An optimization-based control strategy for energy efficiency of discrete manufacturing systems

In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. Based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased.Peer ReviewedPostprint (author's final draft

Anomaly detection with a spatio-temporal tracking of the laser spot

Anomaly detection is an important problem with many applications in industry. This paper introduces a new methodology for detecting anomalies in a real laser heating surface process recorded with a high-speed thermal camera (1000 fps, 32×32 pixels). The system is trained with non-anomalous data only (32 videos with 21500 frames). The proposed method is built upon kernel density estimation and is capable of detecting anomalies in time-series data. The classification should be completed in-process, that is, within the cycle time of the workpiece

Dynamic Bayesian network-based anomaly detection for in-process visual inspection of laser surface heat treatment

We present the application of a cyber-physical system for inprocess quality control based on the visual inspection of a laser surface heat treatment process. To do this, we propose a classification framework that detects anomalies in recorded video sequences that have been preprocessed using a clustering-based method for feature subset selection. One peculiarity of the classification task is that there are no examples with errors, since major irregularities seldom occur in efficient industrial processes. Additionally, the parts to be processed are expensive so the sample size is small. The proposed framework uses anomaly detection, cross-validation and sampling techniques to deal with these issues. Regarding anomaly detection, dynamic Bayesian networks (DBNs) are used to represent the temporal characteristics of the normal process. Experiments are conducted with two diferent types of DBN structure learning algorithms, and classification performance is assessed on both anomalyfree examples and sequences with anomalies simulated by experts

Asymmetric HMMs for online ball-bearing health assessments

The degradation of critical components inside large industrial assets, such as ball-bearings, has a negative impact on production facilities, reducing the availability of assets due to an unexpectedly high failure rate. Machine learning- based monitoring systems can estimate the remaining useful life (RUL) of ball-bearings, reducing the downtime by early failure detection. However, traditional approaches for predictive systems require run-to-failure (RTF) data as training data, which in real scenarios can be scarce and expensive to obtain as the expected useful life could be measured in years. Therefore, to overcome the need of RTF, we propose a new methodology based on online novelty detection and asymmetrical hidden Markov models (As-HMM) to work out the health assessment. This new methodology does not require previous RTF data and can adapt to natural degradation of mechanical components over time in data-stream and online environments. As the system is designed to work online within the electrical cabinet of machines it has to be deployed using embedded electronics. Therefore, a performance analysis of As-HMM is presented to detect the strengths and critical points of the algorithm. To validate our approach, we use real life ball-bearing data-sets and compare our methodology with other methodologies where no RTF data is needed and check the advantages in RUL prediction and health monitoring. As a result, we showcase a complete end-to-end solution from the sensor to actionable insights regarding RUL estimation towards maintenance application in real industrial environments.This study was supported partially by the Spanish Ministry of Economy and Competitiveness through the PID2019-109247GB-I00 project and by the Spanish Ministry of Science and Innovation through the RTC2019-006871-7 (DSTREAMS project). Also, by the H2020 IoTwins project (Distributed Digital Twins for industrial SMEs: a big-data platform) funded by the EU under the call ICT-11-2018- 2019, Grant Agreement No. 857191.Peer ReviewedPostprint (author's final draft

Birds of Universidad de los Llanos (Villavicencio, Colombia): a rich community at the andean foothills-savanna transition

