Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
By G. Bin, Jinji Gao, X. J. Li, B. Dhillon. 2012
497 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/46690b4f5ad1fab1584d652e4eb37ea3be07cf6e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Xiang Li
- Qian Ding
- Onur Avcı
- Osama Abdeljaber
- Moncef Gabbouj
- Serkan Kiranyaz
- Turker Ince
- Changhong Yan
In Mechanics of Materials
- Onur Avcı
- Osama Abdeljaber
- Serkan Kiranyaz
- Moncef Gabbouj
- Daniel J. Inman
- Jianqiao Sun
- Qian Ding
- Xiang Li
Citations in the corpus
- 1-D Convolutional Neural Networks for Signal Processing Applications (2019, methodology): “Figure 5 shows the ROC curves of the 1D CNN method in [23] against the conventional methods, [31]-[34].”
- Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning (2018, background): “Correspondingly, numerous studies have focused on how to extract effective features from measured time-domain signals based on various signal processing techniques, such as time-domain statistical analysis and spectral analysis [2], [3], time–frequency and other transform domain analysis [4]–[7], ad”
- A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, methodology): “Bin et al. (2012) proposed a new approach based on wavelet packet decomposition and empirical mode decomposition to extract fault feature frequency and neural network for rotating machinery early fault diagnosis.”
- 1D Convolutional Neural Networks and Applications: A Survey (2019, methodology): “Figure 14 shows the ROC curves of the 1D CNN method in [55] against the conventional methods such as [75–78].”
- Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019, background): “Especially, data-driven intelligent fault diagnosis methods, which are able to rapidly and efficiently process collected signals, provide reliable fault diagnosis results and do not require prior expertise, are becoming more and more popular nowadays [9–14].”