Cluster Analysis for Drilling-Quality Based on the Modified Algorithm of InDBSCAN


Article Preview

To detect the quality of batch drilling quickly,a new approach based on Acoustic Emission signals is presented. The signals’ statistical characteristics are extracted from acoustic emission signals in Time-domain, and then the signals’ eigenvectors are constructed to reflect each drilling process. A modified incremental clustering algorithm InDBSCAN is used to cluster these eigenvectors,and the batch drilling-quality can be analysed indirectly. Calculation and analysis results show that: the conclusion of incremental cluster analysis is more reasonable by the modified incremental clustering method of InDBSCAN. The detection accuracy of the batch drilling-quality is up to 84.3% according to the manual quality inspection.



Key Engineering Materials (Volumes 480-481)

Edited by:

Yanwen Wu




Y. H. Zhou et al., "Cluster Analysis for Drilling-Quality Based on the Modified Algorithm of InDBSCAN", Key Engineering Materials, Vols. 480-481, pp. 877-882, 2011

Online since:

June 2011




[1] SHAO Ren-ping, HUANG Xin-na, HU Jun-hui. Analysis of data mining of clustering and its application to mechanical transmission fault diagnosis [C]. Journal of Aerospace Power, 2008, 23(10): 1933-(1938).

[2] ZHOU You-hang, ZHANG Jian-xun. Analysis of Relationship between batch drilling process and Multi-Sensor Synchronization Signals [J]. 2009 International Conference on Measuring Technology and Mechatronics Automation, 2009, 2: 127-130.


[3] ZHOU You-hang, ZHANG Jian-xun, TANG Wen-zhuang. Mapping between phases and signals in drilling process based on transient features of signals [J]. Journal of Central South University (Science and Technology), 2010, 41(3): 971-976.

[4] Singh R, Khamba J S. Comparison of slurry effect on machining characteristics of titanium in ultrasonic drilling [J]. Journal of Materials Processing Technology, 2008, 197(2): 200-205.


[5] Jiawei Han, Micheline Kamber. Data Mining: Concepts and Techniques (Second Edition)[M]. FAN Ming, MENG Xiao-feng, translate. Beijing: China Machine Press, 2007: 251-301.

[6] Deng Dong-mei, LONG Ji-zhen, YIN Xiang-zhou. A co-clustering algorithm based on structured Web document [J]. Journal of Central South University (Science and Technology), 2010, 41(5): 1871-1876.

[7] Ester M, Kriegel H-P, Sander J, etc. Incremental Clustering for Mining in a Data Warehousing Environment[C]. In: Proceedings of 24th International conference on very large Data base, 1998: 323-333.

[8] WANG Xin-min, ZHAO Bin, ZHANG Qin-li. Mining method choice based on AHP and fuzzy mathematics [J]. Journal of Central South University (Science and Technology), 2008, 39(5): 875-880.

[9] YANG Yan, JIN Fan, et al. Survey of clustering validity evaluation [J]. Application Research of Computers, 2008, 25(6): 1630-1632.