[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
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 …
An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery
The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …
Currently, several machine learning and deep learning-based modules have achieved …
Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests
Q Hu, XS Si, QH Zhang, AS Qin - Mechanical systems and signal …, 2020 - Elsevier
Fault diagnosis methods based on dimensionless indicators have long been studied for
rotating machinery. However, traditional dimensionless indicators frequently suffer a low …
rotating machinery. However, traditional dimensionless indicators frequently suffer a low …
A scoping review on multi-fault diagnosis of industrial rotating machines using multi-sensor data fusion
Rotating machines is an essential part of any manufacturing industry. The sudden
breakdown of such machines due to improper maintenance can also lead to the industries' …
breakdown of such machines due to improper maintenance can also lead to the industries' …
Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor
Reciprocating compression machinery is the primary source of compressed air in the
industry. Undiagnosed faults in the machinery's components produce a high rate of …
industry. Undiagnosed faults in the machinery's components produce a high rate of …
[HTML][HTML] Imbalanced fault diagnosis of rotating machinery via multi-domain feature extraction and cost-sensitive learning
Q Xu, S Lu, W Jia, C Jiang - Journal of Intelligent Manufacturing, 2020 - Springer
Fault diagnosis plays an essential role in rotating machinery manufacturing systems to
reduce their maintenance costs. How to improve diagnosis accuracy remains an open issue …
reduce their maintenance costs. How to improve diagnosis accuracy remains an open issue …
Research on fault diagnosis of supercharged boiler with limited data based on few-shot learning
G Li, Y Li, C Fang, J Su, H Wang, S Sun, G Zhang, J Shi - Energy, 2023 - Elsevier
The safety of the supercharged boiler affects the normal operation of the steam power
system, while its fault samples are few and contain large noise in reality. Therefore, we …
system, while its fault samples are few and contain large noise in reality. Therefore, we …
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 …