Abstract
In industrial settings, continuous monitoring of the operation of assets generates a vast amount of data originating from a multitude of very diverse sources. This data allows to study and understand asset performance in real operating conditions, paving the way for failure prediction, machine setting optimisation and many other industrial applications. However, it is not always feasible and neither wise to approach data analytics for such applications by merging all the available data into a single data set, which often leads to information loss. The literature lacks methods to inspect asset performance based on splitting the data in different views corresponding to different types of monitored parameters. The multi-view data analysis method proposed in this work allows to extract operating modes for an industrial asset and subsequently, profile their performance. In this two-step approach, the endogeneous (internal working) data view is first exploited to detect and characterise distinct operating modes, while an exogeneous (operating context) data representation (disjoint with the endogeneous view) of these operating modes is subsequently used to derive prototypical performance profiles via non-negative matrix factorisation. The application potential and validity of the proposed method is illustrated based on real-world data from a wind turbine.
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Notes
- 1.
Engie is a multinational French energy company. Url: www.engie.com.
- 2.
The SCADA data set can be found here: www.opendata-renewables.engie.com/explore.
- 3.
More info on Python can be found here: https://www.python.org/.
References
Mackey, R., Iverson, D., Pisanich, G., Toberman, M., Hicks, K.: Integrated system health management (ISHM) technology demonstration project final report. In: IEEE Sensors Applications Symposium (2006)
Dhont, M., Tsiporkova, E., Boeva, V.: Layered integration approach for multi-view analysis of temporal data. In: International Workshop on Advanced Analytics and Learning on Temporal Data, pp. 138–154. Springer (2020)
Févotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the \(\beta \)-divergence. Neural Comput. 23(9), 2421–2456 (2011)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Xu, L., Dong, J., Chen, Z., Dai, X.: Non-negative matrix factorization for diabetes ii metabolic profiling analysis. In: 2007 1st International Conference on Bioinformatics and Biomedical Engineering, pp. 641–643. IEEE (2007)
Qin, M., Lei, K., Bai, B., Zhang, G.: Towards a profiling view for unsupervised traffic classification by exploring the statistic features and link patterns. In: Proceedings of the 2019 Workshop on Network Meets AI & ML, pp. 50–56 (2019)
Mandache, D., á la Guillaume, E.B., Olivo-Marin, J.-C., Meas-Yedid, V.: Blind source separation in dynamic cell imaging using non-negative matrix factorization applied to breast cancer biopsies. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), vol. 2021, pp. 1605–1608. IEEE (2021)
Liu, L., Zhang, Z., Huang, Z.: Flexible discrete multi-view hashing with collective latent feature learning. Neural Process. Lett. 52(3), 1765–1791 (2020)
Fu, L., Lin, P., Vasilakos, A.V., Wang, S.: An overview of recent multi-view clustering. Neurocomputing 402, 148–161 (2020)
Yang, Y., Wang, H.: Multi-view clustering: a survey. Big Data Min. Anal. 1(2), 83–107 (2018)
Liu, X., et al.: Late fusion incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(10), 2410–2423 (2019)
Shao, W.: Online multi-view clustering with incomplete views. In: IEEE International Conference on Big Data (Big Data), vol. 2016, pp. 1012–1017 (2016)
Ye, Y., et al.: Incomplete multiview clustering via late fusion. Comput. Intell. Neurosci. 1–11 (2018)
Jiang, B., et al.: Evolutionary multi-objective optimization for multi-view clustering. In: 2016 IEEE CEC 2016, pp. 3308–3315 (2016)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM International Conference on Data Mining. SIAM (2013), pp. 252–260
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 129–136 (2009)
