Deteksi Cacat Bantalan Poros Engkol Motor Pembakaran dalam Menggunakan Spektrum Envelope

Berli Paripurna Kamiel

Abstract


Bantalan poros engkol adalah salah satu komponen penting pada motor pembakaran-dalam (IC engine) yang dapat mengalami cacat akibat pembebanan berulang dan temperatur tinggi. Kerusakan bantalan menyebabkan penurunan kinerja mesin yang jika tidak segera dilakukan tindakan perawatan dapat mengakibatkan kerusakan total. Analisis spektrum getaran adalah teknik utama yang digunakan untuk mendeteksi cacat bantalan. Namun demikian, spektrum tidak efektif untuk mendeteksi cacat bantalan di mesin-mesin pembakaran-dalam karena menghasilkan background noise yang sangat besar sehingga menutup amplitudo getaran bantalan. Penelitian ini mengusulkan prosedur pre-processing sinyal getaran untuk mengeliminasi frekuensi-rendah-amplitudo-tinggi dan menguatkan amplitudo dari frekuensi bantalan. Penelitian ini menerapkan analisis envelope pada bantalan poros engkol motor pembakaran dalam 2 langkah. Eksperimen pada rig uji menggunakan 3 kondisi bantalan single row dari Danmotor yaitu bantalan normal (tidak cacat), cacat lintasan dalam ukuran 0,25 mm dan 0,50 mm. Kecepatan poros dijaga konstan pada variasi 1500 RPM dan 2000 RPM. Sensor akselerometer diletakkan pada blok mesin dekat dengan lokasi poros engkol untuk merekam sinyal getaran menggunakan kecepatan sampling 51200 Hz. Hasil penelitian menunjukan bahwa spektrum tidak dapat mendeteksi cacat bantalan untuk semua ukuran cacat dan kecepatan poros sedangkan spektrum envelope berhasil menampilkan BPFI dan side band yang dapat digunakan untuk mendeteksi cacat bantalan dan menentukan level cacatnya.

 

A bearing on the crankshaft is one of critical component of the IC engine which may fault due to cyclic loading and high temperature. The vibration spectrum analysis is the main technique used to detect faulty bearings. However, it is not effective because IC engine produces a very large background noise which immerses bearing vibration amplitude. The study proposes a signal pre-processing procedure to eliminate low-frequency high-amplitude vibration and magnifies the amplitude of the bearing frequency. This paper applies envelope analysis on crankshaft bearings of two-strokes IC engine. The experiments on the test rig uses 3 condition of single row bearing from Danmotor i.e. normal bearing (healthy), inner race fault of 0,25 mm and 0,50 mm. The shaft speed of 1500 RPM and 2000 RPM is used during experiment. An accelerometer sensor is placed on the engine block near the location of the crankshaft to record vibration signals using 51200 Hz sampling rate. The result shows that spectrum fails to detect faulty bearing for all size defects and shaft speed. Meanwhile, envelope spectrum shows obvious BPFI and its side bands which can be used to detect and localize bearing fault.

 

 


Keywords


bantalan, poros engkol, motor pembakaran dalam, spektrum envelope, akselerometer

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References


I. Yani, Y. Resti, and F. Burlian, "Identification of bearing failure using signal vibrations," Journal of Physics: Conference Series, vol. 1007, no. 1, p. 012067, 2018.

M. Kunli and W. Yunxin, "Fault diagnosis of rolling element bearing based on vibration frequency analysis," in 2011 Third International Conference on Measuring Technology and Mechatronics Automation, pp. 198-201, 2011.

S. Liu, F. Gu, and A. Ball, "Detection of engine valve faults by vibration signals measured on the cylinder head," Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 220, no. 3, pp. 379-386, 2006.

Y.-C. Choi and Y.-H. Kim, "Fault detection in a ball bearing system using minimum variance cepstrum," Measurement Science and Technology, vol. 18, no. 5, pp. 1433-1440, 2007.

A. Tiwari and R. Jatola, "Fault detection in bearing using envelope analysis," Indian Journal Of Research, vol. 3, no. 5, pp. 75-78, 2013.

D. Hochmann and E. Bechhoefer, "Envelope bearing analysis: theory and practice," in 2005 IEEE Aerospace Conference, pp. 3658-3666, 2005.

B. Betea, P. Dobra, M.-C. Gherman, and L. Tomesc, "Comparison between envelope detection methods for bearing defects diagnose," IFAC Proceedings Volumes, vol. 46, no. 6, pp. 137-142, 2013.

D. Kateris, D. Moshou, X.-E. Pantazi, I. Gravalos, N. Sawalhi, and S. Loutridis, "A machine learning approach for the condition monitoring of rotating machinery," Journal of Mechanical Science and Technology, vol. 28, no. 1, pp. 61-71, 2014.

C. Z. Tan and M. S. Leong, "An experimental study of cavitation detection in a centrifugal pump using envelope analysis," Journal of System Design and Dynamics, vol. 2, no. 1, pp. 274-285, 2008.

B. P. Kamiel, M. Mulyani, and S. Sunardi, "Deteksi cacat bantalan bola pada pompa sentrifugal menggunakan spektrum getaran," Semesta Teknika, vol. 20, no. 2, pp. 204-215, 2017.




DOI: https://doi.org/10.18196/jmpm.3243

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