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Interpreting Remaining Useful Life estimations combining Explainable Artificial Intelligence and domain knowledge in industrial machinery
(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
(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. ...
Design and validation of a methodology to implement data-driven predictive maintenance in industrial environments
(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 ...
Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learning for Anomaly Detection and Diagnosis in Press Machine Data
(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 ...
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
(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 ...
Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation
(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 ...
