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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 ...
Providing Proactiveness: Data Analysis Techniques Portfolios
(River Publishers, 2018)
Impregnation quality diagnosis in Resin Transfer Moulding by machine learning
(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 ...
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. ...
Success Stories on Real Pilots
(River Publishers, 2018)
Implementation of a Reference Architecture for Cyber Physical Systems to support Condition Based Maintenance
(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 ...
Null is Not Always Empty: Monitoring the Null Space for Field-Level Anomaly Detection in Industrial IoT Environments
(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 ...
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 ...
