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Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection

Author

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  • Hsu-Hao Yang

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan)

  • Mei-Ling Huang

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan)

  • Shih-Wei Yang

    (Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung City 41170, Taiwan)

Abstract

This paper presents a novel methodology to detect a set of more suitable attributes that may potentially contribute to emerging faults of a wind turbine. The set of attributes were selected from one-year historical data for analysis. The methodology uses the k -means clustering method to process outlier data and verifies the clustering results by comparing quartiles of boxplots, and applies the auto-associative neural networks to implement the residual approach that transforms the data to be approximately normally distributed. Hotelling T 2 multivariate quality control charts are constructed for monitoring the turbine’s performance and relative contribution of each attribute is calculated for the data points out of upper limits to determine the set of potential attributes. A case using the historical data and the alarm log is given and illustrates that our methodology has the advantage of detecting a set of susceptible attributes at the same time compared with only one independent attribute is monitored.

Suggested Citation

  • Hsu-Hao Yang & Mei-Ling Huang & Shih-Wei Yang, 2015. "Integrating Auto-Associative Neural Networks with Hotelling T 2 Control Charts for Wind Turbine Fault Detection," Energies, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:10:p:12100-12115:d:57654
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    References listed on IDEAS

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    Cited by:

    1. Xin Wu & Hong Wang & Guoqian Jiang & Ping Xie & Xiaoli Li, 2019. "Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data," Energies, MDPI, vol. 12(6), pages 1-19, March.
    2. Li, Yanting & Liu, Shujun & Shu, Lianjie, 2019. "Wind turbine fault diagnosis based on Gaussian process classifiers applied to operational data," Renewable Energy, Elsevier, vol. 134(C), pages 357-366.
    3. Yuehjen E. Shao & Shih-Chieh Lin, 2019. "Using a Time Delay Neural Network Approach to Diagnose the Out-of-Control Signals for a Multivariate Normal Process with Variance Shifts," Mathematics, MDPI, vol. 7(10), pages 1-14, October.
    4. Yang, Hsu-Hao & Huang, Mei-Ling & Lai, Chun-Mei & Jin, Jhih-Rong, 2018. "An approach combining data mining and control charts-based model for fault detection in wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 808-816.
    5. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    6. Yaping Li & Haiyan Li & Zhen Chen & Ying Zhu, 2022. "An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations," Energies, MDPI, vol. 15(5), pages 1-13, February.
    7. Cambron, P. & Lepvrier, R. & Masson, C. & Tahan, A. & Pelletier, F., 2016. "Power curve monitoring using weighted moving average control charts," Renewable Energy, Elsevier, vol. 94(C), pages 126-135.
    8. Wang, Anqi & Pei, Yan & Zhu, Yunyi & Qian, Zheng, 2023. "Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern," Renewable Energy, Elsevier, vol. 211(C), pages 918-937.

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