Testing Topological Data Analysis for Condition Monitoring of Wind Turbines
<|reference_start|>Testing Topological Data Analysis for Condition Monitoring of Wind Turbines: 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 meaningful information from complex datasets by analyzing their structure in state space and computing their underlying topological features. By representing data in a high-dimensional state space, TDA enables the identification of patterns, anomalies, 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 locations of the turbine's gearbox. Both the vibration acceleration data and its frequency spectra were recorded at infrequent intervals for a few seconds at high frequency and failure 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 analyzed with topological methods such as persistent homology 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 detection) to identify faults in wind turbines and classify them accordingly. Topological indicators are shown to be an interesting alternative for failure identification and diagnosis of operational failures in wind turbines.<|reference_end|>
@article{casolo2024testing,
    title={Testing Topological Data Analysis for Condition Monitoring of Wind
  Turbines},
    author={Simone Casolo and Alexander Stasik and Zhenyou Zhang and Signe
  Riemer-Sorensen},
    journal={arXiv preprint arXiv:2406.16380},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16380},
    primaryClass={eess.SY cs.SY}
}