Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN

By D. Pandya, S. Upadhyay, Suraj Prakash Harsha. Expert systems with applications, 2013

327 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/acf6c9dac17709d50c1c0141bed2c2fe155a93da

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. Boyuan Yang
2. Ruonan Liu
3. Dhiraj Neupane
4. Enrico Zio
5. Jong-Hoon Seok
6. Xuefeng Chen

In Mechanical Engineering

1. Dhiraj Neupane
2. Enrico Zio
3. Jong-Hoon Seok
4. Boyuan Yang
5. Xuefeng Chen
6. Ruonan Liu

Citations in the corpus

• Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks (2018, methodology): “[13] applied HHT to acoustic emission (AE) signals and used kNN for classification of roller bearing faults.”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, methodology): “In [37], Pandya et al. present a bearing fault diagnosis technique, which uses the features extracted by HHT as the input of k -NN classifiers.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “In bearing fault detection some of the broadly practiced ML algorithms are artificial neural networks (ANN) [27], principal component analysis (PCA) [28], support vector machines (SVM) [29], [30], k-Nearest Neighbors (k-NN) [31], singular value decomposition (SVD) [32].”