A COMBINED DECISION ALGORITHM FOR DIAGNOSING BEARING FAULTS USING ARTIFICIAL INTELLIGENT TECHNIQUES

Year 2018, Volume: 36 Issue: 4, 1235 - 1253, 01.12.2018

Hüseyin Metin Ertunç

Abstract

The condition monitoring of bearings has gained great importance in recent years to increase reliability and reduce production loss. Many monitoring techniques have been proposed based on different intelligent techniques and feature extraction schemes. In this study, a combined decision algorithm has been developed based on feature set that composed of statistical variables and linear prediction coefficients of time domain vibration signals. Artificial intelligent techniques, namely artificial neural networks, adaptive neuro-fuzzy inference systems and support vector machine were employed together to develop a decision making algorithm that classify the type and severity of bearing faults. Although each method can be used alone for data classification in the developed models with a limited performance, the proposed decision algorithm combines decision of each method with a synergy according to the majority of the decisions. Based on the experimental results, the proposed scheme outperformed the three methods when used alone.

Keywords

Condition monitoring, bearing fault detection, ANN, ANFIS, SVM, combined decision algorithm

References

• [1] Liang M. and Bozchalooi I.S. (2010) An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection. Mech Syst Signal Process, 24(5), 1473–1494.
• [2] Yang H, Mathew J and Ma L. (2005) Fault diagnosis of rolling element bearings using basis pursuit. Mechanical Systems and Signal Processing, 19(2), 341–356.
• [3] Janjarasjitt S, Ocak H and Loparo K.A. (2008) Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. J Sound Vib., 317(1–2), 112–126.
• [4] Ertunc H.M., Ocak H and Aliustaoglu C. (2013) ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Computing & Applications, 435–446.
• [5] Ocak H, Loparo K.A. and Discenzo F.M. (2007) Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: A method for bearing prognostics. Journal of Sound and Vibration, 302, 951–961.
• [6] Yu J. (2011) Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models. Mechanical Systems and Signal Processing, 25(7), 2573–88.
• [7] Cornell E and Owen E. (1982) Improved motors for utility applications, 2: 6759687.
• [8] Konar P and Chattopadhyay P. (2011) Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Applied Soft Computing, 11(6): 4203–4211.
• [9] McFadden P.D. and Smith J.D. (1984) Model for the vibration produced by a single point defect in a rolling element bearing. J Sound and Vibration, 96(1):69–82.
• [10] McFadden P.D. and Smith J.D. (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique - a review. Tribol Int. 17(1):3–10.
• [11] McFadden P.D. and Smith J.D. (1985) The vibration produced by multiple point defects in a rolling element bearing. J Sound Vib. 98(2), 263–273.
• [12] Ocak H. and Loparo K.A. (2004) Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data. Mechanical Systems and Signal Processing, 18(3), 515–533.
• [13] Ertunç H.M., Ocak H, Merdoğlu M. and Bayram S. (2011) Vibration analyses based localized bearing fault diagnosis under different load. 12th International Workshop on Research and Education in Mechatronics. 201–208.
• [14] Shao Y. and Nezu K. (2005) Design of mixture de-noising for detecting faulty bearing signals. J Sound Vib. 282(3–5), 899–917.
• [15] Williams T., Ribadeneria X., Billington S. and Kurfess T. (2001) Rolling Element Bearing Diagnostics in Run-To-Failure Lifetime Testing. Mech Syst Signal Process. 15(5), 979–993.
• [16] Li B., Chow M.Y., Tipsuwan Y. and Hung J.C. (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron. 47(5), 1060–1069.
• [17] Kowalski C.T and Orlowska-Kowalska T.(2003) Neural networks application for induction motor faults diagnosis. Math Comput Simul. 63(3–5), 435–448.
• [18] Zhang L., Jack L.B. and Nandi A.K. (2005) Fault detection using genetic programming. Mech Syst Signal Process. 19(2), 271–289.
• [19] Saimurugan M., Ramachandran K.I., Sugumaran V. and Sakthivel N.R. (2011) Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst Appl. 38(4), 3819–3826.
• [20] Randall R.B. and Antoni J. (2011) Rolling element bearing diagnostics-A tutorial. Mech Syst Signal Process. 25(2), 485–520.
• [21] Lei Y., He Z. and Zi Y. (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Syst Appl. 35(4), 1593-1600.
• [22] Widodo A. and Yang B.S. (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process. 21(6), 2560–2574.
• [23] Yang J., Zhang Y, and Zhu Y. (2007) Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mech Syst Signal Process., 21(5), 2012–2024.
• [24] Hu Q., He Z., Zhang Z., Zi Y. (2007) Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech Syst Signal Process. 21(2): 688–705.
• [25] Li M., Xu J., Yang J., Yang D. and Wang D. (2009) Multiple manifolds analysis and its application to fault diagnosis. Mech Syst Signal Process., 23(8), 2500–2509.
• [26] X.Chen, J. Zhou, J. Xiao, X.Zhang, H Xiao, W. Zhu, W. Fu. (2014) Fault diagnosis based on feature vector and probability neural network for rolling element bearings. Applied Mathematics and Computation, 247: 835-847.
• [27] shttp://csegroups.case.edu/bearingdatacenter