2017
DOI: 10.1007/s12046-017-0678-9
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Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm

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 fa… Show more

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Cited by 24 publications
(15 citation statements)
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“…The work in [2,8] updated this type of study and included SD, P2P, SK, and other methods. Shallow learning methods such as Support Vector Machines (SVM) [25], Naive Bayes (NB) [26], and logistic regression [27] are also common methods. The time domain method has made some progress in early research as a simple basic method [28].…”
Section: Related Workmentioning
confidence: 99%
“…The work in [2,8] updated this type of study and included SD, P2P, SK, and other methods. Shallow learning methods such as Support Vector Machines (SVM) [25], Naive Bayes (NB) [26], and logistic regression [27] are also common methods. The time domain method has made some progress in early research as a simple basic method [28].…”
Section: Related Workmentioning
confidence: 99%
“…To solve this problem, Huang et al [9] proposed the EMD, which can efficiently decompose nonlinear and nonstationary signals without any set of basis functions. Vernekar et al [16] presented an engine gearbox fault diagnosis on the basis of EMD and naive Bayes algorithm to manage vibration signals. ey classified healthy and different simulated faulty conditions of gear and bearing, and results showed that the classification accuracy of their method can reach 98.88%.…”
Section: Introductionmentioning
confidence: 99%
“…Frequently, the signal is decomposed into experimental modes (Empirical Mode Decomposition -EMD), which correspond to the characteristic vibration frequencies and which can be treated as new multidimensional signals subjected to further analysis. In the paper [4], the authors, by means of decomposition into experimental modes, identified acceleration and machine learning signals, and the degree of damage to the gear tooth and various types of damage in the bearings. In the paper [5], the authors, using the extended EMD method (Ensemble Empirical Mode Decomposition -EEMD) in combination with machine learning methods, compared different fault detection algorithms in toothed wheels and obtained the best damage classification for the nearest neighbour method.…”
Section: Introductionmentioning
confidence: 99%