INTELLIGENT CONDITION MONITORING OF A GEARBOX USING ARTIFICIAL NEURAL NETWORK

By J. Rafiee, F. Arvani, Abbas Harifi, M. Sadeghi. 2007

349 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/08ae9f58512f0d78d54baec7c82b519b50419e1c

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. Po-Chun Chen
2. P. W. Tse
3. Mohammad Rafiee
4. G. You
5. Chih-Chiang Wei
6. Yuan-Heng Wang

In Mechanical Engineering

1. Po-Chun Chen
2. P. W. Tse
3. Mohammad Rafiee
4. G. You
5. Chih-Chiang Wei
6. Yuan-Heng Wang
7. Jie Yang

Citations in the corpus

• Wind Turbine Gearbox Failure Identification With Deep Neural Networks (2017, background): “[15] modeled different gear conditions with neural networks (NNs) based on the standard deviation of wavelet packet coefficients.”
• Comparison of methods for non-stationary hydrologic frequency analysis: Case study using annual maximum daily precipitation in Taiwan (2017, methodology): “…is the order of the mother wavelet function, which was determined in previous papers using trialand-error methods based on the intrinsic characteristics of the data (Wang and McFadden, 1995; Samanta and Al-Balushi, 2003; Peter et al., 2004; Liu, 2005; Kar and Mohanty, 2006; Rafiee et al., 2007).”
• Application of mother wavelet functions for automatic gear and bearing fault diagnosis (2010, background): “Rafiee et al. / Expert Systems with Applications 37 (2010) 4568–4579 4569”