Total views : 331

Fault Diagnosis of Helical Gearbox Using Vibration Signals through K-Star Algorithm and Wavelet Features


  • School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, India
  • Department of Mechanical Engineering, IIITDM, Jabalpur - 482005, Madhya Pradesh, India


Objectives: Gears are machine elements that transmit motion by successively engaging teeth. In technical terms, gears are used to transmit motion. Fault in gears can lead to major problems which may end up in affecting the gear’s functionality. Hence, fault diagnosis at an initial stage is of utmost importance to reduce losses that might occur. Continuous monitoring of the gears is very necessary. Vibration signals recorded for good and faulty conditions are used for fault detection in the helical gearbox. The fault diagnosis is done using feature extraction, feature selection and feature classification. Firstly, feature extraction was carried out using MATLAB software. Feature selection was done using J48 classifier. The classification accuracies for different conditions were calculated and compared by using K-Star classifier and the results obtained were very promising. Methods/Analysis: Vibration signals were obtained from the experimental set up of the helical gearbox. The recorded signals were then used for feature extraction using MATLAB through different wavelet features. The total number of signals extracted was 448 with each class consisting of 64 signals. The families of wavelets taken into account for fault diagnosis were Haar, Discrete Mayer, Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflet and Symlets. In wavelet selection, signals were split into different frequency components and each component was studied with a resolution matched to its scale. J48 classifier was used to carry out the feature selection process and decision tree was obtained for Sym 8 wavelet. The best combination of nodes was visualized and further feature classification was done on these nodes. By varying the global blends the optimum number of objects was selected to obtain the highest classification accuracy. Finding: The classification accuracy for the built model was 91.74%. The data extracted from the vibration signal is used for the classification purpose. This maximum classification accuracy was obtained with K star algorithm. Novelty/Improvements: Wavelet selection was different from Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes. K Star algorithm was used to carry out the fault diagnosis.


Decision Tree, Gearbox Fault Diagnosis, J48 Classifier, K-Star Classifier, Wavelet

Full Text:

 |  (PDF views: 271)


