Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble

By Qiao Hu, Zhengjia He, Zhousuo Zhang, Yanyang Zi. 2007

424 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/9e218009a6f079d759842b6b7c4e33e561f3c305

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

1. Ruqiang Yan
2. Xuefeng Chen
3. Robert X. Gao
4. Bin Jiang
5. Hongtian Chen
6. Qingbo He
7. Noureddine Zerhouni
8. Kamal Medjaher

In Mechanical Engineering

1. Robert X. Gao
2. Ruqiang Yan
3. Xuefeng Chen

Citations in the corpus

• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, background): “Li et al. (2000) proposed an approach for motor rolling bearing fault diagnosis using neural networks and time-/frequency-domain bearing vibration analysis by discussing bearing vibration frequency features of motor bearing fault diagnosis.”
• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, background): “SVMs ensembles to separate different bearing faults conditions and assess incipient fault severity [146].”