Gear fault detection using artificial neural networks and support vector machines with genetic algorithms

By Biswanath Samanta. 2004

538 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Mechanical Engineering

How researchers see this paper

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

In Analytical Chemistry

1. Marc-André Zöller
2. Enrico Zio
3. Tutut Herawan
4. Haruna Chiroma
5. Marco F. Huber
6. Tian Han
7. Satish C. Sharma
8. Bo-Suk Yang

In Control and Systems Engineering

1. Xuefeng Chen
2. Enrico Zio
3. Boyuan Yang
4. Ruonan Liu
5. Pavan Kumar Kankar
6. Bo-Suk Yang
7. Achmad Widodo
8. Marc-André Zöller

In Mechanical Engineering

1. Pavan Kumar Kankar
2. Xuefeng Chen
3. Enrico Zio
4. Satish C. Sharma
5. Marc-André Zöller
6. Tutut Herawan
7. Haruna Chiroma
8. Marco F. Huber

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Also in 2004, the first approaches for automatic feature selection have been published (Samanta, 2004).”
• Survey on Automated Machine Learning (2019, background): “Also in 2004, the first approaches for automatic feature selection have been published (Samanta, 2004).”
• Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction (2015, methodology): “GA has been proven to effectively work with NN for simplification of models and optimisation of parameters to improve NN performance [13].”
• Fault diagnosis of ball bearings using machine learning methods (2011, methodology): “Samanta (2004) has compared the performance of gear fault detection using ANN and SVM.”
• Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings (2017, unknown): “Each of these bins was further processed to extract the following features from the original, derivative, and integral signals [39, 40]: maximum amplitude, root mean square (RMS), peak-to-peak amplitude, crest factor, arithmetic mean, variance ( 𝜎 2 ), skewness (normalized 3rd central moment),…”
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018, result): “By utilizing time-domain features as inputs, B. Samanta [76] compares the performance of gear fault detection using ANN and SVM: the results give the classification accuracy of SVM compared to that of ANN.”
• Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors (2007, background): “For example, ball bearing faults (Jack & Nandi, 2002), gear faults (Samanta, 2004),”
• Application of mother wavelet functions for automatic gear and bearing fault diagnosis (2010, unknown): “GA-based (Samanta, 2004), FL-based (Saravanan, Cholairajan, & Ramachandran, 2009), various similar classifiers (Saravanan, Kumar Siddabattuni, and Ramachandran (2008)), expert systems (Ebersbach & Peng, 2008), and combined algorithms (Rafiee, Tse, Harifi, & Sadeghi, 2009; Wu, Hsu, & Wu, 2009) could…”