Periodic Signal Recognition Technology Based on Framing Window Adaptive Scaling Algorithm and Trajectory Tracking Algorithm: A Case Study of Aerospace Loose Particle Detection Signal

In this paper, a novel algorithm combining adaptive scaling of framing windows and pulse trajectory tracking is proposed for the detection of internal loose particles in aerospace sealed electronic components. The proposed algorithm can be used to identify whether the detection signal has general or...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 23; no. 14; p. 1
Main Authors: Zhai, Guofu, Li, Pengfei, Wang, Guotao, Sun, Zhigang, Han, Xiao, Wang, Qiang
Format: Journal Article
Language:English
Published: New York IEEE 15.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive algorithms
adaptive scaling algorithm
Aerospace electronics
Algorithms
Electronic components
Fourier transforms
instability
loose particle detection
Particle impact
Recognition
Scaling
Sensors
signal identification
Time series analysis
Tracking
Trajectory tracking
trajectory tracking algorithm
Vibrations
Visualization
ISSN:1530-437X, 1558-1748
Online Access:Get full text
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Summary:In this paper, a novel algorithm combining adaptive scaling of framing windows and pulse trajectory tracking is proposed for the detection of internal loose particles in aerospace sealed electronic components. The proposed algorithm can be used to identify whether the detection signal has general or local periodicity, and to distinguish particle signals from component signals. The algorithm utilizes adaptive scaling of the framing windows length, which can effectively reduce the influence of the signal periodic instability caused by the change of the signal frequency. In order to evaluate the performance of the algorithm, 600 sets of data were collected on the Particle Impact Noise Detection platform. The single-component signal, loose particle signal, multi-component signal and mixed signal were verified respectively with the accurate rate close to 95%, and the recognition effect was great. In addition, compared with results using Fourier transform, the identification results of signal type using the proposed algorithm are more intuitive.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3280993