Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm
This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed int...
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| Published in | Sadhana (Bangalore) Vol. 42; no. 7; pp. 1143 - 1153 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New Delhi
Springer India
01.07.2017
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0256-2499 0973-7677 |
| DOI | 10.1007/s12046-017-0678-9 |
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| Abstract | This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis. |
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| AbstractList | This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis. |
| Author | Kumar, Hemantha Gangadharan, K V Vernekar, Kiran |
| Author_xml | – sequence: 1 givenname: Kiran surname: Vernekar fullname: Vernekar, Kiran organization: Department of Mechanical Engineering, National Institute of Technology Karnataka – sequence: 2 givenname: Hemantha surname: Kumar fullname: Kumar, Hemantha email: hemanta76@gmail.com organization: Department of Mechanical Engineering, National Institute of Technology Karnataka – sequence: 3 givenname: K V surname: Gangadharan fullname: Gangadharan, K V organization: Department of Mechanical Engineering, National Institute of Technology Karnataka |
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| Cites_doi | 10.1016/j.asoc.2012.03.021 10.1016/j.mspro.2014.07.514 10.1016/j.apacoust.2013.07.001 10.1016/j.ymssp.2006.05.004 10.1006/jsvi.2000.2864 10.1016/j.matdes.2006.07.018 10.1016/j.ymssp.2006.06.010 10.1016/j.eswa.2010.09.042 10.1016/j.ymssp.2006.09.007 10.1016/j.mspro.2014.07.492 10.1504/IJCAET.2014.058002 10.1016/j.jsv.2005.11.002 10.1016/j.measurement.2014.01.018 10.1016/j.measurement.2012.06.009 |
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| Keywords | Engine fault diagnosis Naïve Bayes empirical mode decomposition decision tree technique |
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| References | Hemantha Kumar, Ranjit Kumar, Amarnath, Sugumaran (CR10) 2012; 14 Saimurugan, Ramachandran, Sugumaran, Sakthivel (CR11) 2011; 38 Sugumaran, Muralidharan, Ramachandran (CR17) 2007; 21 Sugumaran, Ramachandran (CR12) 2007; 21 Lin, Qu (CR15) 2000; 234 CR16 Vernekar, Kumar, Gangadharan (CR1) 2014; 5 Addin, Sapuan, Othman, Ali (CR8) 2011; 6 Yu, Junsheng (CR3) 2006; 294 Muralidharan, Sugumaran (CR6) 2012; 12 Wang, Ma, Zhu, Liu, Zhao (CR14) 2014; 75 Kumar, Ranjit Kumar, Amarnath, Sugumaran (CR4) 2014; 6 Addin, Sapuan, Mahdi, Othman (CR5) 2007; 28 Chen, Tang, Chen (CR2) 2013; 46 Vedant, Sugumaran, Amarnath, Kumar (CR7) 2013; 1 Sun, Chen, Li (CR13) 2007; 21 Sharma, Sugumaran, Babu Devasenapati (CR9) 2014; 50 Girish Kumar, Hemantha, Gangadhar, Kumar, Krishna (CR18) 2014; 5 V Sugumaran (678_CR12) 2007; 21 678_CR16 H Kumar (678_CR4) 2014; 6 K Vernekar (678_CR1) 2014; 5 V Muralidharan (678_CR6) 2012; 12 Vedant (678_CR7) 2013; 1 F Chen (678_CR2) 2013; 46 M Girish Kumar (678_CR18) 2014; 5 J Lin (678_CR15) 2000; 234 TA Hemantha Kumar (678_CR10) 2012; 14 O Addin (678_CR8) 2011; 6 W Sun (678_CR13) 2007; 21 A Sharma (678_CR9) 2014; 50 O Addin (678_CR5) 2007; 28 M Saimurugan (678_CR11) 2011; 38 YS Wang (678_CR14) 2014; 75 V Sugumaran (678_CR17) 2007; 21 Y Yu (678_CR3) 2006; 294 |
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| SubjectTerms | Algorithms Bayesian analysis Computer simulation Decomposition Engineering Fault diagnosis Feature extraction Vibration |
| Title | Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm |
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