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Electronics 2018, 7(2), 16; https://doi.org/10.3390/electronics7020016

Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

1
Automobile/Ship Electronics Convergence Center, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Korea
2
College of Engineering, Youngsan University, 99 Pilbong-gil, Haeundae-gu, Busan 612-713, Korea
*
Author to whom correspondence should be addressed.
Received: 20 December 2017 / Revised: 23 January 2018 / Accepted: 24 January 2018 / Published: 31 January 2018
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Abstract

In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An effective solution to these problems in the decomposition process can help to determine significant IMFs and to improve the performance of the fault diagnosis system. This paper describes a novel power-based IMF selection algorithm and evaluates the performance of the proposed fault diagnosis system using improved complete ensemble EMD with adaptive noise and a multi-layer perceptron neural network. View Full-Text
Keywords: fault diagnosis; improved complete ensemble empirical mode decomposition; intrinsic mode function; neural network fault diagnosis; improved complete ensemble empirical mode decomposition; intrinsic mode function; neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Han, H.; Cho, S.; Kwon, S.; Cho, S.-B. Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm. Electronics 2018, 7, 16.

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