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Fault Diagnosis of Helical Gearbox through Vibration Signals using J48 Decision Tree and Wavelet
Objectives: Gear plays an efficient role in power transmission. Minor faults in gears can lead to severe faults. The vibration analysis can be used for determining the causes of the faults which are raised while ongoing operation. This study determines the usage of machine learning algorithm for condition monitoring of helical gearbox. Methods/Statistical Analysis: The vibration signals were taken by using accelerometers from helical gearbox in which artificial faults were incorporated before testing. By using Discrete Wavelet Transform (DWT) feature extraction was done. The feature selection and feature classification was done by using J48 algorithm and subsequent results were observed. Findings: The classification accuracy of helical gearbox using Discrete Wavelet Transform was observed to be 89.28% which itself shows its efficiency. In feature extraction maximum accuracy of 89.06% was obtained by sym 8 wavelet. During feature selection and classification many modifications in algorithm were made i.e. minimum number of object, confidence factor etc. Suitable readings of the modifications were applied and feature classification was done. Improvements: Different Discrete Wavelet Transforms were compared taken from vibration signal proved Sym 8 Discrete Wavelet Transform is the best one to be used in this scenario. The methodology yielded a satisfactory classification accuracy of 89.28%, which is higher than what was obtained by similar experiments with different methodology till date. The results and their analysis are discussed in the study. The performance of this methodology may be further improved by using different classifiers and different wavelets.
Condition Monitoring, Discrete Wavelet Transform, J48 Algorithm, Vibration Signals.
- Panwar VS, Mogal SP. A case study on various defects found in a gear system. International Research Journal of Engineering and Technology. 2015 Jun; 2(3):425–9.
- Raja RI. Gearbox diagnosis and prognosis using acoustic emission. Vancouver: School of Engineering, Cranfield University; 2005 Oct.
- Machine learning method with compensation distance technique for gear fault detection. 2011. Available from: http://ieeexplore.ieee.org/document/5970591/
- Amarnath M, Jain D, Sugumaran V, Kumar. H. Fault diagnosis of helical gearbox using naive Bayes and Bayes net. International Journal of Research in Mechanical Engineering. 2013 Sep; 1(1):22–33.
- Sugumaran V, Jain D, Amarnath M, Kumar H. Fault diagnosis of helical gearbox using Decision Tree through vibration signals. International Journal of Performability Engineering. 2013 Mar; 9(2):221–34.
- Vernekar K, Kumar H, Gangadharan KV. Gear fault detection using vibration analysis and continuous wavelet transform. Proceedia Material Science. 2014 Sep; 5:1846–52.
- Vernekar K, Kumar H, Gangadharan KV. Fault diagnosis of gears through discrete wavelet features based on a Decision Tree and Support Vector Machines. International Journal of Condition Monitoring. 2015 Aug; 5(2):23–9.
- Sarvanan N, Siddabattum VNSK. A comparative study on classification of features by SVM and PSVM extracted using Morelet wavelet for fault diagnosis of spur bevel gearbox. Expert Systems with Applications. 2008 Oct; 35(3):1351–66.
- Fan Q, Ikejo K, Nagamua K, Kawada M, HashimoM. Gear damage diagnosis and classification based on Support Vector Machines. Journal of Advanced Mechanical Design, Systems and Manufacturing. 2014 Jan; 8(3):1–13.
- Aharamuthu K, Ayyasamy E. Gear fault diagnosis using vibrational signals based on Decision Tree assisted intelligent controllers. Journal of Vibro engineering. 2013 Dec; 15(4):1826.
- Chen F, Tang B, Chen R. A novel fault diagnosis model for gearbox based on a wavelet Support Vector Machine with immune Genetic Algorithm. Measurement. 2013 Jan; 46(1):220–32.
- Omar FK, Gouda AM. Dynamic wavelet based tool for gearbox diagnosis. Mechanical Systems and Signal Processing. 2012 Jan; 26:190–204.
- Saravanan N, Ramachandran KI. Fault diagnosis of spur bevel gearbox using Discrete Wavelet Features and Decision Tree classification. Expert Systems with Applications. 2009 Jul; 36(5):9564–73.
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