Loose particles left inside sealed electronic devices are one of the main factors affecting the reliability of the entire military and aerospace system. The main purpose of loose particle detection is to discriminate between the particle signals and inherent mechanical noise (component signals). To address this problem, a multichannel system for loose particle detection is presented in this paper. A synchronization-based method is proposed, which makes good use of multichannel information. The synchronization method takes variance of correlation coefficients of multichannel synchronized pulses as a detection feature and then employs a transductive confidence predictor to produce confidence measures. The experimental results show the effectiveness of the proposed method.
DeLCAdamAWotizR. (2006) Decomposition of surface EMG signals. Journal of Neurophysiology96: 1646–1657.
2.
DanielBCarlottaDJamesPR (2006) Detecting outliers using transduction and statistical testing. In: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, 20–23 August, pp. 55–64.
3.
GammermanAVovkV (2007) Hedging predictions in machine learning. The Computer Journal50: 151–163.
4.
GaoHLZhangHWangSJ (2007) Research on auto-detection for remainder particles of aerospace relay based on wavelet analysis. Chinese Journal of Aeronautics20: 75–80.
5.
HalabiRDiabMOMoslemBKkalilM (2012) Cross-correlation analysis of multichannel uterine EMG Signals. In: The 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 28 August–1 September, pp. 3106–3109.
6.
HassanMTerrienJKarlssonB. (2009) Spatial analysis of uterine EMG signals: evidence of increased in synchronization with term. In: The 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, 3–6 September, pp. 6296–6299.
7.
KusRKaminskiMBlinowskaKJ (2004) Determination of EEG activity propagation: pair-wise versus multichannel estimate. IEEE Transactions on Biomedical Engineering51: 1501–1510.
8.
MaGCYiGXWenQY (2003) Particle impact noise detection in sealed relays based on neural network. In: The 49th IEEE Holm Conference on Electrical Contacts, Piscataway, NJ, 8–10 September, pp. 214–218.
9.
PapadopoulosHGarmmermanAVovkV (2009) Reliable diagnosis of acute abdominal pain with conformal prediction. Engineering Intelligent Systems17: 127–137.
10.
PeredaEQuirogaRQBhattacharyaJ (2005) Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology77: 1–37.
11.
ScaglioneLJ (1994) Neural network application to particle impact noise detection, In: IEEE International Conference on Neural Networks, Orlando, FL, 27 June–2 July, pp. 3415–3419.
12.
ShaferGVovkV (2008) A tutorial on conformal prediction. Journal of Machine Learning Research9: 371–421.
13.
TheisFJGarciaGA (2006) On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms. Signal Processing86: 603–623.
14.
VanderlooySSprinkhuizen-KuyperIG (2007) An overview of algorithmic randomness and its application to reliable instance classification. Technical Report MICC-IKAT 07–02, Universiteit Maastricht, The Netherlands.
15.
WangHZLinCDYangF. (2009) Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine. Computers in Biology and Medicine39: 425–432.
16.
WangNWangRLZhuSW. (2010) Hypoxia EEG analysis based on phase synchronization theory. In: The 9th IEEE International Conference on Cognitive Informatics, Beijing, China, 7–9 July, pp. 397–400.
17.
WangSJGaoHLZhaiGF (2007) Research on feature extraction of remnant particles of aerospace relays. Chinese Journal of Aeronautics20: 253–259.
18.
WangSJChenRZhangL (2011) Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network. Transaction of the Institute of Measurement and Control34: 947–955.
19.
WuYGWangSCWangSJ (2010) Research on wavelet threshold de-noising method of remainder detection for stand-alone electronic equipments in satellite. In: The First International Conference on Pervasive Computing, Signal Processing and Applications, Harbin, China, 17–19 September, pp. 1013–1017.
20.
ZhangHWangSJZhaiGF (2004) Dynamic model of particle impact noise detection. In: The 30th Annual Conference of the IEEE Industrial Electronics Society, Busan, Korea, 2–6 November, pp. 2577–2581.
21.
ZhangHWangSJZhaiGF (2006) Research on vibration test conditions in particle impact noise detection. In: The 4th International Symposium on Instrumentation Science and Technology, Harbin, China, 8–12 August, pp. 46–53.
22.
ZhangHWangSJZhaiGF (2008a) Research on particle impact noise detection standards. IEEE Transactions on Aerospace and Electronic Systems44(2): 808–814.
23.
ZhangJLiGHuM. (2008b) Recognition of hypoxia EEG with a preset confidence level based on EEG analysis. In: International Joint Conference on Neural Networks, Hong Kong, China, 1–8 June, pp. 3005–3008.
24.
ZhangLLiKWangSJ. (2013) Loose particle classification using a new wavelet Fisher discriminant method. In:The 10th International Symposium on Neural Networks, Dalian, China, 4–6 July, Part 1, pp, 582–593.
