMSMs Using Experimental Measurements and a PSO Algorithm

This paper introduces a particle swarm optimization (PSO) algorithm that uses experimental measurements for the identification of the direct (

Broken bars detection on induction motor using MCSA and mathematical morphology: An experimental study

d

Evaluation of thermal patterns and distribution applied to the study of diabetic foot

) and quadrature (

FPGA implementation of Orthogonal Matching Pursuit algorithm

q

Visualization of deep blood vessels in speckle imaging using homogeneity measurement of the co-occurrence matrix

) stator inductances and the stator resistance of permanent-magnet synchronous machines (PMSMs). Strategies for the identification of stator resistance and flux linkage of permanent magnets are also presented. The developed PSO algorithm has the advantages of fast and stable convergence characteristics, and it is relatively easy to implement; moreover, neither voltage source inverters nor closed-loop control strategies are needed. In this paper, the experimental results obtained by PSO are compared with other ones obtained through a current decay test to demonstrate the effectiveness of the presented method. A 32-bit C2000 real-time microcontroller and a commercial surface-mounted PMSM have been used to carry out the experimental measurements.

Detection and Measurement of the Intracellular Calcium Variation in Follicular Cells

Currently, industry demands early failure detection on his processes, machines, production lines, etc. One of the most widely used motors in industry is the induction motor. A common induction motor failure is the broken bars. It is well know that broken bars produce spurious frequencies around the supply frequency. Moreover, the amplitude of the spurious frequencies in the sideband of the main frequency is sensitive to the number of broken bars. In this paper a real-time pre-processing methodology to enhance detectability for broken bar detection using motor current signature analysis and mathematical morphology is presented. The proposed methodology is implemented into a low cost FPGA. A statistical analysis is presented in order to demonstrate the detection improvement.