Machine Learning for industrial applications: A comprehensive literature review
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …
able to learn autonomously, directly from the input data. Over the last decade, ML …
Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects
The recent advancements in the fields of artificial intelligence (AI) and machine learning
(ML) have affected several research fields, leading to improvements that could not have …
(ML) have affected several research fields, leading to improvements that could not have …
A trustworthy privacy preserving framework for machine learning in industrial IoT systems
PCM Arachchige, P Bertok, I Khalil… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy,
agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry …
agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry …
Learning deep multimanifold structure feature representation for quality prediction with an industrial application
Due to the existence of complex disturbances and frequent switching of operational
conditions characteristics in the real industrial processes, the process data under different …
conditions characteristics in the real industrial processes, the process data under different …
Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology
X Zhang, KP Rane, I Kakaravada… - Nonlinear Engineering, 2021 - degruyter.com
Recently, researchers are investing more fervently in fault diagnosis area of electrical
machines. The users and manufacturers of these various efforts are strong to contain …
machines. The users and manufacturers of these various efforts are strong to contain …
Convolution neural networks for pothole detection of critical road infrastructure
A well developed and maintained highway infrastructure is essential for the economic and
social prosperity of modern societies. Highway maintenance poses significant challenges …
social prosperity of modern societies. Highway maintenance poses significant challenges …
Adversarial autoencoder based feature learning for fault detection in industrial processes
Deep learning has recently emerged as a promising method for nonlinear process
monitoring. However, ensuring that the features from process variables have representative …
monitoring. However, ensuring that the features from process variables have representative …
[HTML][HTML] Time-series pattern recognition in Smart Manufacturing Systems: A literature review and ontology
MA Farahani, MR McCormick, R Gianinny… - Journal of Manufacturing …, 2023 - Elsevier
Since the inception of Industry 4.0 in 2012, emerging technologies have enabled the
acquisition of vast amounts of data from diverse sources such as machine tools, robust and …
acquisition of vast amounts of data from diverse sources such as machine tools, robust and …
Multisource-refined transfer network for industrial fault diagnosis under domain and category inconsistencies
Unsupervised cross-domain fault diagnosis has been actively researched in recent years. It
learns transferable features that reduce distribution inconsistency between source and …
learns transferable features that reduce distribution inconsistency between source and …
The role of deep learning in manufacturing applications: Challenges and opportunities
There is a growing interest in using deep learning technologies within the manufacturing
industry to improve quality, productivity, safety, and efficiency, while also reducing costs and …
industry to improve quality, productivity, safety, and efficiency, while also reducing costs and …