Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm
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Date
2017
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Publisher
Springer India sanjiv.goswami@springer.co.in
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. © 2017, Indian Academy of Sciences.
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Keywords
Computer aided diagnosis, Decision trees, Engines, Failure analysis, Fault detection, Gears, Signal processing, Sodium, Trees (mathematics), Vibrations (mechanical), Bayes algorithms, Classification accuracy, Decision tree techniques, Empirical Mode Decomposition, Empirical mode decomposition method, Engine gearboxes, Important features, Intrinsic Mode functions, Data mining
Citation
Sadhana - Academy Proceedings in Engineering Sciences, 2017, 42, 7, pp. 1143-1153