Abstract
Anomaly detection for helicopter rotor provides fault early warning and failure detection to avoid catastrophic accidents and major downtime. It is difficult to extract effective fault features from non-stationary and non-linear vibration data of rotor. A novel time-frequency feature is presented based on short-time Fourier transform in the paper. Due to lack of abundant fault data in practice, support vector data description is also exploited to detect damages by building a model only with normal data. We experimentally evaluate the performance of the proposed anomaly detection on realistic vibration data of helicopter rotor. The results demonstrate that the time-frequency features are closely related to the states of rotor, and the anomaly detection algorithm can clearly detect damages.
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Acknowledgments
This paper is supported by National Natural Science Foundation of China (U1433116), Foundation of Graduate Innovation Center in NUAA (kfjj20151602).
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He, Y., Pi, D. (2016). Anomaly Detection Algorithm for Helicopter Rotor Based on STFT and SVDD. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_34
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DOI: https://doi.org/10.1007/978-3-319-48674-1_34
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