Analysis of Blade Tip Timing Data From Nonuniformly-Spaced Sensors by Discrete Wavelet Transform

Abstract A major drawback of the Blade Tip Timing method is its inherent undersampling of the vibration data. The consequence of this characteristics is that the spectrograms computed using these data are very difficult to read as the spectral traces produces by the signal components are replicated multiple times due to aliasing. This paper describes a technique that exploits the non-uniformity of the sensor spacing to suppress aliasing. The analysis starts with the time-frequency representation of the signal obtained through a discrete wavelet transform. Then, the dominant response component is identified by searching from the highest ridge of the spectrogram. Finally, the corresponding signal component is removed from the spectrogram together with their replicas. The procedure is repeated iteratively until all the relevant signal components are isolated and identified.

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Published
2025-06-16
DOI
https://doi.org/10.1115/gt2025-154016
Primary Topic
Structural Health Monitoring Techniques
Type
article
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article

Analysis of Blade Tip Timing Data From Nonuniformly-Spaced Sensors by Discrete Wavelet Transform

Roberto Guida, Luigi Carassale, Michela Marrè-Brunenghi
Structural Health Monitoring Techniques
article

Analysis of Blade Tip Timing Data From Nonuniformly-Spaced Sensors by Discrete Wavelet Transform

Roberto Guida, Luigi Carassale, Michela Marrè-Brunenghi
article en

Abstract

Abstract A major drawback of the Blade Tip Timing method is its inherent undersampling of the vibration data. The consequence of this characteristics is that the spectrograms computed using these data are very difficult to read as the spectral traces produces by the signal components are replicated multiple times due to aliasing. This paper describes a technique that exploits the non-uniformity of the sensor spacing to suppress aliasing. The analysis starts with the time-frequency representation of the signal obtained through a discrete wavelet transform. Then, the dominant response component is identified by searching from the highest ridge of the spectrogram. Finally, the corresponding signal component is removed from the spectrogram together with their replicas. The procedure is repeated iteratively until all the relevant signal components are isolated and identified.

Ansaldo (Italy) (IT), University of Genoa (IT)
Openalex Percentile: Top 78%
Structural Health Monitoring Techniques
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Analysis of Blade Tip Timing Data From Nonuniformly-Spaced Sensors by Discrete Wavelet Transform — Roberto Guida, Luigi Carassale, et al. · (2025) | TGRS Research Map | TGRS