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Sensors 2017, 17(11), 2477; doi:10.3390/s17112477

Fault Detection of Bearing Systems through EEMD and Optimization Algorithm

Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pildong-ro 1 gil, Jung-gu, Seoul 100-715, Korea
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Received: 31 August 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 28 October 2017
(This article belongs to the Section Physical Sensors)
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Abstract

This study proposes a fault detection and diagnosis method for bearing systems using ensemble empirical mode decomposition (EEMD) based feature extraction, in conjunction with particle swarm optimization (PSO), principal component analysis (PCA), and Isomap. First, a mathematical model is assumed to generate vibration signals from damaged bearing components, such as the inner-race, outer-race, and rolling elements. The process of decomposing vibration signals into intrinsic mode functions (IMFs) and extracting statistical features is introduced to develop a damage-sensitive parameter vector. Finally, PCA and Isomap algorithm are used to classify and visualize this parameter vector, to separate damage characteristics from healthy bearing components. Moreover, the PSO-based optimization algorithm improves the classification performance by selecting proper weightings for the parameter vector, to maximize the visualization effect of separating and grouping of parameter vectors in three-dimensional space. View Full-Text
Keywords: EEMD; Isomap; PSO; fault detection; feature extraction EEMD; Isomap; PSO; fault detection; feature extraction
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Lee, D.-H.; Ahn, J.-H.; Koh, B.-H. Fault Detection of Bearing Systems through EEMD and Optimization Algorithm. Sensors 2017, 17, 2477.

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