Algorithm Used to Detect Weak Signals Covered by Noise in PIND
Open Access
- 14 December 2019
- journal article
- research article
- Published by Hindawi Limited in International Journal of Aerospace Engineering
- Vol. 2019, 1-10
- https://doi.org/10.1155/2019/1637953
Abstract
Detection of the loose particles is urgently required in the spacecraft production processes. PIND (particle impact noise detection) is the most commonly used method for the detection of loose particles in the aerospace electronic components. However, when the mass of loose particles is smaller than 0.01 mg, the weak signals are difficult to be detected accurately. In this paper, the aperiodic stochastic resonance (ASR) is firstly used to detect weak signals of loose particles. The loose particle signal is simulated by the oscillation attenuation signal. The influences of structure parameters on the potential height and detection performance of ASR are studied by a numerical iteration method. The cross-correlation coefficient between input and output is chosen as a criterion for whether there is an existing a particle or not. Through normalization, the loose particle signal-labeled high frequency of 135 kHz is converted into the low-frequency band, which can be detected by the ASR method. According to the algorithm, weak signals covered by noise could be detected. The experimental results show that the detection accuracy is 66.7%. This algorithm improves the detection range of weak loose particle signals effectively.Keywords
Funding Information
- Heilongjiang University (51077022, 2012TD007, 61271347, HDRCCX-201604, 51607059, QL201505, LBH-Z16169, QC2017059)
This publication has 24 references indexed in Scilit:
- Relative Pose and Inertia Determination of Unknown Satellite Using Monocular VisionInternational Journal of Aerospace Engineering, 2018
- A review of stochastic resonance in rotating machine fault detectionMechanical Systems and Signal Processing, 2018
- Multi-frequency weak signal detection based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonanceMechanical Systems and Signal Processing, 2016
- Stochastic Resonance in a Multistable System Driven by Gaussian NoiseDiscrete Dynamics in Nature and Society, 2016
- E-nose based rapid prediction of early mouldy grain using probabilistic neural networksBioengineered, 2015
- Material identification of loose particles in sealed electronic devices using PCA and SVMNeurocomputing, 2015
- Multi-stable stochastic resonance and its application research on mechanical fault diagnosisJournal of Sound and Vibration, 2013
- Research on Feature Extraction of Remnant Particles of Aerospace RelaysChinese Journal of Aeronautics, 2007
- Use of stochastic resonance for enhancement of low-level vibration signal componentsMechanical Systems and Signal Processing, 2005
- Stochastic resonanceReviews of Modern Physics, 1998