Objetivos: Desarrollar el inventario de las aves del campus Barcelona de la Universidad de los Llanos, Villavicencio, Colombia, con el objeto de estimar la riqueza de especies, abundancia y asociaciones de hábitat de la avifauna local. Alcance: Caracterización de la diversidad aviar local y su asociación con diferentes tipos de ecosistemas naturales y transformados. Metodología: Inventariamos la avifauna tomando registros visuales y auditivos semanales entre agosto de 2013 y agosto de 2014, además de observaciones no sistemáticas entre 2013 y 2018. Estimamos la riqueza de especies usando estimadores no paramétricos, y categorizamos las abundancias locales y asociaciones de hábitat con base en la frecuencia de encuentros. Principales resultados: Registramos un total de 189 especies a través de observaciones sistemáticas, además de 21 registradas de manera no sistemática para un total de 210 especies. El listado incluye una especie casi amenazada para Colombia, 20 especies migratorias y cuatro ampliaciones de distribución para la cuenca del Orinoco colombiano. La heterogeneidad de la vegetación mantiene una rica comunidad compuesta principalmente por especies asociadas a zonas urbanas, bosque de galería y lagos artificiales. Muchas especies fueron raras y ocasionales, lo cual sugiere que son visitantes o mantienen pequeñas poblaciones dentro del campus. Conclusiones: Este estudioprovee datos básicos sobre la diversidad de aves en ecosistemas transformados en la cuenca del Orinoco, y resalta la importancia de los mosaicos de sabana, bosque y ecosistemas transformados como refugio y áreas de parada de aves residentes y migratorias.Objectives: To conduct a bird inventory at the Barcelona campus of Universidad de los Llanos Villavicencio, Colombia, with the aim of estimating species richness, abundance and habitat associations of the local avifauna. Scope: Characterization of the local avian diversity and its association with different types of natural and transformed ecosystems. Methodology: We inventoried birds using sight and auditory records made weekly between August 2013 and August 2014, plus opportunistic observations made between 2013 and 2018. We estimated species richness using non-parametric estimates, and categorized local abundances and habitat associations based upon encounter frequencies. Main results: We recorded a total of 210 species (189 species through systematic observations, plus 21 recorded non-systematically). The list includes one Colombian near-endemic, 20 migrant species, and four range extensions for the Orinoco basin. The heterogeneous vegetation sustains a rich community composed mainly by species associated with urban zones, gallery forest and artificial lakes. Most species were rare and occasional, which suggests that they are visitors or maintain small populations within the campus. Conclusions: This study provides basic data on bird diversity of transformed ecosystems in the Orinoco basin, and highlights the importance of mosaics of savanna, forest and transformed ecosystems as refuges and stopover areas of resident and migratory birds

PPAR-γ Gene Expression in Human Adipose Tissue Is Associated with Weight Loss After Sleeve Gastrectomy

[EN] Background The peroxisome proliferator-activated receptor (PPAR)-γ plays a key role in adipose tissue differentiation and fat metabolism. However, it is unclear which factors may regulate its expression and whether obese patients have changes in adipose tissue expression of PPAR-γor potential regulators such as miR-27. Thus, our aims were to analyze PPAR-γ and miR-27 expression in adipose tissue of obese patients, and to correlate their levels with clinical variables. Subjects and Methods. We included 43 morbidly obese subjects who underwent sleeve gastrectomy (31 of them completed 1-year follow-up) and 19 non-obese subjects. mRNA expression of PPAR-γ1 and PPAR-γ2, miR-27a, and miR-27b was measured by qPCR in visceral and subcutaneous adipose tissue. Clinical variables and serum adipokine and hormone levels were correlated with PPAR-γ and miR-27 expression. In addition, a systematic review of the literature regarding PPAR-γ expression in adipose tissue of obese patients was performed. Results We found no differences in the expression of PPAR-γ and miR-27 in adipose tissue of obese patients vs. controls. The literature review revealed discrepant results regarding PPAR-γ expression in adipose tissue of obese patients. Of note, we described a significant negative correlation between pre-operative PPAR-γ1 expression in adipose tissue of obese patients and post-operative weight loss, potentially linked with insulin resistance markers. Conclusion PPAR-γ1 expression in adipose tissue is associated with weight loss after sleeve gastrectomy and may be used as a biomarker for response to surgery.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the following grants to M.M.: ISCIII and FEDER, PI10/01692, PI16/01548, RD16/0017/0023, and I3SNS-INT12/049, L.H.C.: Junta de Castilla y León GRS 681/A/11, J.-L. T.: GRS 1587/A/17 and GRS1356/A/16, G.S.: ERC 260464, EFSD 2030, MICINNSAF2013-43506-R, and Comunidad de Madrid S2010/BMD-2326. G.S. is an investigator of the Ramón y Cajal Program.Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

PPAR-γ Gene Expression in Human Adipose Tissue Is Associated with Weight Loss After Sleeve Gastrectomy

