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Remaining Life-Time Assessment of Gear Box Using Regression Model

Affiliations

  • School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu, India
  • School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu
  • Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing,Airport Road, IIITDM Jabalpur Campus, Khamaria, Jabalpur – 482005, Madhya Pradesh, India
  • Department of Mechanical Engineering, Inha University, Korea, Republic of

Abstract


Objectives: The main objective of this study is to develop a model which can able to predict the remaining life time working of a gearbox using vibration signals. Method: This study is considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. In this research, histogram features are extracted from vibration signals, feature selection are carried out using J48 algorithm and different regression models were built to predict the reaming lifetime assessment of a gearbox. Findings: In this study, the J48 algorithm was used and the regression was found to be 0.8944 for Gaussian model. This is a novel approach to finding the life prediction of gearbox using histogram and regression model. Improvements: This algorithm is applicable for real-time analysis and further the condition monitoring can be carried out using different algorithms with less computation time.

Keywords

Assessment, Fault Diagnosis, Gearbox, Histogram Features, Life Time, Multiple Regression, Sound Signals.

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References


  • Staszewski WJ, Worden K, Tomlinson GR. Time–frequency analysis in gearbox fault detection using the Wigner–Ville distribution and pattern recognition. Mechanical Systems and Signal Processing. 1997 Sep 30; 11(5):673–92.
  • Zheng H, Li Z, Chen X. Gear fault diagnosis based on continuous wavelet transform. Mechanical systems and signal processing. 2002 Mar 31; 16(2):447–57.
  • Widodo A, Yang BS. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing. 2007 Aug 31; 21(6):2560–74.
  • Lei Y, Zuo MJ, He Z, Zi Y. A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Systems with Applications. 2010 Mar 31; 37(2):1419–30.
  • Saravanan N, Ramachandran KI. Incipient gear box fault diagnosis using Discrete Wavelet Transform (DWT) for feature extraction and classification using Artificial Neural Network (ANN). Expert Systems with Applications. 2010 Jun 30; 37(6):4168–81.
  • Wang X, Makis V. Autoregressive model-based gear shaft fault diagnosis using the Kolmogorov–Smirnov test. Journal of Sound and Vibration. 2009 Nov 13; 327(3):413–23.
  • Bartelmus W, Zimroz R. A new feature for monitoring the condition of gearboxes in non-stationary operating conditions.Mechanical Systems and Signal Processing. 2009 Jul 31; 23(5):1528–34.
  • Bartelmus W, Zimroz R. Vibration condition monitoring of planetary gearbox under varying external load. Mechanical Systems and Signal Processing. 2009 Jan 31; 23(1):246–57.
  • Raj GBM, Sugumaran V. Prediction of work piece hardness using artificial neural network. International Journal of Design and Manufacturing Technology (IJDMT). 2010 Aug; 1(1):29–44.
  • Shao Y, Liang J, Gu F, Chen Z, Ball A. Fault prognosis and diagnosis of an automotive rear axle gear using a RBF-BP neural network. In Journal of Physics: Conference Series.IOP. 2011; 305(1):1–11.
  • Nanadic N, Ardis P, Hood A, Thurston M, Ghoshal A, Lewicki D. Comparative study of vibration condition indicators for detecting cracks in spur gears. In the 69th Annual Forum of American Helicopter Society (AHS) International, Phoenix, Arizona; 2013 May 21–3.
  • Assaad B, Eltabach M, Antoni J. Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes. Mechanical Systems and Signal Processing. 2014 Jan 31; 42(1):351–67.
  • Li C, Sanchez RV, Zurita G, Cerrada M, Cabrera D, Vásquez RE. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing. 2015 Nov 30; 168:119–27.
  • Zheng Z, Jiang W, Wang Z, Zhu Y, Yang K. Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mechanism and Machine Theory. 2015 Sep 30; 91:151–67.
  • Elangovan M, Ramachandran KI, Sugumaran V. Studies on bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications. 2010 Mar 15; 37(3):2059–65.
  • Muralidharan V, Sugumaran V, Indira V. Fault diagnosis of monoblock centrifugal pump using SVM. Engineering Science and Technology, an International Journal. 2014 Sep 30; 17(3):152–7.
  • Lou X, Loparo KA. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical systems and signal processing. 2004 Sep 30; 18(5):1077–95.
  • Lu W, Jiang W, Yuan G, Yan L. A gearbox fault diagnosis scheme based on near-field acoustic holography and spatial distribution features of sound field. Journal of Sound and Vibration. 2013 May 13; 332(10):2593–610.
  • Amarnath M, Krishna IRP. Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings. Science, Measurement and Technology, IET. 2012 Jul; 6(4):279–87.
  • Shao R, Hu W, Wang Y, Qi X. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement. 2014 Aug 31; 54:118–32.
  • Sugumaran V, Jain D, Amarnath M, Kumar H. Fault diagnosis of helical gear box using decision tree through vibration signals. International Journal of Performability Engineering. 2013 Mar 1; 9(2):221–34.
  • Ali YH, Rahman RA, Hamzah RI. Regression modelling for spur gear condition monitoring through oil film thickness based on acoustic emission signal. Modern Applied Science. 2015 Aug 1; 9(8):21–8.
  • Zamanian AH, Ohadi A. Gear fault diagnosis based on Gaussian correlation of vibrations signals and wavelet coefficients. Applied Soft Computing. 2011 Dec 31; 11(8):4807–19.
  • Gorinevsky D, Kim SJ, Beard S, Boyd S, Gordon G.Optimal estimation of deterioration from diagnostic image sequence. Institute of Electrical and Electronics Engineers (IEEE) Transactions on Signal Processing. 2009 Mar; 57(3):1030–43.
  • Rousseeuw PJ. Least median of squares regression. Journal of the American statistical association. 1984 Dec 1; 79(388):871–80.
  • Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review. 1958 Nov; 65(6):386–408.
  • Çakır A, Çalış H, Küçüksille EU. Data mining approach for supply unbalance detection in induction motor. Expert Systems with Applications. 2009 Nov 30; 36(9):11808–13.
  • Gunn SR. Support vector machines for classification and regression. ISIS technical report; 1998 May 10. p. 1–53.
  • Bartelmus W, Zimroz R. A new feature for monitoring the condition of gearboxes in non-stationary operating conditions.Mechanical Systems and Signal Processing. 2009 Jul 31; 23(5):1528–34.
  • Feng Z, Zuo MJ, Chu F. Application of regularization dimension to gear damage assessment. Mechanical Systems and Signal Processing. 2010 May 31; 24(4):1081–98.
  • Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KR.Improvements to the SMO algorithm for SVM regression.Institute of Electrical and Electronics Engineers (IEEE) Transactions on Neural Networks. 2000 Sep; 11(5):1188– 93.
  • Joshuva A, Sugumaran V, Amarnath M. Selecting kernel function of support vector machine for fault diagnosis of roller bearings using sound signals through histogram features. International Journal of Applied Engineering Research. 2015; 10(68):482–7.
  • Ferrari T, Gini G. A new predictive model of Mutagenicity, with statistical analysis and validation using data-mining tools in WEKA. In Poster presented at SCARLET workshop; 2008 Apr 2. p. 2–4.

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