Total views : 237
Fault Diagnostics of a Gearbox with Acoustic Signals Using Wavelets and Decision Tree
Objectives: This study aims at devising a methodology for accurately predicting the different fault conditions of gears in a gearbox using acoustic signals. Statistical Analysis: The acoustic signals are captured for several artificially created fault conditions of different magnitude and the wavelet features are extricated from captured acoustic signals. Subsequently,prominent features are selected by utilizing J48 Decision tree which discerns the most dominant traits among the allocated data obtained from wavelet transform of the acoustic signals followed by Random Forest for the classification of features. Findings: Out of a total of eleven features extracted, six were selected through Decision Tree and Random forest was used for feature classification of acoustic signals using wavelet features. Several iterations were conducted on the wavelet features by varying different parameters and the maximum percentage accuracy was found to be 99.76%. The instances of misclassification of features were minimal in Random Forest and it proved to be an efficient and precise classifier. Hence, Random Forest proved to be an easy to use, fast and accurate classifier that could classify various kinds of wavelet features efficiently. Applications: The methodology can be used to provide accurate real time results about the condition of gear teeth.
Acoustic Signals, Decision Tree, Fault Diagnostics, Gear Box, Wavelets
- Amarnath M, Krishna IRP. Local Fault Detection in Helical Gears via Vibration and Acoustic Signals using EMD based Statistical Parameter Analysis, Measurement. 2014; 58:154–64.
- Sakthivel NR, Indira V, Nair BB, Sugumaran V. Use of Histogram Features for Decision Tree Based Fault Diagnosis of Mono Block Centrifugal Pump, International Journal of Granular Computing, Rough Sets and Intelligent Systems (IJGCRSIS). 2011; 2(1):23–36.
- Wu JD, Liu CH, An Expert System for Fault Diagnosis in Internal Combustion Engines Using Wavelet Packet Transform and Neural Network, Expert Systems with Applications. 2009; 36(3-1):4278–86.
- Yesilurt I, Fault Detection and Location in Gears by the Smoothed Instantaneous Power Spectrum Distribution, NDT&E International. 2003; 36(7):535–42.
- Aharamuthu K, Ayyasamy EP. Application of Discrete Wavelet Transform and Zhao-Atlas-Marks Transforms in Non-Stationary Gear Fault Diagnosis, Journal of Mechanical Science and Technology. 2013; 27(3):641−47.
- Vrhel MJ, Lee C, Unser M, Rapid Computation of the Continuous Wavelet Transform by Oblique Projections, IEEE Transactions on Signal Processing. 1997; 45(4):891−900.
- Szabo L, Dobai JB, Biro KA, Discrete Wavelet Transform based Rotor Faults Detection Method for Induction Machines, Department of Electrical Machines, Marketing and Management, Technical University of Cluj, 2008, p 1−12.
- Quinlin JR. Induction of Decision Trees, Machine Learn ing, Kluwer Academic Publishers, Boston - Manufactured in The Netherlands. 1986; 97(1-2):321−27.
- Garg R, Mittal S. Optimization by Genetic Algorithm, International Journal of Advanced Research in Computer Science and Software Engineering. 2014;4(4):111−23.
- Banerjee A. Impact of Principal Component Analysis in the Application of Image Processing, International Journal of Advanced Research in Computer Science and Software Engineering. 2012; 61(3):611−22.
- Saravanan N, Siddabattuni VNSK, Ramachandran KI. A Comparative Study on Classification of Features by SVM and PSVM Extracted using Morlet Wavelet for Fault Diagnosis of Spur Bevel Gear Box, Expert Systems with Applications. 2008; 35(1):1351–66.
- Durgesh K, Srivastava S, Bhambhu L. Data Classification Using Support Vector Machine, Journal of Theoretical and Applied Information Technology. 2009; 12(1):1−3.
- Sakthivel NR, Sugumaran V, Nair BB. Automatic Rule Learning using Roughset for Fuzzy Classifier in Fault Categorization of Centrifugal Pump, International Journal of Applied Soft Computing. 2012; 12(1):196–203.
- Pyke SW, Sheridan PM, Logistic Regression Analysis of Graduate Student Retention, The Canadian Journal of Higher Education. 1993; 23(2):21−76.
- Chharia A, Gupta RK. Enhancing Naïve Bayes Performance with Modified Absolute Discount Smoothing Method in Spam Classification, International Journal of Advanced Research in Computer Science and Software Engineering. 2013; 3(3):1−6.
- Biau G. Analysis of a Random Forests Model, Journal of Machine Learning Research. 2012; 13(1):1063−95.
- 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; 37(6):4168–81.
- Ghate VN, Dudul SV. SVM based Fault Classification of Three Phase Induction Motor, Indian Journal of Science and Technology. 2009 May; 2(4):32−35.
- 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.
- Breiman L, Random Forests. Stastistics Department, University of California, 2001.
- Jain R, Abraham A. A Comparative Study of Fuzzy Classification Methods on Breast Cancer Data, Australasian Physics and Engineering Sciences in Medicine. 2004 Dec; 27(4):213−18.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.