Data-driven fault diagnosis for traction systems in high-speed trains: A survey, challenges, and perspectives
Recently, to ensure the reliability and safety of high-speed trains, detection and diagnosis of
faults (FDD) in traction systems have become an active issue in the transportation area over …
faults (FDD) in traction systems have become an active issue in the transportation area over …
[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning
The increased complexity and intelligence of automation systems require the development
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected …
Data augmentation for improving deep learning in image classification problem
A Mikołajczyk, M Grochowski - … interdisciplinary PhD workshop …, 2018 - ieeexplore.ieee.org
These days deep learning is the fastest-growing field in the field of Machine Learning (ML)
and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional …
and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional …
Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives
Over the last decade, transfer learning has attracted a great deal of attention as a new
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
learning paradigm, based on which fault diagnosis (FD) approaches have been intensively …
An evaluation of anomaly detection and diagnosis in multivariate time series
Several techniques for multivariate time series anomaly detection have been proposed
recently, but a systematic comparison on a common set of datasets and metrics is lacking …
recently, but a systematic comparison on a common set of datasets and metrics is lacking …
Data-driven smart manufacturing
The advances in the internet technology, internet of things, cloud computing, big data, and
artificial intelligence have profoundly impacted manufacturing. The volume of data collected …
artificial intelligence have profoundly impacted manufacturing. The volume of data collected …
A comprehensive review on signal-based and model-based condition monitoring of wind turbines: Fault diagnosis and lifetime prognosis
Wind turbines play an increasingly important role in renewable power generation. To ensure
the efficient production and financial viability of wind power, it is crucial to maintain wind …
the efficient production and financial viability of wind power, it is crucial to maintain wind …
Data mining and analytics in the process industry: The role of machine learning
Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …
decision making/supports in the process industry over the past several decades. As a …
Review on data-driven modeling and monitoring for plant-wide industrial processes
Z Ge - Chemometrics and Intelligent Laboratory Systems, 2017 - Elsevier
Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …
attention in both academy and industry. This paper provides a systematic review on data …