Monitoring and diagnostics of a belt conveyor based on a wireless system for measurement and analysis of the vibrations level

Krzysztof Szczyrba, Marek Sikora, Łukasz Wróbel

Abstract


The article features a system for monitoring and diagnostics of a belt conveyor. The main goal of the system is acquisition, visualization and monitoring of the vibrations level of the conveyor key elements. The system also has a computing and analysis part which enables to carry out predictive maintenance tasks related to the prediction and assessment of the vibrations level. On this basis, it is possible to decide about indispensable renovations. The architecture of the system enables to use it in other applications in which it is required to have a wireless vibration sensors network in order to full diagnostic tasks.

Keywords


vibration sensor; monitoring system; maintenance; belt conveyor; predictive maintenance; data mining; trend analysis

Full Text:

PDF

References


International Society of Automation, https://www.isa.org/isa95. .

Mobley, R.K.: An Introduction to Predictive Maintenance. Elsevier2002.

Team, R.C.: R: A language and environment for statistical computing. 2013.

Korbicz, J., Kościelny, J.M., (Red.): Modeling, Diagnostics and Process Control: Implementation in the DiaSter System. Springer-Verlag, Berlin Heidelberg 2011.

Mazurkiewicz, D.: Computer-aided maintenance and reliability management systems for conveyor belts. Eksploat. Niezawodn., t. 16, sty. 2014, p. 377÷382.

Zio, E.: Some challenges and opportunities in reliability engineering. IEEE Trans. Reliab., t. 65, nr 4, 2016, p. 1769–1782.

Korbicz, J., Koscielny, J.M., Kowalczuk, Z., Cholewa, W.: Fault diagnosis: models, artificial intelligence, applications. Springer Science & Business Media2012.

Wu, S., Zuo, M.J.: Linear and nonlinear preventive maintenance models. IEEE Trans. Reliab., t. 59, nr 1, 2010, p. 242–249.

Przystalka, P., Moczulski, W.: Methodology of neural modelling in fault detection with the use of chaos engineering. Eng. Appl. Artif. Intell., t. 41, 2015, p. 25–40.

Wachla, D., Moczulski, W.A.: Identification of dynamic diagnostic models with the use of methodology of knowledge discovery in databases. Eng. Appl. Artif. Intell., t. 20, nr 5, 2007, p. 699–707.

Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., i in.: Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Ind. Electron. Mag., t. 8, nr 2, 2014, p. 31–42.

Glowacz, A., Glowacz, Z.: Diagnosis of the three-phase induction motor using thermal imaging. Infrared Phys. Technol., t. 81, 2017, p. 7–16.

Du, W., Li, A., Ye, P., Liu, C.: Fault diagnosis of plunger pump in truck crane based on relevance vector machine with particle swarm optimization algorithm. Shock Vib., t. 20, nr 4, 2013, p. 781–792.

Zuber, N., Bajrić, R., Šostakov, R.: Gearbox faults identification using vibration signal analysis and artificial intelligence methods. Eksploat. Niezawodn., t. 16, nr 1, 2014, p. 61–65.

Peng, Z.K., Chu, F.L.: Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process., t. 18, nr 2, 2004, p. 199–221.

Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Process., t. 96, 2014, p. 1–15.

Ye, Z., Wu, B., Zargari, N.: Online mechanical fault diagnosis of induction motor by wavelet artificial neural network using stator current. [w:] Industrial Electronics Society, 2000. IECON 2000. 26th Annual Confjerence of the IEEE, 2000, t. 2, p. 1183–1188.

Hu, X., Ji, Y., Yu, W.: The application of wavelet singularity detection in fault diagnosis of high voltage breakers. [w:] Industrial Electronics Society, 2001. IECON’01. The 27th Annual Conference of the IEEE, 2001, t. 1, p. 490–494.

Glowacz, A.: Diagnostics of direct current machine based on analysis of acoustic signals with the use of symlet wavelet transform and modified classifier based on words. Eksploat. Niezawodn., t. 16, 2014.

Antoni, J.: The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process., t. 20, nr 2, 2006, p. 282–307.

Barszcz, T., Randall, R.B.: Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mech. Syst. Signal Process., t. 23, nr 4, 2009, p. 1352–1365.

Wang, Y., Liang, M.: An adaptive SK technique and its application for fault detection of rolling element bearings. Mech. Syst. Signal Process., t. 25, nr 5, 2011, p. 1750–1764.

Samuel, P.D., Pines, D.J.: A review of vibration-based techniques for helicopter transmission diagnostics. J. Sound Vib., t. 282, nr 1÷2, 2005, p. 475–508.

Bartelmus, W., Zimroz, R.: A new feature for monitoring the condition of gearboxes in non-stationary operating conditions. Mech. Syst. Signal Process., t. 23, nr 5, 2009, p. 1528–1534.

Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust Statistics: The Approach Based on Influence Functions. John Wiley & Sons2011.

Killick, R., Fearnhead, P., Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc., t. 107, nr 500, 2012, p. 1590–1598.