Abstract
The industrial environment requires constant attention for faults on processes. This concern has central importance both for workers safety and process efficiency. Modern Process Automation Systems are capable of produce a large amount of data; upon this data, machine learning algorithms can be trained to detect faults. However, this data high complexity and dimensionality causes a decrease in these algorithms quality metrics. In this work, we introduce a new feature extraction method to improve the quality metrics of data-based fault detection. Our method uses a Fast Fourier Transform (FFT) to extract a temporal signature from the input data, to reduce the feature dimensionality generated by signature extraction, we apply a sequence of Principal Component Analysis (PCA). Then, the feature extraction output feeds a classification algorithm. We achieve an overall improvement of 17.4% on F1 metric for the ANN classifier. Also, due to intrinsic FFT characteristics, we verified a meaningful reduction in development time for data-based fault detection solution.
This work is supported by the BNDES under the FUNTEC-SDCD project.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010)
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., Schröder, J.: Diagnosis and Fault-Tolerant Control, vol. 691. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-35653-0
Brigham, E.O., Brigham, E.: The Fast Fourier Transform and Its Applications, vol. 1. Prentice Hall, Englewood Cliffs (1988)
Comon, P.: Independent component analysis, a new concept? Signal Process. 36(3), 287–314 (1994)
Downs, J.J., Vogel, E.F.: A plant-wide industrial process control problem. Comput. Chem. Eng. 17(3), 245–255 (1993)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Frank, P.M., Blanke, M.: Fault diagnosis and fault-tolerant control. In: Control Systems, Robotics and Automation XVI (2007)
Jing, C., Hou, J.: SVM and PCA based fault classification approaches for complicated industrial process. Neurocomputing 167, 636–642 (2015)
Kruskal, J.B.: Nonmetric multidimensional scaling: a numerical method. Psychometrika 29(2), 115–129 (1964)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)
Nashalji, M.N., Arvand, S., Norouzifard, M.: Integration of principal component analysis and neural classifier for fault detection and diagnosis of Tennessee Eastman process. In: 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T), pp. 166–170. IEEE (2014)
Yin, S., Ding, S.X., Haghani, A., Hao, H., Zhang, P.: A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. J. Process Control 22(9), 1567–1581 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
de Souza, M.M., Netto, J.C., Galante, R. (2019). FFT-2PCA: A New Feature Extraction Method for Data-Based Fault Detection. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-27615-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27614-0
Online ISBN: 978-3-030-27615-7
eBook Packages: Computer ScienceComputer Science (R0)