Chen, M.-S., Huang, L., Wang, C.-D., Huang, D.: Multi-view clustering in latent embedding space. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(04), pp. 3513–3520 (2020)
Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. Adv. Neural Inf. Process. Syst. 24, 1413–1421 (2011)
Wang, X., Guo, X., Lei, Z., Zhang, C., Li, S.Z.: Exclusivity-consistency regularized multi-view subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 923–931 (2017)
Bruno, E., Marchand-Maillet, S.: Multiview clustering: a late fusion approach using latent models. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 736–737 (2009)
Xu, J., Ren, Y., Li, G., Pan, L., Zhu, C., Xu, Z.: Deep embedded multi-view clustering with collaborative training. Inf. Sci. 573, 279–290 (2021)
Greene, D., Cunningham, P.: A matrix factorization approach for integrating multiple data views. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 423–438. Springer (2009)
Kalayeh, M.M., Idrees, H., Shah, M.: Nmf-knn: image annotation using weighted multi-view non-negative matrix factorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 184–191 (2014)
Ou, W., Long, F., Tan, Y., Yu, S., Wang, P.: Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning. Multimedia Tools Appl. 77(10), 12 955–12 978 (2018)
Wang, J., Tian, F., Yu, H., Liu, C.H., Zhan, K., Wang, X.: Diverse non-negative matrix factorization for multi-view data representation. IEEE Trans. Cybern. 48(9), 2620–2632 (2017)
Gong, Y., Li, Z., Zhang, J., Liu, W., Yin, Y., Zheng, Y.: Missing value imputation for multi-view urban statistical data via spatial correlation learning. IEEE Trans. Knowl. Data Eng. (2021)
Zhang, X., Zong, L., Liu, X., Yu, H.: Constrained NMF-based multi-view clustering on unmapped data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29(1) (2015)
Iverson, D.L.: Inductive system health monitoring. NASA (2004)
Murgia, A., Tsiporkova, E., Verbeke, M., Tourwé, T.: Context-aware performance benchmarking of a fleet of industrial assets. Archives of Data Science, Series A, vol. 5 (2020)
Dhillon, I.S., Sra, S.: Generalized nonnegative matrix approximations with Bregman divergences. In: NIPS, vol. 18. Citeseer (2005)
Bellman, R., Corporation, R., Collection, K.M.R.: Dynamic Programming, ser. Rand Corporation research study. Princeton University Press (1957). [Online]. https://books.google.be/books?id=wdtoPwAACAAJ
MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Statistics and Probability (1967)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 2, 224–227 (1979)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (1987)
Krzanowski, W.J., Lai, Y.: A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics 23–34 (1988)
Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)
Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15), 3201–3212 (2005)
Sheather, S.J.: Density estimation. Stat. Sci. 588–597 (2004)
Dhont, M., Tsiporkova, E., Boeva, V.: Advanced discretisation and visualisation methods for performance profiling of wind turbines. Energies (2021), submitted
Bhatia, R.: Matrix Analysis, vol. 169. Springer (2013)
O’Grady, K.E.: Measures of explained variance: cautions and limitations. Psychol. Bull. 92(3), 766 (1982)
Gumilar, L., Afandi, A.N., Sias, Q.A., Nugroho, W.S., Sholeh, M., Gunawan, A.: Comparative study: pitch angle variation for making power curve and search maximum power of horizontal axis wind turbine. In: AIP Conference Proceedings, vol. 2228, no. 1, p. 030005. AIP Publishing LLC (2020)
Funding
This research was subsidised through the projects MISTic and ReWind by the Brussels-Capital Region—Innoviris and received funding from the Flemish Government (AI Research Program).
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Dhont, M., Tsiporkova, E., Boeva, V. (2022). Performance Profiling of Operating Modes via Multi-view Analysis Using Non-negative Matrix Factorisation. In: Pedrycz, W., Chen, SM. (eds) Recent Advancements in Multi-View Data Analytics. Studies in Big Data, vol 106. Springer, Cham. https://doi.org/10.1007/978-3-030-95239-6_11
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DOI: https://doi.org/10.1007/978-3-030-95239-6_11
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