Artificial Neural Network Based Fault Diagnosis of a Pulley-Belt Rotating System
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D. Ying, H. Yigang, and S. Yichuang, "Fault diagnosis of analog circuits with tolerances using artificial neural networks," in IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. , 2000, pp. 292-295.
H. Li, Y. Zhang, and H. Zheng, "Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network," Journal of Mechanical Science and Technology, vol. 23, pp. 2780-2789, 2009.
D. Pandya, S. Upadhyay, and S. Harsha, "Ann based fault diagnosis of rolling element bearing using time-frequency domain feature," International Journal of Engineering Science and Technology, vol. 4, pp. 2878-2886, 2012.
Q. Jiang, Y. Shen, H. Li, and F. Xu, "New fault recognition method for rotary machinery based on information entropy and a probabilistic neural network," Sensors, vol. 18, 2018.
A. A. A. Bulushi, G. R. Rameshkumar, and M. Lokesha, "Fault Diagnosis in Belts using Time and Frequency based Signal Processing Techniques," International Journal of Multidisciplinary Sciences and Engineering, vol. 6, 2015.
W. Li, Z. Wang, Z. Zhu, G. Zhou, and G. Chen, "Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine," Advances in Mechanical Engineering, vol. 5, p. 797183, 2013.
C. Wu, T. Chen, R. Jiang, L. Ning, and Z. Jiang, "ANN Based Multi-classification Using Various Signal Processing Techniques for Bearing Fault Diagnosis," International Journal of Control and Automation, vol. 8, pp. 113-124, 2015.
A. R. Bhendea, G. K. Awarib, and S. P. Untawalec, "Comprehensive bearing condition monitoring algorithm for incipient fault detection using acoustic emission," Jurnal Tribologi, vol. 2, 2014.
M. C. S. Reddy and A. S. Sekhar, "Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems," International Journal of Applied Science and Engineering, vol. 1, pp. 69-84, 2013.
A. R. Hassan and K. M. Ali, "Effects Of Rotational Speed, Center Distance And Diameter Ratios On The Dynamic Response Of Pulley-Belt System Depends On Vibration Analysis," Al-Qadisiyah Journal For Engineering Sciences, vol. 10, 2017.
V. K. Patel and M. N. Patel, "Development of Smart Sensing Unit for Vibration Measurement by Embedding Accelerometer with the Arduino Microcontroller," International Journal of Instrumentation Science, vol. 6, pp. 1 - 7, 2017.
J. Yan, Machinery Prognostics and Prognosis Oriented Maintenance Management: John Wiley & Sons, 2015.
S. Fu, K. Liu, Y. Xu, and Y. Liu, "Rolling Bearing Diagnosing Method Based on Time Domain Analysis and Adaptive Fuzzy -Means Clustering," Shock and Vibration, vol. 2016, p. 8, 2016.
J. K. Sinha, Vibration Analysis,Instruments, and Signal Processing, First Edition ed.: CRC Press, 2014.
W. Caesarendra and T. Tjahjowidodo, "A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing," Machines, vol. 5, p. 21, 2017.
M. czkiewicz and T. Barszcz, "Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine," Shock and Vibration, vol. 2016, p. 12, 2016.
M. W. Ahmad, M. Mourshed, and Y. Rezgui, "Trees vs Neurons: Comparison between Random Forest and ANN for high-resolution prediction of building energy consumption," Energy and Buildings, vol. 147, 2017.
DOI: http://dx.doi.org/10.18517/ijaseit.9.2.7426
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