Background: The peroxisome proliferator-activated receptor (PPAR)-γ plays a key role in adipose tissue differentiation and fat metabolism. However, it is unclear which factors may regulate its expression and whether obese patients have changes in adipose tissue expression of PPAR-γor potential regulators such as miR-27. Thus, our aims were to analyze PPAR-γ and miR-27 expression in adipose tissue of obese patients, and to correlate their levels with clinical variables. Subjects and methods: We included 43 morbidly obese subjects who underwent sleeve gastrectomy (31 of them completed 1-year follow-up) and 19 non-obese subjects. mRNA expression of PPAR-γ1 and PPAR-γ2, miR-27a, and miR-27b was measured by qPCR in visceral and subcutaneous adipose tissue. Clinical variables and serum adipokine and hormone levels were correlated with PPAR-γ and miR-27 expression. In addition, a systematic review of the literature regarding PPAR-γ expression in adipose tissue of obese patients was performed. Results: We found no differences in the expression of PPAR-γ and miR-27 in adipose tissue of obese patients vs. controls. The literature review revealed discrepant results regarding PPAR-γ expression in adipose tissue of obese patients. Of note, we described a significant negative correlation between pre-operative PPAR-γ1 expression in adipose tissue of obese patients and post-operative weight loss, potentially linked with insulin resistance markers. Conclusion: PPAR-γ1 expression in adipose tissue is associated with weight loss after sleeve gastrectomy and may be used as a biomarker for response to surgeryThis work was funded by the following grants to M.M.: ISCIII and FEDER, PI10/01692, PI16/01548, RD16/0017/0023, and I3SNS-INT12/049, L.H.C.: Junta de Castilla y León GRS 681/A/11, J.-L. T.: GRS 1587/A/17 and GRS1356/A/16, G.S.: ERC 260464, EFSD 2030, MICINNSAF2013-43506-R, and Comunidad de Madrid S2010/BMD-2326. G.S. is an investigator of the Ramón y Cajal Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ISCIII,PI10/01692,Miguel Marcos,PI16/01548,Miguel Marcos,Gerencia regional de salud,junta de castilla y león,GRS 681/A/11,Lourdes Hernández-Cosido,J.-L. T,Lourdes Hernández-Cosido,Gerencia Regional de Salud,Junta de Castilla y León,GRS 1587/A/17,Jorge-Luis Torres,GRS1356/A/16,Jorge-Luis Torre

Clustering probabilístico dinámico para la búsqueda de patrones de degradación de elementos de máquina en el ámbito del Industrie 4.0

