Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests

By Ziwei Wang, Qinghua Zhang, Jianbin Xiong, Ming Xiao, Guoxi Sun, Jun He. IEEE Sensors Journal, 2017

310 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/09befeeab8e38b091aad8ffde7b4ce312bcd4238

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. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

In Mechanical Engineering

1. Shen Zhang
2. Bingnan Wang
3. Shibo Zhang
4. Thomas G. Habetler

Citations in the corpus

• Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019, methodology): “Besides the commonly used ML methods listed above, many other algorithms have been applied to the identification of bearing faults, bringing in different characteristics and benefits, including neural fuzzy network [61]–[63], Bayesian networks [64]–[66], self-organizing maps [67], [68], extreme lear”
• Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019, unknown): “fer learning [72]–[74], linear discriminant analysis [75], [76], quadratic discriminant analysis [37], random forest [77], independent component analysis [78], softmax classifiers [79], manifold learning [80], [81], canonical variate analysis [82], particle filter [83], nonlinear preserving projecti”