Abstract
This paper presents a new approach for fault diagnosis of industrial processes during transitions. The proposed diagnosis strategy is based on the combination of the nearest-neighbor classification rule and the multivariate Dynamic Time Warping time series similarity measure. The proposal is compared with four different classification methods: Bayes Classifier, Multi-Layer Perceptron Neural Network, Support Vector Machines and Long Short-Term Memory Network which have high performance in the specialized scientific bibliography. The continuous stirred tank heater benchmark is used under scenarios of faults occurring at different moments of a transition and scarce fault data. The proposed approach achieves a classification performance approximately 20% superior compared to the best results of the four instance-based classifiers.
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References
Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: AAAI Workshop on Knowledge Discovery in Databases, pp. 229–248 (1994)
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0347-9
Dougherty, G.: Pattern Recognition and Classification. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-5323-9
He, Y., Zhou, L., Ge, Z., Song, Z.: Distributed model projection based transition processes recognition and quality-related fault detection. Chemom. Intell. Lab. Syst. 159, 69–79 (2016)
Heijden, F.V.D., Duin, R., Ridder, D.D., Tax, D.: Classification, Parameter Estimation and State Estimation. Wiley, Hoboken (2004)
Hochreiter, S., Schmidhuber, J.: Long short term memory networks. Neural Comput. 9(8), 1735–1780 (1997)
Hsu, C., Lin, C.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Mueen, A., Keogh, E.: Extracting optimal performance from dynamic time warping. In: KDD, pp. 2129–2130 (2016)
Scholkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Song, B., Tan, S., Shi, H.: Key principal components with recursive local outlier factor for multimode chemical process monitoring. J. Process Control 47, 136–149 (2016)
Srinivasan, R., Qian, M.S.: Online fault diagnosis and state identification during process transitions using dynamic locus analysis. Chem. Eng. Sci. 61, 6109–6132 (2006)
Sundarraman, A., Srini, R.: Monitoring transitions in chemical plants using enhanced trend analysis. Comput. Chem. Eng. 27, 1455–1472 (2003)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Elsevier (2009)
Thornhill, N.F., Patwardhan, S.C., Shah, S.L.: A continuous stirred tank heater simulation model with applications. J. Process Control 18, 347–360 (2008)
Wang, L., Yang, C., Sun, Y.: Multi-mode process monitoring approach based on moving window hidden Markov model. Ind. Eng. Chem. Res. 57(1), 292–301 (2017)
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26, 275–309 (2013)
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Acevedo-Galán, D.L., Quiñones-Grueiro, M., Prieto-Moreno, A., Llanes-Santiago, O. (2018). A New Approach for Fault Diagnosis of Industrial Processes During Transitions. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_39
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DOI: https://doi.org/10.1007/978-3-030-01132-1_39
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