La Cuarta Revolución Industrial y en particular, los sistemas ciberfísicos (CPS), han abierto una amplia gama de oportunidades en términos de análisis de rendimiento. Estas oportunidades pueden ser aplicables a sistemas de diagnóstico y predicción de fallos pero también, pueden contribuir a la mejora del diseño de los productos y a la optimización de los procesos industriales. Las capacidades de comunicación de los CPS a alta velocidad permiten adquirir, pre-procesar y procesar los datos que se extraen, por ejemplo, de las máquinas, pilar fundamental de los procesos productivos. Como resultado, la degradación de los elementos de máquina sujetos a comportamientos dinámicos puede detectarse de una manera más rápida mediante el estudio de los patrones que forman sus principales variables de funcionamiento a lo largo del tiempo. Esto permite generar herramientas de monitorización de elementos productivos, aplicables principalmente al mantenimiento y al control de calidad. Sin embargo, este tipo de aproximaciones trabajan con sensores que envían datos de procesos dinámicos a alta velocidad en donde no es fácil generar información útil en el momento adecuado. Una parte del problema se refiere al procesamiento de una gran cantidad de datos, mientras que los fenómenos dinámicos subyacentes relacionados con la máquina posiblemente evolucionen con el tiempo dando lugar a un concept drift. Esto se debe a factores como la degradación, algo completamente normal en los sistemas físicos. Como resultado, cualquier modelo de datos puede volverse obsoleto y es necesaria su constante actualización. Para hacer frente a este problema, se propone una aproximación desde el aprendizaje automático no supervisado. Específicamente, el uso de algoritmos de clustering dinámicos. Para ello, se trabaja en una metodología que primero estudia el rendimiento de los algoritmos de clustering en aplicaciones industriales. Posteriormente, se seleccionan aquellos algoritmos que tengan la capacidad de aportar nuevo conocimiento relacionado con los elementos productivos y sus patrones de degradación. El siguiente paso es adaptar el algoritmo seleccionado al comportamiento dinámico de las máquinas y al trabajo con data streams, mucho más cercano a la realidad industrial. De esta manera, partiendo de algoritmos de clustering como: -fí-medias, jerárquico aglomerativo, espectral, propagación de afinidad y modelos de mixturas de Gaussianas, se selecciona este último tipo como el más apto para esta aplicación. Se propone un nuevo algoritmo de aprendizaje no supervisado, denominado clustering probabilístico dinámico basado en mixturas de Gaussianas (GDPC). GDPC integra y adapta tres algoritmos conocidos para poder ser usados en escenarios dinámicos: el algoritmo de esperanza-maximización (EM) responsable de estimar los parámetros del modelo de mixturas y el test de hipótesis de Page–Hinkley que junto con las cotas de Chernoff permiten detectar los concept drift. A diferencia de otros métodos no supervisados, el modelo inducido por el GDPC proporciona las probabilidades de asignación de cada instancia a cada clúster o componente. Esto permite determinar, a través de un análisis con el Brier score, la robustez de esta asignación y su evolución una vez detectado un concept drift. El GDPC trabaja con una ventana óptima de datos reduciendo de manera importante las necesidades de potencia de cómputo. Sin embargo, el algoritmo requiere un conocimiento del dominio profundo con el fin de seleccionar correctamente los parámetros (por ejemplo, el número de componentes). Además, puede ser inestable debido a otro fenómeno encontrado comúnmente en datos industriales relacionado con fases no estacionarias que ocurren cuando los elementos cambian de estado y se estabilizan en su valor esperado. De esta manera, se proponen una nueva versión con serie de mejoras con el objetivo de aumentar el grado de robustez del algoritmo ante estas problemáticas. Esta nueva versión, denominada GDPC+, introduce las siguientes mejoras: (a) la selección automática del número de componentes de la mixtura de Gaussianas en función del criterio de información Bayesiano; y (b) la estabilización debida a los efectos transitorios, no estacionarios, durante el concept drift gracias a la integración de la divergencia de Cauchy–Schwarz con el test de Dickey–Fuller aumentado. Por lo tanto, el GDPC+ tiene un mejor desempeño que el GDPC en términos del número de falsos positivos en aplicaciones altamente dinámicas. El desarrollo de estos algoritmos ha sido validado con pruebas sobre data stream de origen sintético y también originados a partir de un banco de pruebas y una máquina-herramienta produciendo piezas reales, en este caso, cigüeñales de automoción. Estos resultados se han validado en términos de diferentes medidas como precisión, recall, especificidad y F-score. Adicionalmente, partiendo de los resultados de clustering de datos reales de máquina, se han desarrollado conjuntos de reglas inducidas mediante un algoritmo de clasificación supervisada con el fin de proporcionar información sobre el proceso subyacente y sus concept drift asociados. ----------ABSTRACT---------- The Fourth Industrial Revolution and in particular, the cyber-physical systems (CPS), have opened a wide range of opportunities in terms of performance analysis. They can be applied to fault diagnosis and prediction systems but also to improve the design of industrial products and processes optimization. In this way, the CPS communication capabilities at high-speed allow us to acquire, pre-process and process the data extracted from, e.g., machines, fundamental part of production processes. As a result, the degradation of machine elements subject to dynamic behavior can be detected more quickly by studying the patterns that produce the main operating variables over time. This allows the generation of fundamental monitoring tools, mainly applicable to maintenance and quality control. However, this type of approach works with sensors that send data from dynamic processes at high speed, where it is not easy to generate actionable insights at the right time. One part of the problem concerns the processing of a large amount of data, while the underlying dynamic phenomena related to the machine, possibly evolve over time giving rise to a concept drift. This is due to factors such as degradation, something common in physical systems. Thus, if the model becomes obsolete, an update is necessary. To deal with this problem, an approach from unsupervised machine learning is proposed. Specifically, through dynamic clustering algorithms. To do this, we work on a methodology that first allows us to study the behavior of clustering algorithms in industrial applications. Then, we select those algorithms that have the capabilities to provide new knowledge related to the productive elements and their degradation patterns. The next step is to adapt the selected algorithm to the dynamic behavior of the machines, working with data streams, much closer to the industrial reality. In this way, starting from clustering algorithms such as: K-means, agglomerative hierarchical, spectral, affinity propagation and Gaussian mixture models, the last one is selected as the most suitable for this type of application. A new unsupervised learning algorithm called Gaussian-based dynamic probabilistic clustering (GDPC) is proposed. GDPC integrates and adapts three known algorithms for use in dynamic scenarios: the expectation–maximization algorithm (EM) responsible for parameter estimation of the mixture model and the Page–Hinkley test together with the Chernoff bounds, to detect concept drift. Unlike other unsupervised methods, the model induced by GDPC provides the membership probabilities of each instance to each cluster or component. This allows us to determine, through an analysis with the Brier score, the membership robustness and its evolution each time a concept drift is detected. In addition, the algorithm works with few data needs and significantly less computing power, which allow the algorithm to decide when to change the model. However, this algorithm requires a thorough knowledge of the analyzed domain to correctly select parameters such as the number of components. Also, it may be unstable due to another common phenomenon found in industrial data related to non-stationary phases. Therefore, a series of improvements are proposed to increase the degree of robustness of the algorithm. This new version, called GDPC+, introduces the following improvements: (a) the automatic selection of the number of components of the mixture based on the Bayesian information criterion; and (b) the stabilization due to the transient effects during the concept drift thanks to the integration of the Cauchy–Schwarz divergence with the augmented Dickey– Fuller test. Therefore, GDPC+ can outperform the GDPC in highly dynamic scenarios in terms of the number of false positives. The development of these algorithms has been supported with tests on synthetic data streams and also data originated on testbeds and a machine-tool during real production, in this case, automotive crankshafts. These results have been validated in terms of different figures of merit like accuracy, recall, specificity and F-score. Additionally, based on the results of clustering of real machine data, sets of rules induced by a supervised algorithm were developed in order to provide insights about the underlying process and its related algorithm

