Luis A. Morales-Hernandez

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.

A Hilbert Transform-Based Smart Sensor for Detection, Classification, and Quantification of Power Quality Disturbances

Power quality disturbance (PQD) monitoring has become an important issue due to the growing number of disturbing loads connected to the power line and to the susceptibility of certain loads to their presence. In any real power system, there are multiple sources of several disturbances which can have different magnitudes and appear at different times. In order to avoid equipment damage and estimate the damage severity, they have to be detected, classified, and quantified. In this work, a smart sensor for detection, classification, and quantification of PQD is proposed. First, the Hilbert transform (HT) is used as detection technique; then, the classification of the envelope of a PQD obtained through HT is carried out by a feed forward neural network (FFNN). Finally, the root mean square voltage (Vrms), peak voltage (Vpeak), crest factor (CF), and total harmonic distortion (THD) indices calculated through HT and Parsevals theorem as well as an instantaneous exponential time constant quantify the PQD according to the disturbance presented. The aforementioned methodology is processed online using digital hardware signal processing based on field programmable gate array (FPGA). Besides, the proposed smart sensor performance is validated and tested through synthetic signals and under real operating conditions, respectively.

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.

Thermographic technique as a complement for MCSA in induction motor fault detection

Recently, mechanical condition monitoring in induction motors has become an important research area because of its relevance in different industrial applications. Infrared thermography has been considered for improving the monitoring of induction motors with the advantage of being a non-invasive technique and having a wide range of analysis. In this work, infrared thermography is used as complementary tool for motor current signature analysis (MCSA) under three common mechanical faults: bearing defects, unbalanced mass and misalignment, based on thermographic image segmentation and statistical feature extraction under the segments of interest. Results show the overall performance of the proposed technique as a complement in induction motor monitoring of mechanical faults.

New approach for automatic tool selection in computer numerically controlled lathe by applying image processing

Tool selection is a very important step in manufacturing processes so as to improve productivity with high quality. The contribution of this work is the development of a new method for automatic tool selection in computer numerical control lathe machines, based on image processing techniques and information of the boundary of the piece, provided by either a .DXF file (drawing exchange format) or from an image taken with other devices. The proposed method detects the preferential direction in the boundary of the piece and creates a directional field through a directional gradient aiming at selecting the correct tool. Results from experiments show that the method makes it possible to work with a resolution of 1.1 micrometers, and to obtain good performance in automatic tool selection when several types of two-dimensional parts in the image are processed.

Self-adjustment methodology of a thermal camera for detecting faults in industrial machinery

Industrial machinery makes extensive use of induction motors as primary motion supplies for the associated kinematic chain. These motors and the kinematic chain are susceptible to failures in one or several of the components making the detection of the faults a major issue for industries. Thermography is a technique that has been used for monitoring and diagnosis in industrial facilities and it is suitable for the monitoring of induction motors and the associated kinematic chain. This technique is an aid for the detection of faults and the diagnosis of the operating condition of industrial machinery. Several research works have used thermography for this purpose, but the problem is the manual adjustment that needs to be done to the thermal camera in order to obtain thermal images, named thermograms, that give the true temperature readings of the objects in focus. This paper presents a novel methodology that makes the adjustment of the thermal camera in an automated way, using additional external temperature sensors to calibrate the thermal images provided by the low-cost thermal camera to give readings of the true temperature of the objects. Experimentation is performed on an induction motor with an associated kinematic chain to test the efficiency of the proposed methodology.

Methodology for thermal analysis of induction motors with infrared thermography considering camera location

Induction motors and electric machines in general are important components in most industries. The condition monitoring of these machines is relevant at the industrial facilities for reducing costs and minimizing maintenance downtime. Infrared thermography is a non-invasive technique that allows on-line monitoring of electric machines; characteristic that makes it a suitable solution for industrial environments. However, some external parameters like the camera positioning and location can affect the estimations carried out by the camera. This work presents a methodology for thermal analysis on induction motors, showing how the location of the camera affects the thermography imaging process. Images are taken from different locations, relative to the monitored machine, and a comparative of the results is carried out for an experimental case of study consisting in a healthy induction motor and a motor with a damaged bearing. Results show that despite the fact that a change on the camera location produces a variation on the estimation of the temperature; the difference is not significant for applications on electric machines.

Nonlinear identification of inverted pendulum system using Volterra polynomials

ABSTRACT The inverted pendulum is one of the most significant problems in control theory and has been studied in control literatures. The nonlinear model is useful in the control design. In the present work, Volterra polynomial basis function networks have been used to identify a single inverted pendulum on a moving cart system. Here, the nonlinear model of the inverted pendulum has been implemented. The offline structure selection through orthogonal least square algorithm is used for the nonlinear system identification via the basis function selection of Volterra polynomial networks. The results show good matching between predicted and actual outputs.

A new method for inpainting of depth maps from time-of-flight sensors based on a modified closing by reconstruction algorithm

Abstract Time-of-Flight (ToF) sensors are popular devices that extract 3D information from a scene but result to be susceptible to noise and loss of data creating holes and gaps in the boundaries of the objects. The most common approaches to tackling this problem are supported by color images with good results, however, not all ToF devices produce color information. Mathematical morphology provides operators that can manage the problem of noise in single depth frames. In this paper, a new method for the filtering of single depth maps, when no color image is available, is presented, based on a modification to the morphological closing by reconstruction algorithm. The proposed method eliminates noise, emphasizing a high contour preservation, and it is compared, both qualitative and quantitatively, with other state-of-the-art filters. The proposed method represents an improvement to the closing by reconstruction algorithm that can be applied for filter depth maps of ToF devices.

An Approach Based on the Exploratory Data Analysis to Relate the Wear Behavior with the Microstructure of Ductile Cast Irons

The aim of this work is to propose a new methodology to relate Ductile Cast Irons (DCIs) wear behavior with the separation distances and sizes of the graphite nodules through an Exploratory Data Analysis (EDA). This methodology consists of morphological image processing tools (compacity and size distribution curves), an EDA performed by the use of box plots and an EDA-based section classifying algorithm. This algorithm classifies the microstructure of DCIs into classes and levels grouping different behaviors of the separation distances and sizes of graphite nodules. Finally, it was found, through a number of tribological tests, that the obtained classes and levels have a different wear behavior. The results achieved by this methodology were compared with those of traditional techniques used to characterize the microstructure of the material.