Abstract
Mechanical power transmission systems are an indispensable part of the industrial process. The most complex equipment of these processes is the gear systems. Among the gear systems the worm gearboxes are used in various applications, especially those that need high transmission ratios in one reduction stage. However, worm wheel manifests defects easily because it is made of soft material, in comparison with the worm. The stress on each tooth surface may increase because of overload, shock load, cyclic load change, gear misalignment, etc. This often causes pitting faults in worm gearboxes. This paper focuses on the detection of localized pitting damages in a worm gearbox by a vibration visualization method and artificial neural networks (ANNs). For this purpose, the vibration signals are converted into an image to display and detect pitting defects on the worm wheel tooth surface. In addition, statistical parameters of vibration signals in the time and frequency domains are used as an input to ANN for multi-class recognition. Later, the results obtained from ANN are compared for both axial and radial vibration. It is found that the ANN can classify with high accuracy for any sample of the vibration data obtained from the radial direction according to fault severity levels.
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References
Ozturk H 2006 Gearbox health monitoring and fault detection using vibration analysis. Ph.D. Thesis, Dokuz Eylül University, İzmir
Elasha F, Ruiz-Cárcel C, Mba D, Kiat G, Nze I and Yebra G 2014 Pitting detection in worm gearboxes with vibration analysis. Eng. Fail. Anal. 42: 366–376
Ümütlü R C, Hızarcı B, Ozturk H and Kıral Z 2016 Pitting detection in a worm gearbox using artificial neural networks. In: Proceedings of INTER-NOISE 2016—45th International Congress and Exposition on Noise Control Engineering: Towards a Quieter Future, pp. 2718–2726
Dalpiaz G and Meneghetti U 1991 Monitoring fatigue cracks in gears. NDT&E Int. 24(6): 303–306
Wang W J and McFadden P D 1993 Early detection of gear failure by vibration analysis I. Calculation of the time–frequency distribution. Mech. Syst. Sig. Process. 7(3): 193–203
Wang W J and McFadden P D 1996 Application of wavelets to gearbox vibration signals for fault detection. J. Sound Vib. 192(5): 927–939
Samuel P D and Pines D J 2005 A review of vibration-based techniques for helicopter transmission diagnostics. J. Sound Vib. 282(1): 475–508
Wu S, Zuo M J and Parey A 2008 Simulation of spur gear dynamics and estimation of fault growth. J. Sound Vib. 317(3): 608–624
Peng Z and Kessissoglou N 2003 An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis. Wear 255(7): 1221–1232
Vähäoja P, Lahdelma S and Leinonen J 2006 On the condition monitoring of worm gears. In: Mathew J, Kennedy J, Ma L, Tan A, Anderson D (Eds) Engineering Asset Management. London: Springer, pp. 332–343
Elforjani M, Mba D, Muhammad A and Sire A 2012 Condition monitoring of worm gears. Appl. Acoust. 73(8): 859–863
Vernekar K, Kumar H and Gangadharan K V 2017 Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm. Sādhanā 42(7): 1143–1153
Kuo R J 1995 Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic. Eng. Appl. Artif. Intell. 8(1): 25–34
McCormick A C and Nandi A K 1997 Classification of the rotating machine condition using artificial neural networks. Proc. Inst. Mech. Eng. Pt. C: J. Mech. Eng. Sci. 211(6): 439–450
Samanta B and Al-Balushi K R 2001 Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system. Proc. Inst. Mech. Eng. Pt. I: J. Syst. Contr. Eng. 215(3): 199–207
Samanta B, Al-Balushi K R and Al-Araimi S A 2003 Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng. Appl. Artif. Intell. 16(7): 657–665
Kang Y, Wang C C, Chang Y P, Hsueh C C and Chang M C 2006 Certainty improvement in diagnosis of multiple faults by using versatile membership functions for fuzzy neural networks. In: Proceedings of the International Symposium on Neural Networks, pp. 370–375
Kang Y, Wang C C and Chang Y P 2007 Gear fault diagnosis in time domains by using Bayesian networks. In: Theoretical Advances and Applications of Fuzzy Logic and Soft Computing, pp. 741–751
Abad M A, Ahmadi H, Moosavian A, Khazaee M, Kohan M R and Mohammadi M. 2013 Discrete wavelet transform and artificial neural network for gearbox fault detection based on acoustic signals. J. Vibroeng. 15(1): 459–463
Jia-Li T, Yi-Jun L and Fang-Sheng W 2010 Levenberg–Marquardt neural network for gear fault diagnosis. In: Proceedings of the 2010 2nd International Conference on Networking and Digital Society, Wenzhou, China, 30–31 May 2010, vol. 1, pp. 134–137
Waqar T and Demetgul M 2016 Thermal analysis MLP neural network based fault diagnosis on worm gears. Measurement 86: 56–66
Shahriar M R, Ahsan T and Chong U 2013 Fault diagnosis of induction motors utilizing local binary pattern-based texture analysis. EURASIP J. Image Video Process. 1: 29
Mathworks 2019 Parula. https://www.mathworks.com/help/matlab/ref/parula.html
Ham F M and Kostanic I 2000 Principles of neurocomputing for science and engineering. USA: McGraw-Hill Higher Education
Mathworks 2019 Types of morphological operations. https://www.mathworks.com/help/images/morphological-dilation-and-erosion.html
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Ümütlü, R.C., Hizarci, B., Ozturk, H. et al. Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN. Sādhanā 45, 22 (2020). https://doi.org/10.1007/s12046-019-1263-1
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DOI: https://doi.org/10.1007/s12046-019-1263-1