Research Article | OPEN ACCESS
The Effect of Number of Microphones to Amplitude Changes for Detect Crack on Rotating Shaft with Blind Source Separation-Independent Component Analysis Method
1, 4Novitha L.T. Thenu, 2Dhany Arifianto, 1I Made Ariana and 3Achmad Zubaydi
1Department of Marine Engineering
2Department of Physics Engineering
3Department of Naval Architecture and Shipbuilding Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 60111
4Department of Marine Technology, Pattimura University, Ambon, 97233, Indonesia
Research Journal of Applied Sciences, Engineering and Technology 2018 3:98-106
Received: August 11, 2017 | Accepted: September 11, 2017 | Published: March 15, 2018
Abstract
This study aims to determine the amplitude change due to the addition of the number of microphones to detect the cracking of the rotating shaft. This study presents the measurement of sound data from the cracked shaft which is one component of a simple transmission system. The sound signal emitted by the cracked shaft is very complex, low frequency and comes from various sources. Statistically, the sound signal is independent and based on this nature; the mixed signal can separate. The method to separate mixed signals is blind source separation without knowing the origin and process of a signal combined with Independent Component Analysis (ICA). One of the conditions that must satisfy in the BSS method is to know the number of source signals, but in practice, what happens is not specific and complicated. This study use variable is the number of microphones and the shaft speed. The results obtained are the use of microphone array of two, three and four can increase the magnitude amplitude of the estimation signal, while the increased of the shaft speed, the magnitude amplitude of the estimation signal also increased. Use of four microphones has of higher amplitude values than a single, two and three microphones. Moreover, the lowest mean square error value increases the microphone sensitivity. Implications of using microphones array to detect a crack that cannot measure by an accelerometer and avoid distortion on the rotating shaft.
Keywords:
Amplitude, blind source separation, cracked shaft, independent component analysis, microphone array,
References
-
Jutten, C. and J. Herault, 1991. Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Process., 24(1): 1-10.
CrossRef -
Chuanchuan, W., X. Jiaqi and Z. Yonghu, 2017. Research on influence of source number estimation on application of blind source separation algorithms. Proc. Comput. Sci., 107: 379-384.
CrossRef -
Gelle, G., M. Colas and C. Serviere, 2001. Blind source separation: A tool for rotating machine monitoring by vibrations analysis? J. Sound Vib., 248(5): 865-885.
CrossRef -
Huang, W., S. Wu, F. Kong and Q. Wu, 2009. Research on blind source separation for machine vibrations. Wirel. Sens. Netw., 1: 453-457.
CrossRef -
Joho, M., H. Mathis and R.H. Lambert, 2000. Overdetermined blind source separation: Using more sensors than source signals in a noisy mixture. Proceeding of International Conference on Independent Component Analysis and Blind Signal Separation ICA 2000. Helsinki, Finland, June 19-22, pp: 81-86.
Direct Link -
Lakis, A.A. and A.M. Mohammadi, 2012. Multi-component machine monitoring and fault diagnosis using blind source separation and advanced vibration analysis. Ph.D. Thesis, Universite de Montreal.
Direct Link -
Nishikawa, T., 2005. Blind source separation based on multistage independent component analysis. Ph.D. Thesis, Department of Information Processing, Nara Institute of Science and Technology.
Direct Link -
Nishikawa, T., H. Saruwatari and K. Shikano, 2003. Blind source separation of acoustic signals based on multistage ICA combining frequency-domain ICA and time-domain ICA. IEICE Transactions on Fundamentals of Electronics, Communications, and Computer Science, E86(4): 846-858.
Direct Link -
Popescu, T.D., 2010. Blind separation of vibration signals and source change detection -Application to machine monitoring. Appl. Math. Modell., 34: 3408-3421.
CrossRef -
Serviere, C. and P. Fabry, 2004. Blind source separation of noisy harmonic signals for rotating machine diagnosis. J. Sound Vib., 272(1-2): 317-339.
CrossRef -
Tang, G., G. Luo, W. Zhang, C. Yang and H. Wang, 2016. Underdetermined blind source separation with variational mode decomposition for compound roller bearing fault signals. Sensors, 16(6): E897.
CrossRef PMid:27322268 PMCid:PMC4934323 -
Thenu, N.L.T., D. Arifianto, I.M. Ariana and A. Zubaydi, 2017. An initial step in a blind source separation method to determine the baseline signal with acoustic emission. J. Theor. Appl. Inform. Technol., 95(1): 220-228.
Direct Link -
Ypma, A., A. Leshem and R.P.W. Duin, 2002. Blind separation of rotating machine sources: Bilinear forms and convolutive mixtures. Neurocomputing, 49(1-4): 349-368.
CrossRef -
Zhong, Z.M., J. Chen, P. Zhong and J.B. Wu, 2006. Application of the blind source separation method to feature extraction of machine sound signals. Int. J. Adv. Manuf. Technol., 28(9-10): 855-862.
CrossRef -
Zhou, W. and D. Chelidze, 2007. Blind source separation based vibration mode identification. Mech. Syst. Signal Process., 21(8): 3072-3087.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|