Development of A Low-Cost Analyzer for Misalignment Identification Based on Vibration and Current Analysis

Authors

  • Dedi Suryadi University of Bengkulu
  • Acraz M Bahrum University of Bengkulu
  • Novalio Daratha University of Bengkulu
  • Radzi Ambar Universiti Tun Hussein Onn Malaysia

DOI:

https://doi.org/10.37385/jaets.v6i1.5610

Keywords:

Current signature analysis, misalignment, microcontroller, sensor, vibration

Abstract

This paper proposes a low-cost analyzer based on vibration and motor current signature analysis (MCSA) using the Arduino microcontroller. Misalignment identification on induction motors with disc coupling is considered a case study. Several methods for misalignment identification have already been conducted in previous research. However, there are still issues with making the identification more reliable and well-known for further investigation. This paper describes the design and development of an analyzer for misalignment identification that is easy to use and fast utilizing a low-cost Arduino microcontroller. Furthermore, this experimental rig also consists of several additional components such as; an induction motor, shaft, and bearing dan disk coupling. In this paper, misalignment distance variables were set in 0 mm, 0.5 mm, 1 mm, and 1.5 mm, and the motor speed was varied from 500 rpm to 1500 rpm with an increment of 100 rpm.  The misalignment characteristic was experimentally achieved using an analyzer with two sensors: an accelerometer for vibration analysis and a current sensor for MCSA. As a result, a low-cost analyzer for misalignment has been successfully developed. The results show that misalignment was explicitly defined by dominant peak frequencies at 3X rpm for vibration analysis and side bands around the main frequency for MCSA. Moreover, side-band frequency increases by increasing the misalignment distance.

Downloads

Download data is not yet available.

References

Bossio, J. M., Bossio, G. R. & De Angelo, C. H. (2009). Angular misalignment in Induction Motors with Flexible Coupling. 35th Annual Conference of IEEE Industrial Electronics. pp. 1033-1038.

Carlos, V., José, B., Guillermo, B. & Gerardo, A. (2016). Misalignment detection in induction motors with flexible coupling by means of estimated torque analysis and MCSA. Mechanical Systems and Signal Processing, 80, 570-581. doi.org/10.1016/j.ymssp.2016.04.035.

Calis, H. (2014). Vibration and motor current analysis of induction motors to diagnose mechanical faults. Journal of Measurement in Engineering, 2 (4), pp. 190-198.

Chen, C. S. & Chen, J. (2011). Rotor fault diagnosis system based on individual sGA-based neural networks. Expert Syst. Appl., 38, 10822-10830. Doi: 10.1016/j.eswa.2011.02.074.

Devarajan, G., Chinnusamy, M. & Kaliappan, L. (2021). Detection and classification of mechanical faults of three phase induction motors via analysis of the thermal image pixels and adaptive neuro-fuzzy inference system. Journal of Ambient Intelligence and Humanized Computing, 12, 4619-4630. Doi. 10.1007 / s12652-020-01857-8.

Ganeriwala, S. (2024). Induction Motor Diagnostics Using Vibration and Motor Current Signature Analysis. In: Allen, M., Blough, J., Mains, M. (eds) Special Topics in Structural Dynamics & Experimental Techniques, Volume 5. SEM 2023. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-031-37007-6_21

Jang, J. Y., & Khonsari, M. M. (2015). On the characteristics of misaligned journal bearings. Lubricants, 3 (1), 27-53. Doi. 10.3390 / lubricants3010027.

Kumar, S., Lokesha, M., Kumar, K. & Srinivas, K. R. (2018). Based Vibration Fault Diagnosis Techniques for Rotating Mechanical Components. Review Paper. IOP Conf. Ser. Mater. Sci. Eng. 376 (1), 1-6. Doi. 10.1088 / 1757-899X / 376/1/012 109.

