Technical diagnostics vibration of rotating machinery
Keywords:
Diagnosis, Vibration Signal, Machine Learning, Rotating Machine, AlgorithmAbstract
This paper aims to present traditional diagnostic techniques and emerging diagnostic technologies. To diagnose the vibration of rotating machinery from causes, imbalances, faults in ball bearings. Axial misalignment and misalignment There are a variety of traditional diagnostic techniques, such as time-domain vibration curve diagnostics. Frequency Graph Diagnostics diagnostics by observing worn parts, etc., and emerging diagnostic technologies in the past decade are Machine Learning (ML) with a comprehensive review of the latest applications of ML algorithms used for diagnosing vibration causes in rotary machines. which is currently diagnosing from machine learning It is more feasible and widely used to determine the cause of vibration. with high efficiency and diagnostic accuracy The article also explains the history of the transition from traditional methods to ML methods.
References
ANALYSIS SOFYWARE FOR ROTATING MACHINES. Retrieved 10 August 2021 from
http://cuir.car.chula.ac.th/bitstream/123456789/1268/3/Jutiroj.pdf
Lokesha, M., Kumar, S., & Kiran, K. MV. (2021).
Machine Learning and Wavelet Analysis for Diagnosis & Classification of Faults in Gears, Manufacturing Engineering, vol.19. https://94.176.104.80/PDF_AJME_2021_2/L8.pdf
Muhlisin, L., Agung, F., & Efendi, R. (2021).
Development of vibration monitoring system for rotating machine models using fast fourier transform, Sains Dan Teknologi, 2021, vol. 17, no. 01, pp.35–42.
doi: 10.36055/tjst.v17i1.9763
Peng, Z., & Kessissoglou, N. (2003). An integrated
approach to fault diagnosis of machinery using wear debris and vibration analysis, Wear 255, 1221–1232.
doi: 10.1016/S0043-1648(03)00098-X
Pinheiro, A. A., Brandao, M. L., & Costa, C. D.
(2019). Vibration Analysis of Rotary Machines Using Machine Learning Techniques, Engineering Research and Science, Vol. 4, No. 2.
doi: 10.24018/ejers.2019.4.2.1128
Ponci, L. P., Creci, G., & Menezes, C. J. (2021).
Simplified procedure for vibration analysis and dynamic balancing in mechanical systems with beats frequency, Measurement, vol. 174.
doi: 10.1016/j.measurement.2021.109056
Provezza, L., Bodini, I., Petrogalli, C., Lancini, M.,
Solazzi, L., & Faccoli, M. (2021). Monitoring the Damage Evolution in Rolling Contact Fatigue Tests Using Machine Learning and Vibrations, Metals, vol.11, pp.283.
doi: 10.3390/met11020283
Rirkvaleekul, P. (2021). Necessities Machine
Learning , Retrieved 10 August 2021 from
https://www.twinsynergy.co.th/สิ่งจำเป็นที่สาย-machine-learning-ต้อง/
Sivadee. (2018). MACHINE LEARNING–Definition and
usage examples, Retrieved 10 August 2021 from https://www.toolmakers.co/machine-learning
Yongying, N. (2019). A Deep Learning Approach on
Road Detection from Unmanned Aerial Vehicle-Based Images in Rural Road Monitoring. Retrieved 10 August 2021 from http://www.agi.nu.ac.th/nred/Document/is-PDF/2562/geo_2562_05_FullPaper.pdf
Zarei, J., Tajeddini, M. A., & Hamid, R. K. (2014).
Vibration analysis for bearing fault detection and classification using an intelligent filter, Mechatronics, Volume 24, Issue 2, pp. 151-157.
doi: 10.1016/j.mechatronics.2014.01.003
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กองบรรณาธิการขอสงวนสิทธิ์ในการปรับปรุงแก้ไขตัวอักษรและค่าสะกดต่าง ๆ ที่ไม่ถูกต้อง และต้นฉบับที่ได้รับการตีพิมพ์ในวารสารเทคโนโลยีและนวัตกรรม ถือเป็นกรรมสิทธิ์ของสถาบันวิจัยและพัฒนา มหาวิทยาลัยเทคโนโลยีราชมงคลล้านนา และผลการพิจารณาคัดเลือกบทความตีพิมพ์ในวารสารให้ถือเป็นมติของกองบรรณาธิการเป็นที่สิ้นสุด