The Barcino as a historical document and of collective memory : representation analysis for an unconventional narrative of violence in Huila from 1946 to 1966

Las reflexiones en torno al análisis y comprensión sobre algunas composiciones que se construyeron como forma de expresión e incluso de denuncia de los hechos ocurridos en los tiempos de La Violencia, y los hechos agudizados que ésta generó en los siguientes años dentro un contexto regional, son algunas de las acciones que el campo de la investigación con enfoque histórico-hermenéutico han de promover una manera de situar el acervo histórico y conceptual para el estudio de la violencia del país. La presente investigación tiene como finalidad poner a disposición de los interesados los resultados de un proyecto de interés en el marco cultural, desde su articulación con los archivos musicales y la memoria colectiva, lo cual conlleva a comprender cómo se despliega la memoria colectiva desde las representaciones construidas en el sanjuanero "El Barcino" compuesto por Jorge Villamil Cordovez en 1969, a modo de denuncia de la violencia de tipo regional. El aporte se configura como documento histórico para el estudio de la violencia en el departamento del Huila entre 1946 a 1966, con el cual el análisis discursivo de su representación (Van Dijk, 1980,1999), y del núcleo central y periférico de la letra musical (Abric, 1976,1987), posibilita el modelamiento de una propuesta de narrativa digital con el uso de las fuentes de archivo recuperadas.The reflections on the analysis and understanding of some compositions that were constructed as a way of expressing and even denouncing the events that occurred in the times of violence, and the exacerbated events that it generated in the following years within a regional context, are some of the actions that the field of research with a historical-hermeneutical approach have to promote a way to locate the historical and conceptual heritage for the study of the violence of the country. The purpose of this research is to make available to the interested parties the results of a project of interest in the cultural framework, from its articulation with the musical archives and the collective memory, which leads to an understanding of how collective memory unfolds from the representations built in the sanjuanero "El Barcino" composed by Jorge Villamil Cordovez in 1969, as a denunciation of regional violence. The contribution is configured as a historical document for the study of violence in the department of Huila between 1946 and 1966, with which the discursive analysis of its representation (Van Dijk, 1980, 1999), and the central and peripheral nucleus of the letter musical (Abric, 1976, 1987), enables the modeling of a digital narrative proposal with the use of recovered file sources.Magíster en Archivística Histórica y MemoriaMaestrí

Mechanical rotor unbalance monitoring based on system identification and signal processing approaches

Mechanical unbalance is an important source of vibrations that can cause malfunctions in rotodynamic machinery. In industrial applications, unbalance is a critical issue for mass production machines. Previous studies for detecting and monitoring unbalance are based on balancing machines, trial weights, and intrusive actuators, while other studies rely on signal processing techniques, finite element analysis and physical modeling. These methodologies have some critical drawbacks, especially when non-intrusive monitoring is required, such as having to trial weights or determine constructive parameters such as mass and stiffness. The proposed approach is based on detecting and monitoring the unbalance condition in rotatory machines using data extracted from vibration sensors and a rotation sensor fitted to the system supports. The methodology comprises two main steps: identifying the appropriate speed range for unbalance monitoring and the modal parameters of the rotor, and determining and continuously monitoring the unbalance condition. Signal processing and system identification techniques are used to estimate unbalance in the rotatory machine. Experimental results for two rotodynamic systems demonstrate satisfactory performance in identifying and monitoring different unbalance conditions.Peer ReviewedPostprint (author's final draft