Kumar, R., Singh, M., Khan, S. et al. (2023). A State-of-the-Art Review on the Misalignment, Failure Modes and Its Detection Methods for Bearings. MAPAN 38, 265–274. https://doi.org/10.1007/s12647-022-00605-x

Li Z, Li J, Li M. (2018). Nonlinear dynamics of unsymmetrical rotor-bearing system with fault of parallel misalignment. Advances in Mechanical Engineering, 10(5). doi:10.1177/1687814018772908

Miljkovic, D. (2015). Brief review of the motor current signature analysis. IEEE Ind. Appl. Mag, 5 (1), 14-26.

Prayoga H. & Suryadi, D. (2018). Analisis LKarakteristik Vibrasi pada Paper Dryer Machine untuk Deteksi Dini Kerusakan Spherical Roller Bearing. ROTASI, 20 (2), 110-117. Doi. DOI: 10.14710/rotasi.20.2.110-117.

Rai, A. & Upadhyay, S. (2016). A Review on Signal Processing Techniques utilized in the Fault Diagnosis of Rolling Element Bearings. Tribology International, 96, 289-306. Doi. 10.1016 / j.triboint.2015.12.037.

Saavedra, P. N. & Ramírez, D. E. (2022). Vibration analysis of rotors for the identification of shaft misalignment Part 2. Experimental validation. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 218, 987–999. Doi. 10.1243 / 0954406041991198

Scheffer, C. & Girdhar, P. (2004). Machinery Vibration Analysis and Predictive Maintenance, Elsevier, Netherlands.

Sinha, J. K. (2008). Vibration-based diagnosis techniques used in nuclear power plants. An overview of experiences. Nuclear Engineering Design, 238 (9), 2439-2452. Doi : 10.1016/j.nucengdes.2008.03.007.

Suryadi, D., Meilianda, R., Suryono, A. & Munadi. (2018). Expert System for Identifying Industry Breakdown Method Using Certainty Factor. ROTASI, 20 (1), 56-62. Doi. 10.14710 / rotasi.20.1.

Suryadi, D. & Vetrano, M. (2019). Identification of unbalance and Methods Balancing on Single Rotor by Using Digital Signal Analyzer (DSA). SeNITiA, Bengkulu, Indonesia, pp. 262-266.

Suryadi D. & Pratama, M. D. (2020). Desain and Manufacture of Fault monitoring System based on Vibration Level. REKAYASA MESIN, 11 (1), 21-29. DOI: https://doi.org/10.21776/ub.jrm.2020.011.01.3.

Suryadi, D., Febriyanto, M. R. & Fitrilina. (2021). Analysis of Misalignment on Rotor Dynamic using Sound Analysis. REKAYASA MESIN, 12 (2), 487-495. DOI: https://doi.org/10.21776/ub.jrm.2021.012.02.25

Thomson, W. T. & Fenger, M. (2001). Current signature analysis to detect faults of induction motors. IEEE Industry. Applications Magazine, 7, 26-34. Doi : 10.1109 / 2943.930988.

Verma, A. K., Sarangi, S. and Kolekar, M. (2014). Experimental Investigation of Misaligment Effects on Rotor Shaft VIbration and on Stator Current Signature. Journal of Failure Analysis and Prevention, 14 (2), 125-138. Doi. 10.1007 / s11668-014-9785-7.

Verucchi, C., Bossio, J., Bossio, G. & Acosta, G. (2016). Misalignment detection in induction motors with flexible coupling by means of the estimated torque analysis and MCSA. Mechanical System and Signal Processing, 80, 570-581. Doi : 10.1016 / j.ymssp.2016.04.035.

Wu, J., Zi, Y., Chen, J. & Zhou, Z. (2019). Fault Diagnosis in Speed Variation Conditions via Improved Tacholess Order Tracking Technique. Measurement, 137, 604-616. Doi. 10.1016/j.measurement.2019.01.086.

Downloads

Published

2024-12-15

How to Cite

Suryadi, D., Bahrum, A. M., Daratha, N. ., & Ambar, R. (2024). Development of A Low-Cost Analyzer for Misalignment Identification Based on Vibration and Current Analysis. Journal of Applied Engineering and Technological Science (JAETS), 6(1), 499–507. https://doi.org/10.37385/jaets.v6i1.5610