Artificial Neural Network Based Fault Diagnosis of a Pulley-Belt Rotating System

Alaa Abdulhady Jaber, Khalid Mohsin Ali

Abstract


Rotating machines are widely used in various industrial fields. Hence, an unexpected stoppage due to, for example, bad operating conditions or manufacturing error, has safety implications along with economic considerations. In this research, a fault detection system for a pulley-belt rotating system is developed and then different faults simulated in a test rig are investigated. Vibration signal monitoring is utilized since it represents a reliable approach for fault recognition in rotating machinery. Time-domain signal analysis technique is applied to extract some indicative features, such as root mean square, kurtosis and skewness. An artificial neural network (ANN) model is developed to detect the simulated faults. However, in addition to the machine healthy condition five fault types, such as unbalance in the driving pulley, wear in the belt and pulleys misalignment, have been simulated in the test rig. Two MEMS accelerometers (ADXL335), interfaced to Arduino MEGA 2560 as a data acquisition device, are used for vibration amplitude measurement. LabVIEW, which is a graphical programming software, is utilized to develop a signal capturing, analysis and feature extraction system. The result showed the effectiveness of the developed system in detection of different fault types in the pulley-belt system.

Keywords


fault diagnosis; artificial neural network; pulley-belt system; LabVIEW; arduino.

Full Text:

PDF

References


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

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development