Zerrendatu Artikuluak-Ingeniaritza honen arabera: egilea "bcce90727237eac3d600e78c870b9b62"
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Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data
Serradilla, Oscar; Zugasti, Ekhi; Zurutuza, Urko (MDPI, 2021)Predictive maintenance (PdM) has the potential to reduce industrial costs by anticipating failures and extending the work life of components. Nowadays, factories are monitoring their assets and most collected data belong ... -
Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation
Izagirre, Unai; Serradilla, Oscar; Olaizola, Jon; Zugasti, Ekhi; Aizpurua Unanue, Jose Ignacio (MDPI, 2023)In this paper, a set of best practice data sharing guidelines for wind turbine fault detection model evaluation is developed, which can help practitioners overcome the main challenges of digitalisation. Digitalisation is ... -
A Big Data implementation of the MANTIS Reference Architecture for Predictive Maintenance
Larrinaga, Felix; Zugasti, Ekhi; Garitano, Iñaki; Zurutuza, Urko (Sage Journals, 2019) -
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
Serradilla, Oscar; Zugasti, Ekhi; Zurutuza, Urko (Springer Science+Business Media, LLC, 2022)Given the growing amount of industrial data in the 4th industrial revolution, deep learning solutions have become popular for predictive maintenance (PdM) tasks, which involve monitoring assets to anticipate their requirements ... -
Impregnation quality diagnosis in Resin Transfer Moulding by machine learning
Mendikute, Julen; Plazaola Madinabeitia, Joanes; Baskaran, Maider; Zugasti, Ekhi; Aretxabaleta, Laurentzi; Aurrekoetxea, Jon (Elsevier Ltd., 2021)In recent years, several optimization strategies have been developed which reduce the overall defectiveness of the RTM manufactured part. RTM filling simulations showed that, even using optimized injection strategies, local ... -
A Methodology for Advanced Manufacturing Defect Detection through Self-Supervised Learning on X-ray Images
Intxausti Arbaiza, Eneko; Cernuda, Carlos; Zugasti, Ekhi (MDPI, 2024)In industrial quality control, especially in the field of manufacturing defect detection, deep learning plays an increasingly critical role. However, the efficacy of these advanced models is often hindered by their need ... -
Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge
Serradilla, Oscar; Zugasti, Ekhi; Zurutuza, Urko (Taylor and Francis, 2022)The 4th industrial revolution has connected machines and industrial plants, facilitating process monitoring and the implementation of predictive maintenance (PdM) systems that can save up to 60% of maintenance costs. ... -
Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
Labaien Soto, Jokin; Zugasti, Ekhi (MDPI, 2023)Explainable Artificial Intelligence (XAI) has gained significant attention in recent years due to concerns over the lack of interpretability of Deep Learning models, which hinders their decision-making processes. To address ...