Holes drilling quality consistency analysis based on the fusion of monitoring signals marginal spectrum characteristics and bispectrum characteristics

ZHOU Youhang XIE Saiyuan XIE Qi ZHOU Houming

Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (24) : 40-45.

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PDF(1633 KB)
Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (24) : 40-45.

Holes drilling quality consistency analysis based on the fusion of monitoring signals marginal spectrum characteristics and bispectrum characteristics

  • ZHOU Youhang  XIE Saiyuan  XIE Qi  ZHOU Houming
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Abstract

The information, which is mined from the drilling process monitoring signals data, could be helpful to inspect the holes drilling quality. This study presents a holes drilling consistency inspection method based on the fusion of monitoring signals marginal spectrum characteristics and bispectrum characteristics. At first, the three acceleration vibration sensor and acoustic emission sensor are used to monitor holes drilling process, and the Hilbert Huang transform and a high order spectrum estimation are used to analysis every hole drilling monitoring signals, so every hole’s the marginal spectrum and double spectrum features are extracted from the monitoring signals. Finally, the principal component analysis method is used to realize features dimension reduction, fusion features and features clustering, the computer conclusion show the change condition of these features directly and clear. Based on the coupling relationship between the drilling process quality fluctuation and the numerical changes of these features, and compared with the artificial quality test results of drilling hole, the result show that the data clustering research of the fusion of hole drilling monitoring signals marginal spectrum characteristics and bispectrum characteristics can realize the holes drilling quality consistency detection effectively, analysis and identify the abnormal drilling quality rapidly.
 

Key words

holes drilling / quality consistency inspection / marginal spectrum / bispectrum / PCA

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ZHOU Youhang XIE Saiyuan XIE Qi ZHOU Houming. Holes drilling quality consistency analysis based on the fusion of monitoring signals marginal spectrum characteristics and bispectrum characteristics[J]. Journal of Vibration and Shock, 2015, 34(24): 40-45

References

[1] DEVDAS S,TOM E,CLAUDIO C. New approach to the inspection of cooling holes in aero-engines[J]. Optics and Lasers in Engineering,2009,(47):686-694
[2] 崔亚新.  基于双神经网络的微孔钻削在线监测研究[D]. 长春:吉林大学,2012.
Cui Yaxin.Research on Micro-drilling Online Monitoring Based on Double Neural Network[D].ChangChun: Jilin University,2012.
[3]  I.S. Shyha,S.L. Soo,D.K. Aspinwall,S. Bradley,R. Perry,P. Harden,S. Dawson.  Hole quality assessment following drilling of metallic-composite stacks[J]. International Journal of Machine Tools and Manufacture . 2011, (7) .
[4] Christophe Ramirez, Gerard Poulachon, Frederic Rossi, Rachid M'Saoubi. Tool Wear Monitoring and Hole Surrfaceand  Quality DuringCFRPDrilling[J].Procedia CIRP,2014,(13): 163-168
[5] S. H. Lee, D. Lee.  In-process monitoring of drilling burr formation using acoustic emission and a wavelet-based artificial neural network[J]. International Journal of Production Research . 2008, (17).
[6] COSTES J P. A predictive surface profile model for turning based on spectral analysis[J]. Journal of Materials Processing Technology, 2013, 213(1):94-100.
[7] Rawat S, Attia H. Characterization of the Dry High Speed Drilling Process of Woven Composites Using Machinability Maps Approach[J]. CIRPAnnals, 2009, 58(1):105–108.
[8] Susana Ferreiro, Basilio Sierra, Itziar Irigoien, et al. Data mining for quality control: Burr detection in the drilling  process[J]. Computers & Industrial Engineering,2011,60(4): 801-810.
[9] 刘贵杰,徐萌,王欣等. 基于HHT的管道阀门内漏声发射检测研究[J]. 振动与冲击,2012,23:62-66.
Liu Guijie, Xu Meng,Wang Xin,etal. AE detection for pipeline valve leaklage based on HHT[J]. Journal of Vibration and Shock, 2012,23:62-66.
[10] Gerardo Beruvides, Ramón Quiza, Raúl del Toro, Rodolfo E. Haber.Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten–copper composite material[J]. Sensors and Actuators,2013, 199(1):165-175.
[11] 孟宗,李姗姗. 基于小波改进阈值去噪和HHT的滚动轴承故障诊断[J]. 振动与冲击,2013,14:204-208+214.
Meng Zong,Li Shanshan. Rolling bearing fault diagnosis based on improved wavelet threshold de-noising method and HHT[J]. Journal of Vibration and Shock,2013,14: 204-208+214.
[12] 钟佑明,秦树人,汤宝平.希尔伯特黄变换中边际谱的研究[J]. 系统工程与电子技术,2004,09:1323-1326.
ZHONG Youming, QIN Shuren, TANG Baopin. Study on the marginal spectrum in Hilbert-Huang transform[J]. Journal of Systems Engineering and Electronics ,2004,09:1323-1326.
[13] Shen Guoji, Stephen McLaughlin, Xu Yongcheng, etal. Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears[J]. Mechanical Systems and Signal Processing, 2014,43(1-2):76-89.
[14] Alexander Bertrand, Marc Moonen. Distributed adaptive estimation of covariance matrix eigenvectors in wireless sensor networks with application to distributed PCA[J]. Signal Processing, 2014, 104:120-135.
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