Bearing defect diagnosis and acoustic emission

By A. Morhain, D. Mba. 2003

143 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/d6af9428ca4c5e8e9d1f8769483f1900c5c1829b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. David He

In Control and Systems Engineering

1. Bo-Suk Yang
2. Achmad Widodo
3. David He
4. Miao He
5. Wei Qiao
6. Dingguo Lu
7. Jian Hou
8. Zuyu Yin

In Mechanics of Materials

1. David He
2. Miao He

Citations in the corpus

• Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine (2009, background): “Morhain and Mba (2003) undertook an investigation to ascertain the most appropriate threshold lever for AE count diagnosis in rolling element bearings.”
• Deep Learning Based Approach for Bearing Fault Diagnosis (2017, background): “Over the recent years, the application of acoustic emission (AE) sensors for machine health monitoring and fault diagnosis has been reported [1]–[8].”
• Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing (2009, background): “Several other authors have shown the advantages of applying AE to monitoring of rolling element bearings [4 – 17].”