Diagnosa Kerusakan Bearing Menggunakan Principal Component Analysis (PCA) dan Naïve Bayes Classifier

DOI: https://doi.org/10.21456/vol6iss2pp114-123

Article Info
Submitted: 25-09-2016
Published: 26-12-2016
Section: Research Articles

This research is discussing about the usage of data mining which addressed for bearing fault diagnosis. Bearing was one of the essential parts in industry machinery. Bearing was used to reduce machines frictions or could be a moving component which oppressed each other.  This fault diagnosis can avoid loss and damage of other machines components. This research was started with data preprocessing using wavelet discrete transformation, feature extraction, feature reduction using Principal Component Analysis (PCA), and classification process using Naïve Bayes classifier methods. Naïve Bayes Classifier is a classification method which based on probability and Bayesian theorem. Output of these method shows that Naïve Bayes classification have a good performance which shown by a good accuracy in each data test.

Keywords

Data mining; Fault Diagnosis; PCA; Naïve Bayes Classification

  1. Dwi Pudyastuti 
    Universitas Diponegoro , Indonesia
    Magister Sistem Informasi
  2. Toni Prahasto 
    Universitas Diponegoro
    Fakultas Teknik
  3. Achmad Widodo 
    Universitas Diponegoro
    Fakultas Teknik

Anant, K.S., Dowla, F.U., 1997. Wavelet Transform Methods for Phase Identification in Three-Component Seismograms, Bulletin of Seismological Society of America, Vol. 87, No. 6, 1598 – 1612.

Bengtsson, M., Olsson, E., Funk, P., Jackson, M., 2004. Technical design of condition based maintenance system-a case study using sound analysis and case based reasoning, The 8th Maintenance and Reability Conference, Knoxville USA, 2-5 Mei.

Berger, C., 1990. Statistical Inference, Pacific Grove, California.

Berry, M., Linoff, G.S., 2004. Data Mining Techniques Second Edition, Wiley Publishing, Inc., New York.

Bolstad, W.M., 2007. Introduction to Bayesian Statistics Second Edition, A John Wiley & Sons, Inc., New York.

Chu, P.S., Xin, Z., 2011. Bayesian analysis for extreme climatic events: A review, Hawai, Journal of Atmospheric Research 102, 243-262

Daubechies, C.I., 1992. Ten Lectures on Wavelet, SIAM, Philadelphia.

Foster, D.J., Mosher, C.C., Hassanzadeh, S., 1994. Wavelet Transform Methods for Geophysical Applications, Proceeding of SPIE 2033, San Diego, 11 Juli, 1465–1468.

Hand, J., Kamber M. 2001. Data Mining: Concepts and Techniques, Academic Press, Cambridge.

Helle, A., 2006, Development of prognostic concepts and tools, VTT Symposium 243, Espoo, 12 Desember, 5-12.

Hsu, C.C., Ping, H.Y., Keng-Wei, C., 2008. Extended Naïve Bayes classifier for mixed data, Journal of Informations Sciences 163, 103-122

ISO 13373-1, 2002. Condition Monitoring and Diagnostics of Machines-Vibration Condition Monitoring, First Edition, International Organization Standart, Geneva.

Jiang, X., Sankaran, M., 2007. Bayesian risk-based decision method for model validation under uncertainty, Journal of Reliability Engineering & System Safety 92, 707-718

Joliffe, I. J., 1986. Principal Component Analysis, Springer, New York.

Koc, L.,Thomas, M., Shahram, S., 2012. A network intrusion detection system based on a hidden Naïve Bayes multiclass classifier, Journal of Expert Systems with Applications 39, 13492-13500

Li, D., Zhen, Y.H., Feng, L.X., 2013. Prediction analysis of a wastewater treatment system using a Bayesian network, Journal of Environmental Modelling & Software 40, 140-150

Li, G., Jing, S., 2012. Application of Bayesian methods in wind energy conversion systems, Journal of Renewable Energy 43, 1-8

Menon, S., Schoes, J. N., Hamza, R., Bush, D., 2000. Wavelet-based acoustic emission detection method with adaptive thresholding, Proceeding of SPIE 3986, Newport Beach, 6 Maret, 71-77.

Newland, D., E., 1994. Wavelet analysis of vibration, Journal of Vibration and Acoustics, 409-417

Niu, X., Zhu, L., dan Ding, H., 2005. New Statistical Moments for Detection of Defects in Rolling Element Bearing, Journal of Manufacture Technology, 1268-1274.

Polikar, R., 1998. Multi Resolution Analysis: The Discrete Wavelet Transform, Durham Computation Center, Iowa.

Sripathi, D., 2003. Efficient Implementations of Discrete Wavelet Transform using FPGAs, Florida State University, Florida.

Terzija, N., 2006. Robust Digital Image Watermarking Algorithms for Copyright Protection, Universität Duisburg, Essen.

Thorp, B., 2007. What is predictive maintenance and how it has benefitted Seminole Electric Corp. Inc., Journal of Asset Management and Maintenance, 20(2), 14-21.

Widodo, A., 2011. Application of Intelligent System for Machine Fault Diagnosis and Prognosis, Badan Penerbit Universitas Diponegoro, Semarang.