FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

Chemical Characterization and Assessment of Public Health Risk due to Inhalation of PM2.5 in the City of Salamanca, Guanajuato.

In this study, we conducted an analysis of health risks faced by residents of Salamanca, Mexico, who were exposed to fine particulate matter with a diameter of 2.5 µm (PM2.5) through inhalation. The characterization and analysis of these particulate matter samples were undertaken. A total of 131 samples were collected from two different sites: 65 from the Red Cross site (RC) and 66 from the Integral Family Development site (DIF) in 2014-2015. These samples were analyzed for a set of chemical components, including metals and ions. Non-cancerous health risk levels associated with PM2.5 exposure through the human respiratory system, as per the WHO benchmark (assigned a value of 1), revealed notable risk values for two elements: Manganese (Mn) with a range of 1.19-2.12 in the adult population and 1.59-2.84 in the child population, and Nickel (Ni) with a uniform risk value of 1.39 for both evaluated population groups. However, concerns arose regarding potential non-cancerous effects as the cumulative risk levels for various assessed elements showed elevated indices. These ranged from 3.81 to 4.4 in adults and 4.48-5.24 in children. This study provided comprehensive data on composition and its potential impact on human health, offering valuable insights for the implementation of mitigation measures aimed at reducing inhalation-related exposure.

Statistical Interdependence between Daily Precipitation and Extreme Daily Temperature in Regions of Mexico and Colombia.

We study the statistical interdependence between daily precipitation and daily extreme temperature for regions of Mexico (14 climatic stations, period 1960-2020) and Colombia (7 climatic stations, period 1973-2020) using linear (cross-correlation and coherence) and nonlinear (global phase synchronization index, mutual information, and cross-sample entropy) synchronization metrics. The information shared between these variables is relevant and exhibits changes when comparing regions with different climatic conditions. We show that precipitation and temperature records from La Mojana are characterized by high persistence, while data from Mexico City exhibit lower persistence (less memory). We find that the information exchange and the level of coupling between the precipitation and temperature are higher for the case of the La Mojana region (Colombia) compared to Mexico City (Mexico), revealing that regions where seasonal changes are almost null and with low temperature gradients (less local variability) tend to display higher synchrony compared to regions where seasonal changes are very pronounced. The interdependence characterization between precipitation and temperature represents a robust option to characterize and analyze the collective dynamics of the system, applicable in climate change studies, as well as in changes not easily identifiable in future scenarios.

Measurement of Cutting Temperature in Interrupted Machining Using Optical Spectrometry.

This research presents an experimental study focused on measuring temperature at the tool flank during the up-milling process at high cutting speed. The proposed system deals with emissivity compensation through a two-photodetector system and during calibration. A ratio pyrometer composed of two photodetectors and a multimode fiber-optic coupler is employed to capture the radiation emitted by the cutting insert. The pyrometer is calibrated using an innovative calibration system that addresses theoretical discrepancies arising from various factors affecting the measurement of cutting temperature. This calibration system replicates the milling process to generate a calibration curve. Experimentally, AISI 4140 steel is machined with coated tungsten carbide inserts, using cutting speeds of 300 and 400 m/min, and feed rates of 0.08 and 0.16 mm/tooth. The results reveal a maximum recorded cutting temperature of 518 °C and a minimum of 304 °C. The cutting temperature tends to increase with higher cutting speeds and feed rates, with cutting speed being the more influential factor in this increase. Both the pyrometer calibration and experimental outcomes yield satisfactory results. Finally, the results showed that the process and the device prove to be a convenient, effective, and precise method of measuring cutting temperature in machine processes.

Environmental and biological monitoring of organochlorine pesticides in the city of Salamanca, Mexico.

