A Comparison of Feature Selection and Feature Extraction Techniques for Condition Monitoring of a Hydraulic Actuator

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Stephen Adams Ryan Meekins Peter A. Beling Kevin Farinholt Nathan Brown Sherwood Polter Qing Dong

Abstract

In many applications, there are a number of data sources that can be collected and numerous features that can be calculated from these data sources. The error of big data has lead many to believe that the larger the data, the better the results. However, as the dimensionality of the data increases, the effects of the curse of dimensionality become more prevalent. Further, a large feature set also increases the computational cost
of data collection and feature calculation. In this study, we evaluated four dimensionality reduction techniques as part of a system for condition monitoring of a hydraulic actuator. Two feature selection techniques, ReliefF and variable importance, and two feature extraction techniques, principal component analysis and autoencoders, are used to reduce the input into three classification algorithms. We conclude that variable importance in conjunction with the random forest algorithm outperforms the other dimensionality reduction techniques. Feature selection has the added advantage of being able to remove data sources and features from the data collection and feature calculation process that are not present in
the relevant feature subset.

How to Cite

Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2017). A Comparison of Feature Selection and Feature Extraction Techniques for Condition Monitoring of a Hydraulic Actuator. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2452
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Keywords

condition monitoring, feature extraction, feature selection

References
Adams, S., Beling, P. A., & Cogill, R. (2016). Feature selection for hidden Markov models and hidden semi-Markov models. IEEE Access, 4, 1642–1657.
Adams, S., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2016). Condition based monitoring for a hydraulic actuator. In Proceedings of the annual conference of the prognostics and health management society 2016.
Almuallim, H., & Dietterich, T. G. (1991). Learning with many irrelevant features. In Aaai (Vol. 91, pp. 547– 552).
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213–223.
Bishop, C. (2006). Pattern recognition and machine learning. Springer-Verlag New York.
Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial intelligence, 97(1), 245–271.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.
Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent data analysis, 1(1-4), 131–156.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. 2nd. Edition. New York.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182. Harmouche, J., Delpha, C., & Diallo, D. (2014). Incipient fault detection and diagnosis based on kullback–leibler divergence using principal component analysis: Part i. Signal Processing, 94, 278–287.
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504–507.
Hu, Y., Palmé, T., & Fink, O. (2016). Deep health indicator extraction: A method based on auto-encoders and extreme learning machines. In Annual conference of the prognostics and health management society 2016.
Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4–37.
Section
Technical Papers