Data-driven fault diagnosis for wind turbines using modified multiscale fluctuation dispersion entropy and cosine pairwise-constrained supervised manifold mapping

Abstract

Condition monitoring and fault diagnosis of wind turbines is an attractive yet challenging task. This paper presents a novel data-driven fault diagnosis scheme for wind turbines, using refined time-shift multiscale fluctuation-based dispersion entropy (RTSMFDE) and cosine pairwise-constrained supervised manifold mapping (CPCSMM). The developed RTSMFDE can improve multiscale dispersion entropy in complexity measurement of time series, by employing the refined time-shift analysis approach. Moreover, the proposed CPCSMM combines cosine distance metric, supervised theory and sparse global manifold structure, to obtain the most important features, resulting in a low dimensional representation. In this fault diagnosis scheme, a high-dimensional fault feature set is first constructed using RTSMFDE. Then, sensitive features are extracted using CPCSMM. Finally, the low-dimensional feature set is fed to a beetle antennae search-based support vector machine for fault identification. Simulation experiments and wind turbine fault diagnosis experiments were used, to show that the proposed RTSMFDE can comprehensively mine the wind turbine features and outperforms the existing state-of-the-art entropy methods. The visualization results and classification effects of the proposed CPCSMM approach are better than existing unsupervised and supervised dimensionality reduction methods. The proposed data-driven fault diagnosis method can effectively and accurately identify multi-faults and outperforms existing fault diagnosis techniques.