An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension

DOI

10.2991/icacie-17.2017.33How to use a DOI?

Keywords

fractal dimension; morphology; EEMD; condition monitoring; diesel engine

Abstract

In respect to the nonlinear and low signal-to-noise ratio characteristics of the vibration signals measured from diesel engine, This paper conducts an investigation on diesel engine condition monitoring based on ensemble empirical mode decomposition(EEMD) and morphological fractal dimension. Firstly, the vibration signal is decomposed into a set of intrinsic mode functions(IMFs) by EEMD, and get the fault information of the characteristic IMF. Then the morphological fractal dimension of IMFs which contain diesel engine fault characteristic information is computed and as it for the characteristic parameters to identifying the diesel engine working states and fault types. The analysis of vibration signals measured from diesel engine at different states that are normal and exhaust valve leakage have been done. Results show that it can reflect nonlinear characteristics of vibration signals measured from diesel engine and monitor working condition of diesel engine accurately.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Fengli Wang
AU  - Sihong Li
AU  - Yuchao Song
PY  - 2017/08
DA  - 2017/08
TI  - An Investigation on Engine Condition Monitoring Based on EEMD and Morphological Fractal Dimension
BT  - Proceedings of the 2017 2nd International Conference on Automatic Control and Information Engineering (ICACIE 2017)
PB  - Atlantis Press
SP  - 141
EP  - 144
SN  - 2352-5401
UR  - https://doi.org/10.2991/icacie-17.2017.33
DO  - 10.2991/icacie-17.2017.33
ID  - Wang2017/08
ER  -