Browsing by Author "Sierra, Basilio"
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Item 3D Convolutional Neural Networks Initialized from Pretrained 2D Convolutional Neural Networks for Classification of Industrial Parts(Multidisciplinary Digital Publishing Institute (MDPI), 2021-02-04) Merino, Ibon; Azpiazu, Jon; Remazeilles, Anthony; Sierra, BasilioDeep learning methods have been successfully applied to image processing, mainly using 2D vision sensors. Recently, the rise of depth cameras and other similar 3D sensors has opened the field for new perception techniques. Nevertheless, 3D convolutional neural networks perform slightly worse than other 3D deep learning methods, and even worse than their 2D version. In this paper, we propose to improve 3D deep learning results by transferring the pretrained weights learned in 2D networks to their corresponding 3D version. Using an industrial object recognition context, we have analyzed different combinations of 3D convolutional networks (VGG16, ResNet, Inception ResNet, and EfficientNet), comparing the recognition accuracy. The highest accuracy is obtained with EfficientNetB0 using extrusion with an accuracy of 0.9217, which gives comparable results to state-of-the art methods. We also observed that the transfer approach enabled to improve the accuracy of the Inception ResNet 3D version up to 18% with respect to the score of the 3D approach alone.Item Data Augmentation for Industrial Prognosis Using Generative Adversarial Networks(Springer, 2020-10-27) Ortego, Patxi; Diez-Olivan, Alberto; Del Ser, Javier; Sierra, Basilio; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Tecnalia Research & Innovation; IAThe Industry 4.0 revolution allows monitoring and intelligent processing of big amounts of data. When monitoring certain assets, very few data is found for operation under faulty conditions because the cost of not operating properly is unacceptable and thus preventive strategies are put in practice. Because machine learning algorithms are data exhaustive, synthetic data can be created for these cases. Deep learning techniques have been proven to work very well for these cases. Generative Adversarial Networks (GANs) have been deployed in numerous applications with data augmentation objectives, but not so much for balancing unidimensional series with few data. In this paper, a GAN is applied in order to augment data for assets operating under faulty conditions. The proposed method is validated on a real industrial case, yielding promising results with respect to the case with no strategy for class imbalance whatsoever.Item Enhancing VTOL Multirotor Performance With a Passive Rotor Tilting Mechanism(2021-04) Iriarte, Imanol; Iglesias, Inaki; Lasa, Joseba; Calvo-Soraluze, Hodei; Sierra, Basilio; INNOV_AIR_MOBILThis article discusses the benefits of introducing a simple passive mechanism to enable rotor tilting in Vertical Take-Off and Landing (VTOL) multirotor vehicles. Such a system is evaluated in relevant Urban Air Mobility (UAM) passenger transport scenarios such as hovering in wind conditions and overcoming rotor failures. While conventional parallel axis multirotors are underactuated systems, the proposed mechanism makes the vehicle fully actuated in SE(3), which implies independent cabin position and orientation control. An accurate vehicle simulator with realistic parameters is presented to compare in simulation the proposed architecture with a conventional underactuated VTOL vehicle that shares the same physical properties. In order to make fair comparisons, controllers are obtained solving an optimization problem in which the cost function of both systems is chosen to be equivalent. In particular, the control laws are Linear-Quadratic Regulators (LQR), which are derived by linearizing the systems around hover. It is shown through extensive simulation that the introduction of a passive rotor tilting mechanism based on universal joints improves performance metrics such as vehicle stability, power consumption, passenger comfort and position tracking precision in nominal flight conditions and it does not compromise vehicle safety in rotor failure situations.Item Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts(2020-06-01) Merino, Ibon; Azpiazu, Jon; Remazeilles, Anthony; Sierra, Basilio; Tecnalia Research & Innovation; ROBOTICA_FLEX; Robótica MédicaThis article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.Item Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes(2019-12-02) Outón, Jose Luis; Villaverde, Iván; Herrero, Héctor; Esnaola, Urko; Sierra, Basilio; ROBOTICA_FLEXThere is a paradigm shift in current manufacturing needs that is causing a change from the current mass-production-based approach to a mass customization approach where production volumes are smaller and more variable. Current processes are very adapted to the previous paradigm and lack the required flexibility to adapt to the new production needs. To solve this problem, an innovative industrial mobile manipulator is presented. The robot is equipped with a variety of sensors that allow it to perceive its