Zerrendatu honen arabera: egilea "625a7f9e2b226e958788bcdcd3b02aef"
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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 ... -
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 ... -
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 ... -
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. ...
