Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
O Serradilla, E Zugasti, J Rodriguez, U Zurutuza - Applied Intelligence, 2022 - Springer
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 …
solutions have become popular for predictive maintenance (PdM) tasks, which involve …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
A Theissler, J Pérez-Velázquez, M Kettelgerdes… - Reliability engineering & …, 2021 - Elsevier
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
Machine learning based concept drift detection for predictive maintenance
In this work we present a machine learning based approach for detecting drifting behavior–
so-called concept drifts–in continuous data streams. The motivation for this contribution …
so-called concept drifts–in continuous data streams. The motivation for this contribution …
A deep learning ensemble for network anomaly and cyber-attack detection
Currently, expert systems and applied machine learning algorithms are widely used to
automate network intrusion detection. In critical infrastructure applications of communication …
automate network intrusion detection. In critical infrastructure applications of communication …
[PDF][PDF] An empirical comparison of missing value imputation techniques on APS failure prediction
S Rafsunjani, RS Safa, A Al Imran… - … Journal of Information …, 2019 - researchgate.net
The Air Pressure System (APS) is a type of function used in heavy vehicles to assist braking
and gear changing. The APS failure dataset consists of the daily operational sensor data …
and gear changing. The APS failure dataset consists of the daily operational sensor data …
Methodology for data-driven predictive maintenance models design, development and implementation on manufacturing guided by domain knowledge
O Serradilla, E Zugasti… - … Journal of Computer …, 2022 - Taylor & Francis
The 4th industrial revolution has connected machines and industrial plants, facilitating
process monitoring and the implementation of predictive maintenance (PdM) systems that …
process monitoring and the implementation of predictive maintenance (PdM) systems that …
Detecting APS failures using LSTM-AE and anomaly transformer enhanced with human expert analysis
This study develops a novel semi-supervised approach for detecting Air Pressure System
(APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning …
(APS) failures in Heavy-Duty Vehicles (HDVs) by exploiting two modern Machine Learning …
A hybrid machine learning system to impute and classify a component-based robot
In the field of cybernetic systems and more specifically in robotics, one of the fundamental
objectives is the detection of anomalies in order to minimize loss of time. Following this idea …
objectives is the detection of anomalies in order to minimize loss of time. Following this idea …
Broad embedded logistic regression classifier for prediction of air pressure systems failure
In recent years, the latest maintenance modelling techniques that adopt the data-based
method, such as machine learning (ML), have brought about a broad range of useful …
method, such as machine learning (ML), have brought about a broad range of useful …
A dissimilarity-based approach to predictive maintenance with application to HVAC systems
R Satta, S Cavallari, E Pomponi, D Grasselli… - arxiv preprint arxiv …, 2017 - arxiv.org
The goal of predictive maintenance is to forecast the occurrence of faults of an appliance, in
order to proactively take the necessary actions to ensure its availability. In many application …
order to proactively take the necessary actions to ensure its availability. In many application …