Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems
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We present the results of our investigation into the use of Genetic Algorithms (GA) for identifying near optimal design parameters of Diagnostic Systems that are based on Artificial Neural Networks (ANNs) for condition monitoring of mechanical systems. ANNs have been widely used for health diagnosis of mechanical bearing using features extracted from vibration and acoustic emission signals. However, different sensors and the corresponding features exhibit varied response to different faults. Moreover, a number of different features can be used as inputs to a classifier ANN. Identification of the most useful features is important for an efficient classification as opposed to using all features from all channels, leading to very high computational cost and is, consequently, not desirable. Furthermore, determining the ANN structure is a fundamental design issue and can be critical for the classification performance. We show that GA can be used to select a smaller subset of features that together form a genetically fit family for successful fault identification and classification tasks. At the same time, an appropriate structure of the ANN, in terms of the number of nodes in the hidden layer, can be determined, resulting in improved performance.
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- Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems
- Book Title
- Applied Soft Computing Technologies: The Challenge of Complexity
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- pp 135-149
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- Advances in Soft Computing
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- Springer Berlin Heidelberg
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- Editor Affiliations
- 2. School of Computer Science and Engineering, Chung-Ang University
- 3. Department of Applied Mathematics Biometrics and Process Control, University Gent
- 4. Dept. Automation Technologies, Fraunhofer IPK Berlin
- 5. Dept. Automation Technologies, Fraunhofer IPK Berlin
- Author Affiliations
- 6. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- 7. Associate Professor, School of Electrical and Computer Engineering, Georgia Institute of Technology, Savannah, GA, 31407, USA
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