The former Tekchem Industrial Unit located in the city of Salamanca, Mexico, constitutes an environmental liability in which the presence of high levels of organochlorine pesticides (OCPs) has been reported. In the present study, levels of OCPs were quantified using gas chromatography-mass spectrometry in 52 soil samples and in 88 blood samples from school-age children in the city of Salamanca. A median concentration of 70.6 ng/g (6.93-3299) was obtained for total OCPs in soil, while for the total sum of dichlorodiphenyltrichloroethane (DDT) the value was 49.6 ng/g (6.93-3276). In children, the median level of the total sum of OCPs was 390 ng/g lipid (7.34-14,895), and for the total sum of DDT was 175 ng/g lipid (< LOD-14,802). The OCPs that resulted in highest concentrations in soil were DDT and its metabolites, as well as aldrin and heptachlor epoxide, while in blood the highest levels corresponded to 4,4'-dichlorodiphenyltrichloroethane (4,4'-DDT) and its metabolites, followed by heptachlor and heptachlor epoxide. The spatial distribution of the concentrations of OCPs in soil shows that the facilities of Tekchem may be a significant potential source for the dispersion of these compounds toward the metropolitan area of Salamanca. The results obtained in the present study demonstrate the presence of OCPs in soil and in child population, providing important bases to study the problem from a broader perspective, while reiterating the importance of continuing efforts to generate resolute and precautionary measures with respect to the environmental liability of Tekchem.

Automatic Classification of Rotor Faults in Soft-Started Induction Motors, Based on Persistence Spectrum and Convolutional Neural Network Applied to Stray-Flux Signals.

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.

Infrared Thermography Smart Sensor for the Condition Monitoring of Gearbox and Bearings Faults in Induction Motors.

The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed in which sensors are the key to providing relevant information to verify the system. Recently, the smart sensor concept is common, in which the sensors are integrated with a data processing unit executing dedicated algorithms used to generate meaningful information about the system in situ. Additionally, infrared thermography has gained relevance in monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing, the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way in industrial applications. Therefore, in this manuscript, the structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: an infrared primary sensor, a preprocessing module, an image processing module, classification of faults, and a user interface. The infrared primary sensor considers a low-cost micro thermal camera for acquiring the thermal images. The processing modules and the classification module implement the data processing algorithms into digital development boards, enabling smart system characteristics. Finally, the interface module allows the final users to require the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be provided by the system. The smart sensor is validated in a real experimental test bench, demonstrating its capabilities in different case studies.

System for Tool-Wear Condition Monitoring in CNC Machines under Variations of Cutting Parameter Based on Fusion Stray Flux-Current Processing.

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.

Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis.

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.

Multifractality of Pseudo-Velocities and Seismic Quiescence Associated with the Tehuantepec M8.2 EQ.

By using earthquake catalogs, previous studies have reported evidence that some changes in the spatial and temporal organization of earthquake activity are observed before and after of a main shock. These previous studies have used different approaches for detecting clustering behavior and distance-events density in order to point out the asymmetric behavior of foreshocks and aftershocks. Here, we present a statistical analysis of the seismic activity related to the M w = 8.2 earthquake that occurred on 7 September 2017 in Mexico. First, we calculated the inter-event time and distance between successive events for the period 1 January 1998 until 20 October 2017 in a circular region centered at the epicenter of the M w = 8.2 EQ. Next, we introduced the concept of pseudo-velocity as the ratio between the inter-event distance and inter-event time. A sliding window is considered to estimate some statistical features of the pseudo-velocity sequence before the main shock. Specifically, we applied the multifractal method to detect changes in the spectrum of singularities for the period before the main event on 7 September. Our results point out that the multifractality associated with the pseudo-velocities exhibits noticeable changes in the characteristics of the spectra (more narrower) for approximately three years, from 2013 until 2016, which is preceded and followed by periods with wider spectra. On the other hand, we present an analysis of patterns of seismic quiescence before the M w = 8.2 earthquake based on the Schreider algorithm over a period of 27 years. We report the existence of an important period of seismic quietude, for six to seven years, from 2008 to 2015 approximately, known as the alpha stage, and a beta stage of resumption of seismic activity, with a duration of approximately three years until the occurrence of the great earthquake of magnitude M w = 8.2 . Our results are in general concordance with previous results reported for statistics based on magnitude temporal sequences.

Availability of Internet-Based Cognitive-Behavioral Therapies for Depression: A Systematic Review.

