introduction to the special issue on the viii latin american congress on mechanical engineering

This special issue on Advances in mechanical engineering from Latin-America contains papers that have been presented at COLIM 2014, the VIII Latin-American Congress on Mechanical Engineering held in Cuenca (Ecuador) from November 25 to 27, 2014. The conference has been the eighth event of a series of conferences which started in Merida, Venezuela in1999 as a conference activity mainly for promoting mechanical engineering in South America community. The previous conferences have been held in Quito, Ecuador in 2001, in Lima, Peru in 2003, in Morelia, Mexico in 2005, in Cucuta, Colombia in 2008, in Cochabamba, Bolivia in 2010, and in Cuzco, Peru in 2012. The congress focuses on today's technical challenges, research updates, research and development innovations that are shaping the future of mechanical engineering for industrial and non-industrial applications. The congress also has the goal to encourage exchanging experiences and to build up international cooperation networks by inviting researchers, teachers, students and professionals from engineering disciplines in sharing experiences and results. In the latest years, the event has received an increased attention, as it can be seen from the fact that the 2014 congress proceedings contain 128 papers contributions, from all around the world. This special issue has been obtained as a result of a second review process and selection among the best presented papers at COLIM 2014. The content of the papers cover several aspects of mechanical engineering with challenges in the area of interests in Latin-America from theoretical aspects up to practical industrial and non-industrial applications, such as science applied to mechanical engineering, design of machines and components, manufacture of machines, automotive engineering, and methods and techniques for engineering education. We would like to express grateful thanks to the members of the International Scientific Committee for 2014 COLIM conference (listed below), for co-operating enthusiastically for the success of the COLIM event and this special issue: Simon Figueroa (Venezuela), Conference Chair Marco Ceccarelli (Italy), Conference Co-Chair Rene Vinicio Sanchez (Ecuador), Conference Co-Chair Victor Cardenas (Ecuador) Jose Felix Vazquez (Mexico) Jaime Cervantes de Gortari (Mexico) Jose Carlos Miranda (Mexico) Lisa Lugo (Paraguay) Cesar Alejandro Isaza (Colombia

Multi-layer neural network with deep belief network for gearbox fault diagnosis

Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery

Generative Adversarial Networks Selection Approach for Extremely Imbalanced Fault Diagnosis of Reciprocating Machinery

At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is in equal proportions for all conditions. However, when the application is closer to reality, the problem of data imbalance is increasingly evident. In this paper, we propose a method for the creation of diagnoses that consider an extreme imbalance in the available data. Our approach first processes the vibration signals of the machine using a wavelet packet transform-based feature-extraction stage. Then, improved generative models are obtained with a dissimilarity-based model selection to artificially balance the dataset. Finally, a Random Forest classifier is created to address the diagnostic task. This methodology provides a considerable improvement with 99% of data imbalance over other approaches reported in the literature, showing performance similar to that obtained with a balanced set of data.National Natural Science Foundation of China, under Grant 51605406National Natural Science Foundation of China under Grant 7180104

Exploiting generative adversarial networks as an oversampling method for fault diagnosis of an industrial robotic manipulator

Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.FCT-through IDMEC, under LAETA, project UIDB/50022/2020.info:eu-repo/semantics/publishedVersio

Evaluation of Time and Frequency Condition Indicators from Vibration Signals for Crack Detection in Railway Axles

Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features by applying the Mean Decrease Accuracy method of Random Forest. Finally, we test the best indicators with a K-Nearest Neighbor classifier. For the data collection, a real bogie test bench has been used to simulate crack faults on the railway axles, and vibration signals from both the left and right sides of the axle were measured. The results not only show the performance of condition indicators in different domains, but also show that the fusion of condition indicators works well together to detect a crack fault in railway axles.Authors would like to thank the support provided by the Spanish Government, through the MAQ-STATUS DPI2015-69325-C2-1-R project, and Universidad Politécnica Salesiana through the research group GIDTEC

