Testing Topological Data Analysis for Condition Monitoring of Wind Turbines
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0000-0003-2945-8931
Alexander Stasik Zhenyou Zhang
Signe Riemer-Sørensen
Abstract
We present an investigation of how topological data analysis (TDA) can be applied to condition-based monitoring (CBM) of wind turbines for energy generation.
TDA is a branch of data analysis focusing on extracting mean- ingful information from complex datasets by analyzing their structure in state space and computing their underlying topo- logical features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anoma- lies, and trends in the data that may not be apparent through traditional signal processing methods.
For this study, wind turbine data was acquired from a wind park in Norway via standard vibration sensors at different lo- cations of the turbine’s gearbox. Both the vibration acceler- ation data and its frequency spectra were recorded at infre- quent intervals for a few seconds at high frequency and fail- ure events were labelled as either gear-tooth or ball-bearing failures. The data processing and analysis are based on a pipeline where the time series data is first split into intervals and then transformed into multi-dimensional point clouds via a time-delay embedding. The shape of the point cloud is an- alyzed with topological methods such as persistent homol- ogy to generate topology-based key health indicators based on Betti numbers, information entropy and signal persistence. Such indicators are tested for CBM and diagnosis (fault de- tection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an in- teresting alternative for failure identification and diagnosis of operational failures in wind turbines.
How to Cite
##plugins.themes.bootstrap3.article.details##
Topological data analysis, vibration analysis, predictive maintenance
M. K., & Kundu, P. (2024). An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis. Measurement, 226, 114191. Chazal, F., & Michel, B. (2021). An introduction to topological data analysis: Fundamental and practical aspects for data scientists. Front. Artif. Intell., 4, 667963. Fraser, A., & Swinney, H. (1986). Independent coordinates for strange attractors from mutual information. Phys. Rev. A, 33(2), 1134–1140.
Hatcher, A. (2002). Algebraic topology. Cambridge University Press.
Jiang, Z., Zhang, K., Xiang, L., Yu, G., & Xu, Y. (2023). A time-frequency spectral amplitude modulation method and its applications in rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 185, 109832.
Khasawneh, F. A., & Munch, E. (2016). Chatter detection in turning using persistent homology. Mechanical Systems and Signal Processing, 70-71, 527-541. Khasawneh, F. A., Munch, E., & Perea, J. A. (2018). Chatter classification in turning using machine learning and topological data analysis. IFAC-PapersOnLine, 51(14), 195-200.
Perea, J. A. (2016). Persistent homology of toroidal sliding window embeddings. In 2016 IEEE international conference on acoustics, speech and signal processing (icassp) (pp. 6435–6439).
Perea, J. A., & Harer, J. (2015). Sliding windows and persistence: as application of topological methods to signal analysis. Found. of Comput. Mathematics, 15, 799. P´erez, J. B., Hauke, S., Lupo, U., Caorsi, M., & Dassatti,
A. (2021). giotto-ph: A Python library for high-performance computation of persistent homology of Vietoris-Rips filtrations. Sawalhi, N., & Randall, R. B. (2004). The application of spectral kurtosis to bearing diagnostics. Proceedings of Acoustics, 040115.
Smith, A. D., Dłotko, P., & Zavala, V. M. (2021). Topological data analysis: Concepts, computation, and applications in chemical engineering. Comput. Chem. Eng., 146, 107202. Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renew. Energy, 133, 620-639. Takens, F. (1981). Detecting strange attractors in turbulence. In D. A. Rand & L.-S. Young (Eds.), Dynamical systems and turbulence, lecture notes in mathematics (Vol. 898, p. 366-381). Springer-Verlag. Tchakoua, P., Wamkeue, R., Ouhrouche, M., SlaouiHasnaoui, F., Tameghe, T. A., & Ekemb, G. (2014). Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 7, 2595-2630. Wang, Q., Dong, Z., Li, R., & Wang, L. (2022). Renewable energy and economic growth: New insight from country risks. Energy, 238, 122018. Wang, W.-K., Wan, M., Zhang, W.-H., & Yang, Y. (2022). Chatter detection methods in the machining processes: A review. Journal of Manufacturing Processes, 77, 240-259. Wasserman, L. (2018). Fault analysis and condition monitoring of the wind turbine gearbox. Annual Review of Statistics and its Application, 5(3), 501-532. Xiao, F., Tian, C., Wait, I., Yang, Z., Still, B., & Chen, G. S. (2020). Fault analysis and condition monitoring of the wind turbine gearbox. Advances in Mechanical Engineering, 12(3). Yesilli, M. C., Khasawneh, F. A., & Otto, A. (2022a). Chatter detection in turning using machine learning and similarity measures of time series via dynamic time warping. Journal of Manufacturing Processes, 77, 190-206. Yesilli, M. C., Khasawneh, F. A., & Otto, A. (2022b). Topological feature vectors for chatter detection in turning processes. Int J Adv Manuf Technol, 119, 5687–5713. Zhang, H., Chen, X., Du, Z., & Yan, R. (2016). Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis. Mechanical Systems and Signal Processing, 80, 349-376. Zhang, Z., Verma, A., & Kusiak, A. (2012). Fault analysis and condition monitoring of the wind turbine gearbox. IEEE Transactions on Energy Conversion, 27(2), 526535.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.