Compound fault prediction of rolling bearing using multimedia data

Compound fault prediction of rolling bearing using multimedia data Catastrophic failure of mechanical systems due to faults occurring on rolling bearing is still a great challenge. These faults, which are of multiple type, are compounded in nature. Vibration analysis of multimedia signals is one of the most effective techniques for the health monitoring of these bearings. A compound fault signal usually consists of multiple characteristic signals and strong confusion noise, which makes it a tough task to separate weak fault signals from them. To resolve the compound fault diagnosis problem of rolling bearings byseparation of multimedia signals’ (obtained from acoustic or acceleration sensors), ensemble empirical mode decomposition (EEMD) method along with some classifier (like independent component analysis (ICA) technique) has been used to some degree of success. But, they are not found capable of detecting difficult faults existing on small balls of the bearing. In order to solve this problem, we are going to propose a new method based on use of Combined Mode Functions (CMF) for selecting the intrinsic mode functions(IMFs) instead of the maximum cross correlation coefficient based EEMD technique, sandwiched with, Convolution Neural Networks (CNN), which are deep neural nets, used as fault classifiers. This composite CNN-CMF-EEMD methodovercomes the deficiencies of other approaches, such as the inability to learn the complex non-linear relationships in fault diagnosis issues and fine compound faults like those occurring on small balls of the bearing. The difficult compound faults can be separated effectively by executing CNN-CMF-EEMD method, which makes the fault features more easily extracted and more clearly identified. Experimental results reinforce the effectiveness of using CNN-CMF--EEMD technique for fine compound faults. A comparison of CNN-CMF-EEMD with Artificial Neural Networks (ANN) based ANN-CMF-EEMD shows the capability of CNN as a powerful classifier in the domain of compound fault features of rolling bearing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Compound fault prediction of rolling bearing using multimedia data

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4419-1
Publisher site
See Article on Publisher Site

Abstract

Catastrophic failure of mechanical systems due to faults occurring on rolling bearing is still a great challenge. These faults, which are of multiple type, are compounded in nature. Vibration analysis of multimedia signals is one of the most effective techniques for the health monitoring of these bearings. A compound fault signal usually consists of multiple characteristic signals and strong confusion noise, which makes it a tough task to separate weak fault signals from them. To resolve the compound fault diagnosis problem of rolling bearings byseparation of multimedia signals’ (obtained from acoustic or acceleration sensors), ensemble empirical mode decomposition (EEMD) method along with some classifier (like independent component analysis (ICA) technique) has been used to some degree of success. But, they are not found capable of detecting difficult faults existing on small balls of the bearing. In order to solve this problem, we are going to propose a new method based on use of Combined Mode Functions (CMF) for selecting the intrinsic mode functions(IMFs) instead of the maximum cross correlation coefficient based EEMD technique, sandwiched with, Convolution Neural Networks (CNN), which are deep neural nets, used as fault classifiers. This composite CNN-CMF-EEMD methodovercomes the deficiencies of other approaches, such as the inability to learn the complex non-linear relationships in fault diagnosis issues and fine compound faults like those occurring on small balls of the bearing. The difficult compound faults can be separated effectively by executing CNN-CMF-EEMD method, which makes the fault features more easily extracted and more clearly identified. Experimental results reinforce the effectiveness of using CNN-CMF--EEMD technique for fine compound faults. A comparison of CNN-CMF-EEMD with Artificial Neural Networks (ANN) based ANN-CMF-EEMD shows the capability of CNN as a powerful classifier in the domain of compound fault features of rolling bearing.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Feb 6, 2017

References

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