Imbalanced Classification for Fault Detection in Monitored Critical Infrastructures
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Published
Jul 14, 2017
Yan-Fu Li
Abstract
Safety and reliability are among the most crucial factors for the critical infrastructures (CIs). For this reason, they are typically closely monitored and large amounts of data have been collected. Due to their importance, CIs are designed to be highly reliable such that fault cases are rare in the Bigdata set. This renders the fault detection an imbalanced binary classification task. In this work, we developed accurate data mining classifier to tackle this problem. The imbalance ratio of the data can be more than 200.
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Keywords
PHM
References
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Misra, J., & Saha, I. (2010). Artificial neural networks in hardware: A survey of two decades of progress. Neurocomputing, 74(1), 239-255.
Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168-4181.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(3), 1157-1182.
Hall, M. A. (1999). Correlation-based feature subset selection for machine learning. PhD Dissertation, University of Waikato.
Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.
Misra, J., & Saha, I. (2010). Artificial neural networks in hardware: A survey of two decades of progress. Neurocomputing, 74(1), 239-255.
Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168-4181.
Section
Regular Session Papers