Diagnostics of Machinery Faults based on EMD and ICA

DOI

10.2991/icmcm-16.2016.121How to use a DOI?

Keywords

Empirical mode decomposition; Independent component analysis; Fault diagnosis; Rotating machinery.

Abstract

Diagnostics of the rotating machinery can identify potential failure at its early stage and reduce severe machine damage and costly machine downtime. Rub-impact is common faults in rotating machinery and results in impact and friction between rotor and stator. The vibration signals due to impact and friction are always non-stationary which includes the rub-impact signal, the background signal and the noise signal. Empirical mode decomposition (EMD) is based on the local characteristic time scale of signal and could decompose the complicated signal into a number of intrinsic mode functions (IMFs). Due to the weak rub-impact signal submerged in the background and noise signals, EMD procedure would generate the components redundancy. In order to solve the problem, a novel method combining with independent component analysis (ICA) and EMD is developed. ICA was introduced into EMD, so that the IMFs are orthogonal to each other and the components redundancy can be removed. Experimental analysis results show that the proposed method is more suitable and have a better performance for the incipient fault detection. The proposed method is thus proved to have potential to become a powerful tool for the surveillance and diagnosis of rotating machinery.

Copyright

© 2016, 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  - Yannian Cai
AU  - Sihong Li
AU  - Hui Xing
PY  - 2016/12
DA  - 2016/12
TI  - Diagnostics of Machinery Faults based on EMD and ICA
BT  - Proceedings of the 2016 7th International Conference on Mechatronics, Control and Materials (ICMCM 2016)
PB  - Atlantis Press
SP  - 647
EP  - 652
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmcm-16.2016.121
DO  - 10.2991/icmcm-16.2016.121
ID  - Wang2016/12
ER  -