Jose Rangel-Magdaleno

Novel Methodology for Online Half-Broken-Bar Detection on Induction Motors

The relevance of the development of monitoring systems for rotating machines is not only the ability to detect failures but is also how early these failures can be detected. Squirrel-cage induction motors are the most popular motors used in industry, consuming around 85% of the power in industrial plants. Broken rotor bars in induction motors are among the major failures that are desirable to detect at early stages because this failure significantly increases power consumption and is responsible for further damage to the machinery. Previously reported works base their analysis on current or vibration monitoring for broken-bar detection up to one broken bar under mechanically loaded motor conditions. The contribution of this paper presents a novel methodology for half-broken-bar detection, which combines current and vibration analysis by correlating the signal spectra to enhance detectability for mechanically loaded and unloaded operating conditions of the motor, which the other isolated techniques are unable to detect. The proposed methodology is implemented in a low-cost field-programmable gate array (FPGA), giving a special-purpose system-on-a-chip (SoC) solution for online operation, with the development of a complex postprocessing decision-making unit. Several cases of study are presented to demonstrate the performance of the implementation.

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.

FPGA-Based Vibration Analyzer for Continuous CNC Machinery Monitoring With Fused FFT-DWT Signal Processing

The industry of machine tools keeps a constant progress that responds to the most demanding market requirements, such as production-time decreasing and zero-defect manufacturing. As part of this progress, sensors and monitoring instruments have been integrated on machine tools, improving computer numerical control (CNC) and monitoring during the manufacturing process. The vibrations are one of the most significant variables to be monitored on machine tools since they directly affect the end piece finishing, tool life expectancy, general machine-tool condition, current consumption, etc. Time domain, fast Fourier transform (FFT), and discrete wavelet transform (DWT) are techniques often used for vibration analysis, because they are computationally efficient for online implementation; unfortunately, it is difficult to find an instrument for vibration analysis that allows individually applying the time, FFT, and DWT techniques, and their fusion. Moreover, the lack of instruments with characteristics such as open architecture, low cost, parallel processing, and continuous online monitoring is remarkable. The contribution of this work consists of developing a vibration-analysis instrument based on the time, FFT, DWT, and DWT-FFT fusion techniques. The instrument is implemented into a low-cost field-programmable gate array with proprietary hardware signal-processing cores in parallel for achieving real-time and continuous online analysis. Experimentation is done to show the proposed methodology efficiency and the developed-instrument versatility, focusing on industrial control applications for CNC machine tools.

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.

Novel oversampling technique for improving signal-to-quantization noise ratio on accelerometer-based smart Jerk sensors in CNC applications

Jerk monitoring, defined as the first derivative of acceleration, has become a major issue in computerized numeric controlled (CNC) machines. Several works highlight the necessity of measuring jerk in a reliable way for improving production processes. Nowadays, the computation of jerk is done by finite differences of the acceleration signal, computed at the Nyquist rate, which leads to low signal-to-quantization noise ratio (SQNR) during the estimation. The novelty of this work is the development of a smart sensor for jerk monitoring from a standard accelerometer, which has improved SQNR. The proposal is based on oversampling techniques that give a better estimation of jerk than that produced by a Nyquist-rate differentiator. Simulations and experimental results are presented to show the overall methodology performance.

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 (

FPGA-Matlab-based open core for three-time controllers in automatic control applications

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

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