Next Article in Journal
Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)
Previous Article in Journal
An Electronic System for the Contactless Reading of ECG Signals
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2477;

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
Author to whom correspondence should be addressed.
Received: 31 August 2017 / Revised: 20 October 2017 / Accepted: 23 October 2017 / Published: 28 October 2017
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [23060 KB, uploaded 30 October 2017]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Lee, D.-H.; Ahn, J.-H.; Koh, B.-H. Fault Detection of Bearing Systems through EEMD and Optimization Algorithm. Sensors 2017, 17, 2477.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top