1er. Coloquio de educación para el diseño en la sociedad 5.0

Las memorias del 1er. Coloquio de Educación para el Diseño en la Sociedad 5.0 debenser entendidas como un esfuerzo colectivo de la comunidad de académicos de la División de Ciencias y Artes para el Diseño, que pone de manifiesto los retos y oportunidades que enfrenta la educación en diseño en un contexto de cambio acelerado y rompimiento de paradigmas.El evento se realizó el pasado mes de mayo de 2018 y se recibieron más de 50 ponencias por parte de las profesoras y profesores de la División.Las experiencias y/o propuestas innovadoras en cuanto a procesos de enseñanza y aprendizaje que presentan los autores en cada uno de sus textos son una invitación a reflexionar sobre nuestra situación actual en la materia, y emprender acciones en la División para continuar brindando una educación de calidad en diseño a nuestras alumnas, alumnos y la sociedad.Adicionalmente, se organizaron tres conferencias magistrales sobre la situación actual de la educación en Diseño y de las Instituciones de Educación Superior, impartidas por el Mtro. Luis Sarale, profesor de la Universidad Nacional de Cuyo en Mendoza (Argentina), y Presidente en su momento, de la Red de Carreras de Diseño en Universidades Públicas Latinoamericanas (DISUR), el Dr. Romualdo López Zárate, Rector de la Unidad Azcapotzalco, así como del Mtro. Luis Antonio Rivera Díaz, Jefe de Departamento de Teoría y Procesos del Diseño de la División de la Ciencias de la Comunicación y Diseño, en la Unidad Cuajimalpa de nuestra institución.La publicación de estas memorias son un esfuerzo divisional, organizado desde la Coordinación de Docencia Divisional y la Coordinación de Tecnologías del Aprendizaje, del Conocimiento y la Comunicación, para contribuir a los objetivos planteados en el documento ACCIONES:Agenda CyAD2021, en particular al eje de Innovación Educativa. Es necesario impulsar a todos los niveles de la División espacios de discusión orientados a reflexionar sobre el presente y futuro en la educación del diseñador, que contribuya a mejorar la calidad de la docencia y favorezca al fortalecimiento de los procesos de enseñanza y aprendizaje.Finalmente, extiendo un amplio reconocimiento a todos los miembros de la División que hicieron posible este evento, así como a todos los ponentes y participantes por compartir su conocimiento para que la División sea cada día mejor

Cuentos de nunca acabar. Aproximaciones desde la interculturalidad

Cuentos de nunca acabar. Aproximaciones desde la interculturalidad, surge después de la pandemia y su imposibilidad de socializar “en persona” con los compañeros de eventuales encuentros, porque la Comprensión Lectora tenía que reinventarse para su nueva reflexión cognitiva, adaptación contextual y reconstrucción del conocimiento. Este renovado enfoque de la realidad postpandemia, concebido en el marco de la educación intercultural comunitaria, busca potencializar los entornos naturales, sociales y culturales como recursos de aprendizaje multidisciplinario a través del lenguaje animado de los cuentos. En este marco, había que dinamizar la asignatura de Comunicación Oral y Escrita, que se dicta en los Primeros Niveles de los Centros de Apoyo de Otavalo, Cayambe, Latacunga y Riobamba, mediante un eje transversal donde los estudiantes escriban fundamentados en valores de la cosmovisión andina, considerando que provienen de varios lugares de la sierra y amazonía ecuatoriana. Todo surgió del encuentro presencial de un sábado cualquiera donde los estudiantes realizaban ejercicios narrativos, logrando una apreciable respuesta de imaginación, más emotiva que la clásica tarea de las Unidades, tanto así que, pasados unos días, seguían llegando sus escritos a mi correo. Entonces nos pusimos manos a la obra, cada estudiante tendría dos opciones como Actividad Integradora, la primera consistía en escribir un cuento de su propia inspiración, y la segunda analizar un clásico para comentar sus valores y antivalores. La mayor parte de estudiantes decidió escribir su propio cuento, de donde se escogieron algunas participaciones que podrían considerarse originales, para una edición que, respetando la transcripción de la tradición oral que prima en los sectores comunitarios, nos concretamos en revisar la puntuación y ortografía para publicarlos. Con esto buscamos innovar la Actividad Integradora, por algo más práctico y operativo para configurar los Objetos de Aprendizaje que buscamos. Así nació, en medio del camino, este libro de Cuentos de nunca acabar. Aproximaciones desde la interculturalidad, que ponemos en sus manos. Hernán Hermosa Mantilla Quito, junio de 202

Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss

The gearbox is one of the most fragile parts of a wind turbine (WT). Fault diagnosis of the WT gearbox is of great importance to reduce operation and maintenance (O&M) costs and improve cost-effectiveness. At present, intelligent fault diagnosis methods based on long short-term memory (LSTM) networks have been widely adopted. As the traditional softmax loss of an LSTM network usually lacks the power of discrimination, this paper proposes a fault diagnosis method for wind turbine gearboxes based on optimized LSTM neural networks with cosine loss (Cos-LSTM). The loss can be converted from Euclid space to angular space by cosine loss, thus eliminating the effect of signal strength and improve the diagnosis accuracy. The energy sequence features and the wavelet energy entropy of the vibration signals are used to evaluate the Cos-LSTM networks. The effectiveness of the proposed method is verified with the fault vibration data collected on a gearbox fault diagnosis experimental platform. In addition, the Cos-LSTM method is also compared with other classic fault diagnosis techniques. The results demonstrate that the Cos-LSTM has better performance for gearbox fault diagnosis

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults