A survey of predictive maintenance: Systems, purposes and approaches
This paper provides a comprehensive literature review on Predictive Maintenance (PdM)
with emphasis on system architectures, purposes and approaches. In industry, any outages …
with emphasis on system architectures, purposes and approaches. In industry, any outages …
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] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources
J Ferrero Bermejo, JF Gómez Fernández… - Applied Sciences, 2019 - mdpi.com
The generation of energy from renewable sources is subjected to very dynamic changes in
environmental parameters and asset operating conditions. This is a very relevant issue to be …
environmental parameters and asset operating conditions. This is a very relevant issue to be …
[HTML][HTML] Health indicator for machine condition monitoring built in the latent space of a deep autoencoder
A González-Muñiz, I Diaz, AA Cuadrado… - Reliability Engineering & …, 2022 - Elsevier
The construction of effective health indicators plays a key role in the engineering systems
field: they reflect the degradation degree of the system under study, thus providing vital …
field: they reflect the degradation degree of the system under study, thus providing vital …
[HTML][HTML] Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer
In industrial applications, anomaly detectors are trained to raise alarms when measured
samples deviate from the training data distribution. The samples used to train the model …
samples deviate from the training data distribution. The samples used to train the model …
Prognostics and health management of industrial assets: Current progress and road ahead
L Biggio, I Kastanis - Frontiers in Artificial Intelligence, 2020 - frontiersin.org
Prognostic and Health Management (PHM) systems are some of the main protagonists of
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …
the Industry 4.0 revolution. Efficiently detecting whether an industrial component has …
[HTML][HTML] A survey on machine learning based analysis of heterogeneous data in industrial automation
In many application domains data from different sources are increasingly available to
thoroughly monitor and describe a system or device. Especially within the industrial …
thoroughly monitor and describe a system or device. Especially within the industrial …
[HTML][HTML] Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning
Abstract Predictive Maintenance (PdM) is one of the core innovations in recent years that
sparks interest in both research and industry. While researchers develop more and more …
sparks interest in both research and industry. While researchers develop more and more …
Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series
High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for
monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed …
monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed …