Prognostics and health management of rotating machinery of industrial robot with deep learning applications—A review
The availability of computational power in the domain of Prognostics and Health
Management (PHM) with deep learning (DL) applications has attracted researchers …
Management (PHM) with deep learning (DL) applications has attracted researchers …
Machine learning for fault analysis in rotating machinery: A comprehensive review
As the concept of Industry 4.0 is introduced, artificial intelligence-based fault analysis is
attracted the corresponding community to develop effective intelligent fault diagnosis and …
attracted the corresponding community to develop effective intelligent fault diagnosis and …
A knowledge dynamic matching unit-guided multi-source domain adaptation network with attention mechanism for rolling bearing fault diagnosis
Z Wu, H Jiang, H Zhu, X Wang - Mechanical Systems and Signal …, 2023 - Elsevier
Most current research on multi-source domain adaptation in bearing fault diagnosis focuses
on training domain-agnostic networks whose parameters are static. However, it is …
on training domain-agnostic networks whose parameters are static. However, it is …
Machinery multi-sensor fault diagnosis based on adaptive multivariate feature mode decomposition and multi-attention fusion residual convolutional neural network
Due to the complex and rugged working environment of real machinery equipment, the
resulting fault information is easily submerged by severe noise interference. Additionally …
resulting fault information is easily submerged by severe noise interference. Additionally …
Intelligent fault diagnosis for planetary gearbox using transferable deep q network under variable conditions with small training data
Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and
dependability of mechanical drive systems. Nevertheless, variable conditions and …
dependability of mechanical drive systems. Nevertheless, variable conditions and …
Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis
Unsupervised domain adaptation (UDA) has shown remarkable results in fault diagnosis
under changing working conditions in recent years. However, most UDA methods do not …
under changing working conditions in recent years. However, most UDA methods do not …
Machine learning based condition monitoring for gear transmission systems using data generated by optimal multibody dynamics models
J Koutsoupakis, P Seventekidis… - Mechanical Systems and …, 2023 - Elsevier
Condition monitoring (CM) of mechanical systems such as gear transmissions can be
performed with vibration measurements and processing of the recorded signals for …
performed with vibration measurements and processing of the recorded signals for …
Deep attention relation network: A zero-shot learning method for bearing fault diagnosis under unknown domains
Z Chen, J Wu, C Deng, X Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to
severe data distribution difference under variable working conditions, they have …
severe data distribution difference under variable working conditions, they have …
[HTML][HTML] Fault diagnosis of a wave energy converter gearbox based on an Adam optimized CNN-LSTM algorithm
The complex structure and harsh operating environment of wave energy converters can
result in various faults in transmission components. Environmental noise in practical …
result in various faults in transmission components. Environmental noise in practical …
Development of Deep belief network for tool faults recognition
AP Kale, RM Wahul, AD Patange, R Soman… - Sensors, 2023 - mdpi.com
The controlled interaction of work material and cutting tool is responsible for the precise
outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and …
outcome of machining activity. Any deviation in cutting parameters such as speed, feed, and …