Browsing by Author "Ortego, Patxi"
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Item Acoustic emission characterisation of two pre-cracked specimens(British Institute of Non-Destructive Testing, 2022) Gálvez, Antonio; Galar, Diego; Alonso, Asier; Errasti-Alcalá, Borja; Bienvenido, Ismael; Ortego, Patxi; Juuso, Esko; Tecnalia Research & InnovationThis article contains the experiments carried-out to study the capabilities of Acoustic Emissions (AE) in a Ship To Shore (STS) crane. This solution studies the implementation of Structural Health Monitoring (SHM) in an STS crane based on acoustic emissions (AE) technique for detecting cracks and assessing their growth in steel elements subjected to fatigue. The first experiment is performed using a compact tension specimen (CT) made of steel S355 whose dimensions are 125x120x50 mm and its cracks and dimensions are defined based on ASTM and ISO standards. The CT is monitored using AE sensors, and then, the features are extracted from the raw data and used to train, test and validate an unsupervised model. The crack detection model obtains a remarkable accuracy; crack detection at sizing of 3 mm length. As the CT dimensions are small, it is difficult to evaluate the attenuation of AE signals, which is completely necessary for monitoring STS cranes. Therefore, a second experiment is performed using a panel made of steel S355, whose dimensions are 2120x200x8 mm; the panel contains a crack of 50x3 mm. This experiment is performed to analyse the AE signals that come from cracks; specifically, to assess signals attenuation, how the attenuation affects cracks detection in the panel, and features evolution while crack propagation. This is led by monitoring the crack growth with crack detection gauges and installing the AE sensors at different distances of the crack. The assessment is used to develop an unsupervised model to detect cracks and an algorithm for localizing them.Item Adaptive Dendritic Cell-Deep Learning Approach for Industrial Prognosis under Changing Conditions(2021-11) Diez-Olivan, Alberto; Ortego, Patxi; Ser, Javier Del; Landa-Torres, Itziar; Galar, Diego; Camacho, David; Sierra, Basilio; Tecnalia Research & Innovation; IAIndustrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.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 Evolutionary LSTM-FCN networks for pattern classification in industrial processes(2020-05) Ortego, Patxi; Diez-Olivan, Alberto; Del Ser, Javier; Veiga, Fernando; Penalva, Mariluz; Sierra, Basilio; Tecnalia Research & Innovation; IA; FABRIC_INTELThe Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are gaining a lot of attention over the last decade due to their capability of successfully modeling nonlinear feature interactions. However, they have not been yet fully applied for pattern classification tasks in time series data within the digital industry. In this paper, a novel approach based on an evolutionary algorithm for optimizing the networks hyperparameters and on the resulting deep learning model for pattern classification is proposed. In order to demonstrate the applicability of this method, a test scenario that involves a process related to blind fastener installation in the aeronautical industry is provided. The results achieved with the proposed approach are compared with shallow models and it is demonstrated that the proposed method obtains better results with an accuracy value of 95%.