  • Amarnath M, Jain D, Sugumaran V, Kumar H. Fault diagnosis of helical gearbox using decision tree and best-first tree. International Journal of Research in Mechanical Engineering. 2013 Jul-Sep; 1(1):22–33.
  • Patidar S, Soni PK. An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults. International Journal of Engineering Trends and Technology. 2013 May; 4(5):1804–9.
  • Zhang H, Zhou H, Shi X, Huang J, Sun J, Huang L. Research on rolling bearing fault diagnosis with adaptive frequency selection based on LabVIEW. International Journal of Control and Automation. 2014 Apr; 7(3):93–100.
  • Villa LF, Renones A, Peran JR, De Miguel LJ. Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load mechanical systems and signal processing. Mechanical Systems and Signal Processing. 2012 May; 29:436–46.
  • Gangadhar N, Kumar H, Narendranath S., Sugumaran V. Fault diagnosis of single point cutting tool through vibration signal using decision tree algorithm. Procedia Materials Science. 2014 Sep; 5:1434–41.
  • Byrtus M, Zeman V. On modeling and vibration of gear drives influenced by nonlinear couplings. Mechanism and Machine Theory. 2011 Mar; 46(3):375–97.
  • Muralidharan V, Sugumaran V, Sakthivel NR. Wavelet decomposition and Support Vector Machine for fault diagnosis of monoblock centrifugal pump. International Journal of Data Analysis Techniques and Strategies. 2011 Jan; 3(2):159–77.
  • Tse PW, Yang WX, Tam HY. Machine fault diagnosis through an effective exact wavelet analysis. Journal of Sound and Vibration. 2004 Nov; 277(4-5):1005–24.
  • Ngui WK, Leong MS, Hee LM, Abdelrehman AM. Wavelet analysis: Mother wavelet selection methods. Applied Mechanics and Materials. 2013 Sep; 393:953–8.
  • Yadav RB, Jha B Rao KRM, Yadav HL. Selection of optimal mother wavelet for fault detection using Discrete Wavelet Transform. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2013 Jun; 2(6):2338–43.
  • Saravanan N, Kumar Siddabattuni VNS, Ramachandran KI. Fault diagnosis of spur bevel gearbox using Artificial Neural Network (ANN) and Proximal Support Vector Machine (PSVM). Applied Soft Computing. 2010 Jan; 10(1):344–60.
  • Burges CJC. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery. 1998 Jun; 2(2):121–67.
  • Yesilyurt I. Gearbox fault detection and severity assessment using vibration analysis. [Ph.D Thesis]. UK: University of Manchester; 1997.
  • Sharma A, Amarnath M, Kankar PK. Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control. 2016 Jan; 22(1):176–92.
  • Kumar H, Ranjit Kumar TA, Amarnath M, Sugumaran V. Fault diagnosis of bearings through vibration signal using Bayes classifiers. International Journal of Computer Aided Engineering and Technology. 2014 Jan; 6(1):14–28.
  • Vijayarani S, Muthulakshmi S. Comparative analysis of Bayes and lazy classification algorithms. International Journal of Advanced Research in Computer and Communication Engineering. 2013 Aug; 2(8):3118–24.
  • Amarnath M, Jain D, Sugumaran V, Kumar H. Fault diagnosis of helical gearbox using Naïve Bayes and Bayes net. International Journal of Decision Support Systems. 2015 Jan; 1(1):4–17.
  • Arora R, Suman. Comparative Analysis of classification algorithms on different datasets using WEKA. International Journal of Computer Applications. 2012 Sep; 54(13):21–5.
  • Katore LS, Umale JS. Comparative study of recommendation algorithms and systems using WEKA. International Journal of Computer Applications. 2015 Jan; 110(3):14–7.
  • Serasiya SD, Chaudhary N. Simulation of various classifications results using WEKA. International Journal of Recent Technology and Engineering. 2012 Aug; 1(3):155–62.
  • Patil TR, Sherekar SS. Performance analysis of naive Bayes and J48 classification algorithm for data classification. International Journal of Computer Science and Applications. 2013 Apr; 6(2):256–61.
  • Sharma G, Bhargava N, Bhargava R, Mathuria M. Decision tree analysis on J48 algorithm for data mining. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Jun; 3(6):1114–9.
  • Zvokelj M, Zupan S, Prebil I. Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis. Mechanical Systems and Signal Processing. 2010 May; 24(4):1049–67.
  • Mahmood DY, Hussein MA. Intrusion detection system based on K-Star classifier and feature set reduction. IOSR Journal of Computer Engineering. 2013 Nov-Dec; 15(5):107–12.
  • Tripathi G, Naganna S. Feature selection and classification approach for sentiment analysis. Machine Learning and Applications: An International Journal. 2015 Jun; 2(2):1–16.
  • Kannan A, Sugumaran V, Amarnath M, Soman KP. Fault diagnosis of helical gearbox using variational mode decomposition with naïve Bayes and Bayes net classifiers through vibration signals. International Journal of Manufacturing Systems and Design. 2014 Oct; 1(1):1–16..
  • Anilkumar PH, Augusta Sophy Beulet P. Lifting-based Discrete Wavelet Transform for real-time signal detection. Indian Journal of Science and Technology. 2015 Oct; 8(25):1–6.
  • Satishkumar R, Sugumaran V. Vibration based health assessment of bearings using random forest classifier. Indian Journal of Science and Technology. 2016 Mar; 9(10):1–6.
  • Muralidharan A, Sugumaran V, Soman KP, Amarnath M. Fault diagnosis of helical gearbox using variational mode decomposition and random forest algorithm. SDHM: Structural Durability and Health Monitoring. 2015 Jan; 10(1):55–80.
  • Anuradha C, Velmurugan T. A comparative analysis on the evaluation of classification algorithms in the prediction of student’s performance. Indian Journal of Science and Technology. 2015 Jul; 8(15):1–12.
  • Venkata Lakshmi S, Edwin Prabakaran T. Performance analysis of multiple classifiers on KDD Cup dataset using WEKA Tool. Indian Journal of Science and Technology. 2015 Aug; 8(17):1–10.
  • Verma A, Kaur I, Arora N. Comparative analysis of information extraction techniques for data minining. Indian Journal of Science and Technology. 2016 Mar; 9(11):1–18.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.