Multi-fault diagnosis of Industrial Rotating Machines using Data-driven approach: A review of two decades of research
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …
machinery. The majority of these machines comprise rotating components and are called …
Data mining in predictive maintenance systems: A taxonomy and systematic review
Predictive maintenance is a field of study whose main objective is to optimize the timing and
type of maintenance to perform on various industrial systems. This aim involves maximizing …
type of maintenance to perform on various industrial systems. This aim involves maximizing …
Multi-source domain generalization for degradation monitoring of journal bearings under unseen conditions
N Ding, H Li, Q Xin, B Wu, D Jiang - Reliability Engineering & System Safety, 2023 - Elsevier
Degradation detection and remaining useful life (RUL) prediction are the essential tasks of
Prognostics and Health Management (PHM) designed to increase the reliability of key …
Prognostics and Health Management (PHM) designed to increase the reliability of key …
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 …
Explainable AI for bearing fault prognosis using deep learning techniques
DC Sanakkayala, V Varadarajan, N Kumar, Karan… - Micromachines, 2022 - mdpi.com
Predicting bearing failures is a vital component of machine health monitoring since bearings
are essential parts of rotary machines, particularly large motor machines. In addition …
are essential parts of rotary machines, particularly large motor machines. In addition …
[HTML][HTML] A predictive maintenance model using long short-term memory neural networks and Bayesian inference
D Pagano - Decision Analytics Journal, 2023 - Elsevier
The fourth industrial revolution is a profound transformation utilizing emerging technologies
like smart automation, large-scale machine-to-machine communication, and the internet of …
like smart automation, large-scale machine-to-machine communication, and the internet of …
Systematic literature review on visual analytics of predictive maintenance in the manufacturing industry
The widespread adoption of cyber-physical systems and other cutting-edge digital
technology in manufacturing industry production facilities may motivate stakeholders to …
technology in manufacturing industry production facilities may motivate stakeholders to …
TL–LEDarcNet: Transfer Learning Method for Low-Energy Series DC Arc-Fault Detection in Photovoltaic Systems
The arc-fault phenomenon in photovoltaic (PV) systems has emerged as a major problem in
recent years. Existing studies on arc-fault detection in conventional PV systems primarily …
recent years. Existing studies on arc-fault detection in conventional PV systems primarily …
Multi-sensor GA-BP algorithm based gearbox fault diagnosis
Y Fu, Y Liu, Y Yang - Applied Sciences, 2022 - mdpi.com
To address the problem of the low recognition rate of time-frequency domain methods
gearbox fault identification, a method featuring decision-level fusion of DS evidence theory …
gearbox fault identification, a method featuring decision-level fusion of DS evidence theory …
Rolling bearing fault diagnosis based on deep learning and autoencoder information fusion
J Ma, C Li, G Zhang - Symmetry, 2021 - mdpi.com
The multisource information fusion technique is currently one of the common methods for
rolling bearing fault diagnosis. However, the current research rarely fuses information from …
rolling bearing fault diagnosis. However, the current research rarely fuses information from …