surroundings and perform complex tasks in dynamic environments. Following the current needs of the industry, the robot is capable of autonomous navigation, safely avoiding obstacles. It is flexible enough to be able to perform a wide variety of tasks, being the change between tasks done easily thanks to skills-based programming and the ability to change tools autonomously. In addition, its security systems allow it to share the workspace with human operators. This prototype has been developed as part of THOMAS European project, and it has been tested and demonstrated in real-world manufacturing use cases.Item Modeling and control of an overactuated aerial vehicle with four tiltable quadrotors attached by means of passive universal joints(IEEE, 2020-10) Iriarte, Imanol; Otaola, Erlantz; Culla, David; Iglesias, Inaki; Lasa, Joseba; Sierra, Basilio; INNOV_AIR_MOBIL; Tecnalia Research & Innovation; MAQUINASWe present a novel overactuated aerial vehicle based on four quadrotors connected to an airframe by means of passive universal joints. The proposed architecture allows to independently control the six degrees of freedom of the airframe without having fixed propellers at inefficient configurations or making use of dedicated rotor tilting actuators. After deriving the dynamic equations that describe its motion, we propose a linear control strategy that is able to successfully decouple rotation and translation, relying exclusively on on-board sensors. A prototype is built and preliminary experimental results demonstrate that the concept is feasible.Video: https://youtu.be/9ASP3FyhCJw.Item Predictive Maintenance on the Machining Process and Machine Tool(2020-01-01) Jimenez-Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Rodriguez, German; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELThis paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.Item Quantile regression forests-based modeling and environmental indicators for decision support in broiler farming(2019-06) Diez-Olivan, Alberto; Averós, Xavier; Sanz, Ricardo; Sierra, Basilio; Estevez, Inma; Tecnalia Research & InnovationAn efficient and sustainable animal production requires fine-tuning and control of all the parameters involved. But this is not a simple task. Animal farming is a complex biological system in which environmental parameters and management practices interact in a dynamic way. In addition, the typical non-linear response of biological processes implies that relationships across parameters that are critical to assure animal welfare and performance are difficult to determine. In this paper a novel decision support system based on environmental indicators and on weights, leg problems and mortality rates is proposed to address this issue. The data-driven modeling process is performed by a quantile regression forests approach that allows estimating growth, welfare and mortality parameters on the basis of environmental deviations from optimal farm conditions. Resulting models also provide confidence intervals able to deal with uncertainty. They are deployed in farm, offering an accessible tool for farmers, veterinarians and technical personnel. Experimental results involving 20 flocks of broiler meat chickens from different farms show the validity of the system, obtaining robust prediction intervals and high accuracy, namely over 81% for every model. The in-field use of the proposed approach will facilitate an efficient and animal welfare-friendly production management.Item A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context(2021-05-27) Outón, Jose Luis; Merino, Ibon; Villaverde, Iván; Ibarguren, Aitor; Herrero, Héctor; Daelman, Paul; Sierra, Basilio; Tecnalia Research & Innovation; ROBOTICA_FLEX; ROBOTICA_AUTOMAIn modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.Item A statistical data-based approach to instability detection and wear prediction in radial turning processes(2018) Jimenez Cortadi, Alberto; Irigoien, Itziar; Boto, Fernando; Sierra, Basilio; Suarez, Alfredo; Galar, Diego; Tecnalia Research & Innovation; FACTORY; FABRIC_INTELRadial turning forces for tool-life improvements are studied, with the emphasis on predictive rather than preventive maintenance. A tool for wear prediction in various experimental settings of instability is proposed through the application of two statistical approaches to process data on tool-wear during turning processes: three sigma edit rule analysis and Principal Component Analysis (PCA). A Linear Mixed Model (LMM) is applied for wear prediction. These statistical approaches to instability detection generate results of acceptable accuracy for delivering expert opinion. They may be used for on-line monitoring to improve the processing of different materials. The LMM predicted significant differences for tool wear when turning different alloys and with different lubrication systems. It also predicted the degree to which the turning process could be extended while conserving stability. Finally, it should be mentioned that tool force in contact with the material was not considered to be an important input variable for the model.