We examined the availability and components of internet-based cognitive-behavioral therapies (iCBTs) for depression tested in randomized-controlled trials (RCTs). The objectives of this literature review were to determine the extent to which research-validated iCBTs were available to the public, as well as to determine their therapeutic content. A literature review of RCTs for iCBTs was conducted on July 30, 2021. For each iCBT, interventions were rated by content and compared to commercially available smartphone apps. Our search yielded 80 studies using 41 unique iCBTs. Of these, only 6 (15%) were completely available to the public, more than half were not publicly available (46%), and the remaining 39% were available to the public with some restrictions (e.g., those based on the user's geographical location). When comparing iCBTs evaluated in RCTs to commercially available smartphone apps, we found that iCBTs were more likely to contain psychoeducation, cognitive restructuring, behavioral activation, problem solving, and interpersonal communication components. iCBTs from RCTs contain evidence-based content but few are available to the public. Extending beyond efficacy, attention should be paid to the dissemination of iCBTs.

Assessment of CFH and HTRA1 polymorphisms in age-related macular degeneration using classic and machine-learning approaches.

BACKGROUND: CFH and HTRA1 are pivotal genes driving increased risk for age-related macular degeneration (AMD) among several populations. Here, we performed a hospital-based case-control study to evaluate the effects of three single nucleotide polymorphisms (SNPs) among Hispanics from Mexico. MATERIALS AND METHODS: 122 cases and 249 controls were genotyped using Taqman probes. Experienced ophthalmologists diagnosed AMD following the American Association of Ophthalmology guidelines. We studied CFH (rs1329428, rs203687) and HTRA1 (rs11200638) SNPs thoroughly by logistic regression models (assuming different modes of inheritance) and machine learning-based methods (ML). RESULTS: HTRA1 rs11200638 is the most significant polymorphism associated with AMD in our studied population. In a multivariate regression model adjusted for clinically and statistically meaningful covariates, the A/G and A/A genotypes increased the odds of disease by a factor of 2.32 and 7.81, respectively (P < .05) suggesting a multiplicative effect of the polymorphic A allele. Furthermore, this observation remains statistically meaningful in the allelic, dominant, and recessive models, and ML algorithms. When stratifying by phenotype, this polymorphism was significantly associated with increased odds for geographic atrophy (GA) in a recessive mode of inheritance (12.4, p < .05). CONCLUSIONS: In sum, this work supports a strong association between HTRA1 genetic variants and AMD in Hispanics from Mexico, especially with GA. Moreover, ML was able to replicate the results of conventional biostatistics methods unbiasedly.

Photophysics of 1-aminonaphthalene: a theoretical and time-resolved experimental study.

The photophysics of 1-aminonaphthalene (1-napthylamine, AMN) has been investigated on the basis of a constructive experimental-theoretical interplay derived from time-resolved measurements and high-level quantum-chemical ab initio CASPT2//CASSCF calculations. Transient ionization signals at femtosecond resolution were collected for AMN cold isolated molecules following excitation from the vibrationless ground level to a number of vibrational states (within the pump resolution) in the lowest accessible excited state and further multiphoton ionization probing at 500, 800, and 1300 nm. Theory predicts two pipi* states, (1)L(b) and (1)L(a), as the lowest singlet electronic excitations, with adiabatic transitions from S(0) at 3.50 and 3.69 eV, respectively. Since the associated oscillator strength for the lowest transition is exceedingly small, the (1)L(b) state is not expected to become populated significantly and the (1)L(a) state appears as the main protagonist of the AMN photophysics. Though calculations foresee a surface crossing between (1)L(a) and the lower (1)L(b) states, no dynamical signature of it is observed in the time-dependent measurements. In the relaxation of (1)L(a), the radiant emission competes with the intersystem crossing and internal conversion channels. The rates of these mechanisms have been determined at different excitation energies. The internal conversion is mediated by a (1)L(a)/S(0) conical intersection located 0.7 eV above the (1)L(a) minimum. The relaxation of a higher-lying singlet excited state, observed above 40 000 cm(-1) (4.96 eV) and calculated at 5.18 eV, has been also explored.

Molecular basis of DNA photodimerization: intrinsic production of cyclobutane cytosine dimers.

