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Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis

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  • Wang, Zhenya
  • Yao, Ligang
  • Cai, Yongwu
  • Zhang, Jun

Abstract

Intelligent fault diagnosis of wind turbine rolling bearings is an important task to improve the reliability of wind turbines and reduce maintenance costs. In this paper, a novel intelligent fault diagnosis method is proposed for wind turbine rolling bearings based on Mahalanobis Semi-supervised Mapping (MSSM) manifold learning algorithm and Beetle Antennae Search based Support Vector Machine (BAS-SVM), mainly including three stages (i.e., feature extraction, dimensionality reduction, and pattern recognition). In the first stage, Multiscale Permutation Entropy (MPE) is utilized to extract the feature information from rolling bearing vibration signals at multiple scales, while a high-dimensional feature set is constructed. Second, the proposed MSSM algorithm, combining the advantages of Mahalanobis distance, semi-supervised learning and manifold learning, is applied to reduce the dimension of high-dimensional MPE feature set. Subsequently, low-dimensional features are input to the BAS-SVM classifier for pattern recognition using the BAS algorithm to search the best parameters. The performance of the proposed fault diagnosis method was confirmed by conducting a fault diagnosis experiment of wind turbine rolling bearings. The application results show that the proposed method can effectively and accurately identify different states of wind turbine rolling bearings with a recognition accuracy of 100%.

Suggested Citation

  • Wang, Zhenya & Yao, Ligang & Cai, Yongwu & Zhang, Jun, 2020. "Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis," Renewable Energy, Elsevier, vol. 155(C), pages 1312-1327.
    • Handle: RePEc:eee:renene:v:155:y:2020:i:c:p:1312-1327
    DOI: 10.1016/j.renene.2020.04.041
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    Cited by:

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    3. Kong, Yun & Qin, Zhaoye & Wang, Tianyang & Han, Qinkai & Chu, Fulei, 2021. "An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines," Renewable Energy, Elsevier, vol. 173(C), pages 987-1004.
    4. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    5. Dan Ling & Chaosong Li & Yan Wang & Pengye Zhang, 2022. "Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost," Energies, MDPI, vol. 15(17), pages 1-19, August.
    6. Ma, Chenyang & Wang, Xianzhi & Li, Yongbo & Cai, Zhiqiang, 2024. "Broad zero-shot diagnosis for rotating machinery with untrained compound faults," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Kong, Yun & Han, Qinkai & Chu, Fulei & Qin, Yechen & Dong, Mingming, 2023. "Spectral ensemble sparse representation classification approach for super-robust health diagnostics of wind turbine planetary gearbox," Renewable Energy, Elsevier, vol. 219(P1).

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