Enhanced discriminate feature learning deep residual CNN for multitask bearing fault diagnosis with information fusion
Deep learning-based diagnosis methods currently face some challenges and open
problems. First, domain knowledge of fault modes and operating conditions are not …
problems. First, domain knowledge of fault modes and operating conditions are not …
Advances in Machine Learning for Sensing and Condition Monitoring
In order to overcome the complexities encountered in sensing devices with data collection,
transmission, storage and analysis toward condition monitoring, estimation and control …
transmission, storage and analysis toward condition monitoring, estimation and control …
Convolutional neural network with automatic learning rate scheduler for fault classification
Fault classification is vital in smart manufacturing, and convolutional neural network (CNN)
has been widely applied in fault classification. But the performance of CNN heavily depends …
has been widely applied in fault classification. But the performance of CNN heavily depends …
Gear pitting fault diagnosis with mixed operating conditions based on adaptive 1D separable convolution with residual connection
Gear pitting fault diagnosis has always been an important subject to industry and research
community. In the past, the diagnosis of early gear pitting faults has usually been carried out …
community. In the past, the diagnosis of early gear pitting faults has usually been carried out …
Recent advancement of deep learning applications to machine condition monitoring part 1: a critical review
With the huge success of applying deep learning (DL) methodologies to image recognition
and natural language processing in recent years, researchers are now keen to use them in …
and natural language processing in recent years, researchers are now keen to use them in …
Combinatorial synthesis and analysis of AlxTayVz-Cr20Mo20Nb20Ti20Zr10 and Al10CrMoxNbTiZr10 refractory high-entropy alloys: Oxidation behavior
The combinatorial development of refractory high-entropy alloy Al x Ta y V z-Cr 20 Mo 20 Nb
20 Ti 20 Zr 10 (Al x Ta y V zQ) was carried out, and microstructural analysis was performed …
20 Ti 20 Zr 10 (Al x Ta y V zQ) was carried out, and microstructural analysis was performed …
Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine
SY Park, J Ahn - Acta Astronautica, 2020 - Elsevier
We propose a fault detection and diagnosis (FDD) method for liquid-propellant rocket
engine tests during startup transient based on deep learning. A numerical model describing …
engine tests during startup transient based on deep learning. A numerical model describing …
An adaptive anti-noise neural network for bearing fault diagnosis under noise and varying load conditions
Fault diagnosis in rolling bearings is an indispensable part of maintaining the normal
operation of modern machinery, especially under the varying operating conditions. In this …
operation of modern machinery, especially under the varying operating conditions. In this …
[HTML][HTML] Convolutional-neural-network-based multi-signals fault diagnosis of induction motor using single and multi-channels datasets
M Abdelmaksoud, M Torki, M El-Habrouk… - Alexandria Engineering …, 2023 - Elsevier
Using deep learning in three-phase induction motor fault diagnosis has gained increasing
interest nowadays. This paper proposes a Convolutional Neural Network (CNN) model to …
interest nowadays. This paper proposes a Convolutional Neural Network (CNN) model to …
[HTML][HTML] Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis
Extensive research has been conducted on fault diagnosis of planetary gearboxes using
vibration signals and deep learning (DL) approaches. However, DL-based methods are …
vibration signals and deep learning (DL) approaches. However, DL-based methods are …