Based on CASPT2 results, the present contribution establishes for the first time that cytosine photodimer formation (C< >C) is mediated along the triplet and singlet manifold by a singlet-triplet crossing, (T1/S0)X, and by a conical intersection, (S1/S0)CI, respectively. The former can be accessed in a barrierless way from a great variety of photochemical avenues and exhibits a covalent single bond between the ethene C6-C6' carbon atoms of each monomer. The efficiency of the stepwise triplet mechanism, however, would be modulated by the effectiveness of the intersystem crossing mechanism. The results provide the grounds for the understanding of the potential photogenotoxicity of endogenous and exogenous compounds via triplet-triplet sensitization, with a lower bound for cytosine oligonucleotides predicted to be 2.70 eV, and give support to the traditional view of the primary role of triplet excited states in the photochemistry of DNA, a well-known source of photoproducts in solution under triplet photosensitization conditions. The function played by singlet excimers (excited dimers) to explain both the red-shifted fluorescence and photoreaction is highlighted. A rationale on the pronounced wavelength dependence of the observed fluorescence is offered. Geometrical arrangements at the time of light irradiation close to, but energetically above, (S1/S0)CI are suggested as reactive orientations that become prone to produce C< >C directly, with no energy barrier. Because of the outstanding intrinsic ability of cytosine to form stable relaxed excimers, the system located near the bound relaxed excimer has to accumulate enough vibrational energy to surmount a small barrier of 0.2 eV to reach (S1/S0)CI, making the overall process to proceed at a slower relative rate as compared to other compounds such as thymine, which is not susceptible of forming so stable excimers.

Theoretical insight into the intrinsic ultrafast formation of cyclobutane pyrimidine dimers in UV-irradiated DNA: thymine versus cytosine.

The higher formation yields measured in the ultrafast photoinduced formation of cyclobutane thymine dimers (T<>T) with respect to those of cytosine (C<>C) are explained, on the basis of ab initio CASPT2 results, by the existence in thymine of more reactive orientations and a less efficient photoreversibility, whereas in cytosine the funnel toward the photolesion becomes competitive with that mediating the internal conversion of the excited-cytosine monomer.

Intraluminal-restricted 17 beta-estradiol exerts the same myocardial protection against ischemia/reperfusion injury in vivo as free 17 beta-estradiol.

Several in vitro studies show that in animals and isolated cells, 17 beta-estradiol induces cardiovascular protective effects and it has also been observed that it reduces coronary heart disease risk. However, the use of estrogens to improve or protect cardiovascular function in humans has been controversial, this might be explained by the wide variety of effects, because estrogen receptors (ER) are expressed ubiquitously. Therefore, a cell-specific targeting therapeutic approach might be necessary. 17 beta-Estradiol was coupled to a large modified dextran through an aminocaproic spacer. For this study we used intact and gonadectomized male Wistar rats, 15 days after surgical procedure. Intravascular administration of 17 beta-estradiol-macromolecular conjugate, prior to coronary reperfusion diminishes the area of damage induced by coronary ischemia reperfusion (I/R) injury on an in vivo model. This effect was observed at 17 beta-estradiol sub-physiological concentrations [0.01 nmol/L], it is mediated by luminal endothelial ER alpha activation. 17 beta-Estradiol-macromolecular conjugate decreases phosphorylation level of PKC alpha and Akt, as part of the process to induce myocardial protection against coronary I/R. We proved that the hormone-macromolecular conjugate labeled with [3H]estradiol remained confined in the intravascular space the conjugate was not internalized into organs like heart, lung or liver. It is noteworthy that the 17 beta-estradiol-macromolecular conjugate has a slow renal elimination, which might increase its pharmacological advantage. We concluded that the stimulus of endothelial estrogen receptors is enough to decrease the myocardial damage induced by coronary reperfusion.

Embryonary Mouse Cardiac Fibroblast Isolation.

Mouse cardiac fibroblasts have been widely used as an in vitro model for studying fundamental biological processes and mechanisms underlying cardiac pathologies, as well as identifying potential therapeutic targets. Cardiac FBs are relatively easy to culture in a dish and can be manipulated using molecular and pharmacological tools. Because FBs rapidly decrease cell cycle division and proliferative rate after birth, they are prone to phenotypic changes and senescence in cell culture soon after a few passages. Therefore, primary cultures of differentiated fibroblasts from embryos are more desirable. Below we will describe a method that provides good cell yield and viability of E16 CD-1 mouse embryonic cardiac fibroblasts in primary cultures.