A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Z Zhu, Y Lei, G Qi, Y Chai, N Mazur, Y An, X Huang - Measurement, 2023 - Elsevier
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …
tremendous progress, which can help reduce costly breakdowns. However, different …
Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method
J Li, Y Liu, Q Li - Measurement, 2022 - Elsevier
Data-driven intelligent method has been widely used in fault diagnostics. However, it is
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …
observed that previous research studies focusing on imbalanced datasets for fault diagnosis …
Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks
In the application of structural health monitoring, the measured data might be temporarily or
permanently lost due to sensor fault or transmission failure. The measured data with a high …
permanently lost due to sensor fault or transmission failure. The measured data with a high …
An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis
Variational mode decomposition has been widely applied to machinery fault diagnosis
during these years. However, it remains difficult to set proper hyperparameters for the …
during these years. However, it remains difficult to set proper hyperparameters for the …
Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems
A challenging problem in risk and reliability analysis of Complex Engineering Systems
(CES) is performing and updating risk and reliability assessments on the whole system with …
(CES) is performing and updating risk and reliability assessments on the whole system with …
Semi-supervised bearing fault diagnosis and classification using variational autoencoder-based deep generative models
Many industries are evaluating the use of the Internet of Things (IoT) technology to perform
remote monitoring and predictive maintenance on their mission-critical assets and …
remote monitoring and predictive maintenance on their mission-critical assets and …
A novel data-driven method for structural health monitoring under ambient vibration and high-dimensional features by robust multidimensional scaling
Dealing with the problem of large volumes of high-dimensional features and detecting
damage under ambient vibration are critical to structural health monitoring. To address these …
damage under ambient vibration are critical to structural health monitoring. To address these …
A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties
J Caceres, D Gonzalez, T Zhou… - Structural Control and …, 2021 - Wiley Online Library
Deep learning‐based approach has emerged as a promising solution to handle big
machinery data from multi‐sensor suites in complex physical assets and predict their …
machinery data from multi‐sensor suites in complex physical assets and predict their …
Uncertainty quantification in deep convolutional neural network diagnostics of journal bearings with ovalization fault
Bearings play a crucial role in machine longevity and is, at the same time, one of the most
critical sources of failure in rotor dynamics. Particularly for journal bearings, it is not …
critical sources of failure in rotor dynamics. Particularly for journal bearings, it is not …