Fault diagnosis of ball bearings using machine learning methods

By Pavan Kumar Kankar, Satish C. Sharma, Suraj Prakash Harsha. Expert systems with applications, 2011

474 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/b5ee991fd45fb7f12df815c1fdb0767a98f19127

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. Dhiraj Neupane
2. Jong-Hoon Seok
3. Satish C. Sharma

In Mechanical Engineering

1. Dhiraj Neupane
2. Jong-Hoon Seok
3. Satish C. Sharma

In Mechanics of Materials

1. Dhiraj Neupane
2. Jong-Hoon Seok

Citations in the corpus

• Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform (2011, background): “Recently, Kankar et al. (2011) have carried out a comparative experimental study of the effectiveness of Artificial Neural Network (ANN) and SVM for diagnosis of bearing faults and found that SVM gives better classification efficiency than of ANN.”
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, unknown): “IFD based on traditional machine learning methods [1], including random forest [2] and support vector machine [3], has been widely applied in research and industry scenarios.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, methodology): “In MFDD, the use of these ML algorithms for developing a knowledge base system has become useful for early diagnosis of a defect to prevent catastrophic failure and reduce operating costs [26].”