Listar por autor "bcce90727237eac3d600e78c870b9b62"
Mostrando ítems 1-20 de 23
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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 ... -
An attribute oriented induction based methodology to aid in predictive maintenance: anomaly detection, root cause analysis and remaining useful life
Fernández Anakabe, Javier (Mondragon Unibertsitatea. Goi Eskola Politeknikoa, 2019)Predictive Maintenance is the maintenance methodology that provides the best performance to industrial organisations in terms of time, equipment effectiveness and economic savings. Thanks to the recent advances in technology, ... -
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 ... -
Design and validation of a methodology to implement data-driven predictive maintenance in industrial environments
Serradilla, Oscar (Mondragon Unibertsitatea. Goi Eskola Politeknikoa, 2021)New trends in manufacturing and industry lead to digitalise all processes, machines and communicate them forming Cyber Physical Systems (CPS), facilitating process monitoring and data acquisition. The analysis of that ... -
Explainable Artificial Intelligence for Industrial Anomaly Diagnosis in Multi-Sensor Data
Labaien Soto, Jokin (Mondragon Unibertsitatea. Goi Eskola Politeknikoa, 2023)This thesis explores the potential of Explainable Artificial Intelligence (XAI) in the context of time-series anomaly detection and diagnosis. By enhancing the transparency of traditionally opaque models and offering ... -
A hybrid probabilistic battery health management approach for robust inspection drone operations
Alcibar, Jokin; Aizpurua Unanue, Jose Ignacio; Zugasti, Ekhi; Peñagarikano, Oier (Elsevier, 2025)Monitoring the health of remote critical infrastructure poses significant challenges due to limited accessibility and harsh operational environments. Inspection drones are ubiquitous assets that enhance the reliability of ... -
Implementation of a Reference Architecture for Cyber Physical Systems to support Condition Based Maintenance
Larrinaga, Felix; Garitano, Iñaki; Zugasti, Ekhi; Zurutuza, Urko (2018)This paper presents the implementation of a refer-ence architecture for Cyber Physical Systems (CPS) to supportCondition Based Maintenance (CBM) of industrial assets. The article focuses on describing how the MANTIS ... -
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 ... -
An interpretable operational state classification framework for elevators through Convolutional Neural Networks
Olaizola Alberdi, Jon; Izagirre, Unai; Serradilla, Oscar; Zugasti, Ekhi; Mendicute, Mikel; Aizpurua Unanue, Jose Ignacio (Wiley, 2025)Ensuring the safe, reliable, and cost-efficient operation of transportation systems such as elevators is critical for the maintenance of civil infrastructures. The ability to monitor the health state and classify different ... -
Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery
Serradilla, Oscar; Zugasti, Ekhi; Cernuda, Carlos; Zurutuza, Urko (IEEE, 2020)This paper presents the implementation and explanations of a remaining life estimator model based on machine learning, applied to industrial data. Concretely, the model has been applied to a bushings testbed, where fatigue ... -
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. ... -
Null is Not Always Empty: Monitoring the Null Space for Field-Level Anomaly Detection in Industrial IoT Environments
Zugasti, Ekhi; Garitano, Iñaki; Iturbe, Mikel; Zurutuza, Urko (IEEE, 2018)Industrial environments have vastly changed sincethe conception of initial primitive and isolated networks. Thecurrent full interconnection paradigm, where connectivity be-tween different devices and the Internet has become ... -
Predicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models
Mendikute, Julen; Baskaran, Maider; Llavori, Inigo; Zugasti, Ekhi; Aretxabaleta, Laurentzi; Aurrekoetxea, Jon (Elsevier, 2023)The main objective of the present paper is to demonstrate the feasibility of machine-learning-based surrogate models for predicting low-velocity impact behaviour considering void content and location generated during the ... -
Providing Proactiveness: Data Analysis Techniques Portfolios
Zugasti, Ekhi; Zurutuza, Urko (River Publishers, 2018) -
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 ... -
Success Stories on Real Pilots
Zurutuza, Urko; Zugasti, Ekhi; Larrinaga, Felix (River Publishers, 2018) -
Towards a probabilistic error correction approach for improved drone battery health assessment
Alcibar, Jokin; Aizpurua Unanue, Jose Ignacio; Zugasti, Ekhi (Research Publishing, Singapore, 2023)Health monitoring of remote critical infrastructure, such as offshore wind turbines, is complex and expensive. For the offshore energy sector, the accessibility for on-site asset inspection is hampered due to their harsh ...
