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

Zhou, You Hang; Guo, Hui; Dong, Yin Song; He, Qi

doi:10.4028/www.scientific.net/KEM.480-481.877

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Home Key Engineering Materials Materials Engineering for Advanced TechnologiesCluster Analysis for Drilling-Quality Based on the...

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

Abstract:

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.

Info:

Periodical:

Key Engineering Materials (Volumes 480-481)

Main Theme:

Materials Engineering for Advanced Technologies

Edited by:

Yanwen Wu

Pages:

877-882

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.480-481.877

Citation:

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

Authors:

You Hang Zhou, Hui Guo, Yin Song Dong, Qi He

Keywords:

Analysis Hierarchy Process (AHP), Batch Drilling, Eigenvector, Incremental Clustering, Process Quality

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$38.00

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References

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Paper TitlePages

Research on Improved K-Means Clustering Algorithm

Authors: Yin Sheng Zhang, Hui Lin Shan, Jia Qiang Li, Jie Zhou

Abstract: The traditional K-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster center. Hierarchical clustering algorithm can be used to generate the initial cluster center of K-means clustering algorithm. The geometric features of input data can achieve a good distribution by means of pretreatment and feature extraction and selection. In the learning of fuzzy neural network, Java language is used to write source code of the algorithm. The experimental results show that new algorithm has improved the clustering quality effectively.

1977

A New K-Means Clustering Based on Genetic Algorithm

Authors: Chang Sheng Cheng, Yan Meng Shang

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Improved K-Means Clustering Method Based on Complex Network for Rolling Bearing Fault Diagnosis

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Abstract: According to the selection difficulties of initial clustering center of k-means clustering algorithm, this paper proposes a method that is to use complex network degree to improve k-means clustering algorithm for fault pattern recognition method, and to improve the accuracy of clustering. Use network to represent fault data structure, with joint connecting matrix to express similarity between nodes, according to the complex concepts of networks degree, calculate the size of every node degree, and select the maximum degree of node as k-means clustering initial center. This method is applied to the rolling bearing clustering diagnosis example, achieving good fault diagnosis effect. This study provides a new method for the selection of initial cluster centers of K-means clustering.

250

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Authors: Qian Qian Xu, Hai Yan Zhang, He Ping Hou, Zhuo Fei Xu

Abstract: The printing machine is a sort of large-scale equipment characterized by high speed and precision. A fault diagnosis method based on kernel principal component analysis (KPCA) and K-means clustering is developed to classify the types of feeding fault. The multidimensional and nonlinear data of printed image could be reduced by KPCA to make up the deficiency of the traditional K-means clustering method. In this paper, it is experimentally verified that the classification accuracy of the combined method is higher than the traditional clustering analysis method in feeding fault detection and diagnosis. This method provides a shortcut for the determination of fault sources and realizes multi-faults diagnosis of printing machinery efficiently

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