Machine Health Monitoring Using An Innovative Mechanical Approach

Vera Romayasari(1*), Sigit Auliana(2), Gagah Dwiki Putra Aryono(3),

(1) Universitas Bina Bangsa, Indonesia
(2) Universitas Bina Bangsa, Indonesia
(3) Universitas Bina Bangsa, Indonesia
(*) Corresponding Author

Abstract


In today's world, machines are essential in daily life, requiring efficient and safe operation. Tools have been developed to assess machine health by monitoring power usage, temperature, noise, and vibrations. Anomalies in these parameters can indicate potential defects. FFT analyzers, commonly used for vibration measurement, are often too costly for small businesses and may lack the ability to measure speed, temperature, or power usage. This project aims to create a low-cost alternative for health monitoring systems, capable of measuring vibrations, noise, temperature, speed, and power consumption. Integrating an Arduino Uno R3 with sensors and a MATLAB 2018b GUI provides an affordable solution, catering to small firms unable to invest in expensive FFT analyzers

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References


M. Yazdi, “Maintenance Strategies and Optimization Techniques,” 2024, pp. 43–58. doi: 10.1007/978-3-031-53514-7_3.

R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, “Deep learning and its applications to machine health monitoring,” Mech. Syst. Signal Process., vol. 115, pp. 213–237, Jan. 2019, doi: 10.1016/j.ymssp.2018.05.050.

A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliab. Eng. Syst. Saf., vol. 215, p. 107864, Nov. 2021, doi: 10.1016/j.ress.2021.107864.

C. Virginia Anikwe et al., “Mobile and wearable sensors for data-driven health monitoring system: State-of-the-art and future prospect,” Expert Syst. Appl., vol. 202, p. 117362, Sep. 2022, doi: 10.1016/j.eswa.2022.117362.

M. Greenhawt, J. Oppenheimer, and C. D. Codispoti, “A Practical Guide to Understanding Cost-Effectiveness Analyses,” J. Allergy Clin. Immunol. Pract., vol. 9, no. 12, pp. 4200–4207, Dec. 2021, doi: 10.1016/j.jaip.2021.10.006.

D. Glandon et al., “The State of Cost-Effectiveness Guidance: Ten Best Resources for CEA in Impact Evaluations,” J. Dev. Eff., vol. 15, no. 1, pp. 5–16, Jan. 2023, doi: 10.1080/19439342.2022.2034916.

H. Hegab, I. Shaban, M. Jamil, and N. Khanna, “Toward sustainable future: Strategies, indicators, and challenges for implementing sustainable production systems,” Sustain. Mater. Technol., vol. 36, p. e00617, Jul. 2023, doi: 10.1016/j.susmat.2023.e00617.

A. Soliman et al., “Innovative construction material technologies for sustainable and resilient civil infrastructure,” Mater. Today Proc., vol. 60, pp. 365–372, 2022, doi: 10.1016/j.matpr.2022.01.248.

Z. Xu, Y. Guo, and J. Homer Saleh, “Multi-objective optimization for sensor placement: An integrated combinatorial approach with reduced order model and Gaussian process,” Measurement, vol. 187, p. 110370, Jan. 2022, doi: 10.1016/j.measurement.2021.110370.

M. Javaid, A. Haleem, R. P. Singh, S. Rab, and R. Suman, “Significance of sensors for industry 4.0: Roles, capabilities, and applications,” Sensors Int., vol. 2, p. 100110, 2021, doi: 10.1016/j.sintl.2021.100110.

M. Romanssini, P. C. C. de Aguirre, L. Compassi-Severo, and A. G. Girardi, “A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery,” Eng, vol. 4, no. 3, pp. 1797–1817, Jun. 2023, doi: 10.3390/eng4030102.

T. D. Popescu, D. Aiordachioaie, and A. Culea-Florescu, “Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview,” Int. J. Adv. Manuf. Technol., vol. 118, no. 9–10, pp. 2883–2899, Feb. 2022, doi: 10.1007/s00170-021-07703-1.

M. Vishwakarma, R. Purohit, V. Harshlata, and P. Rajput, “Vibration Analysis & Condition Monitoring for Rotating Machines: A Review,” Mater. Today Proc., vol. 4, no. 2, pp. 2659–2664, 2017, doi: 10.1016/j.matpr.2017.02.140.

A. I. Paganelli et al., “Real-time data analysis in health monitoring systems: A comprehensive systematic literature review,” J. Biomed. Inform., vol. 127, p.

, Mar. 2022, doi: 10.1016/j.jbi.2022.104009.

H. K. Kondaveeti, N. K. Kumaravelu, S. D. Vanambathina, S. E. Mathe, and S. Vappangi, “A systematic literature review on prototyping with Arduino: Applications, challenges, advantages, and limitations,” Comput. Sci. Rev., vol. 40, p. 100364, May 2021, doi: 10.1016/j.cosrev.2021.100364.

K. Gnana Sheela and A. Rose Varghese, “Machine Learning based Health Monitoring System,” Mater. Today Proc., vol. 24, pp. 1788–1794, 2020, doi: 10.1016/j.matpr.2020.03.603.

A. R. Al-Ali et al., “An IoT-Based Road Bridge Health Monitoring and Warning System,” Sensors, vol. 24, no. 2, p. 469, Jan. 2024, doi: 10.3390/s24020469.

K. Mettert, C. Lewis, C. Dorsey, H. Halko, and B. Weiner, “Measuring implementation outcomes: An updated systematic review of measures’ psychometric properties,” Implement. Res. Pract., vol. 1, p. 263348952093664, Jan. 2020, doi: 10.1177/2633489520936644.




DOI: https://doi.org/10.30645/kesatria.v5i3.450

DOI (PDF): https://doi.org/10.30645/kesatria.v5i3.450.g445

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