Improved K-means algorithm for manufacturing process anomaly detection and recognition

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 自动化系统研究室

	Improved K-means algorithm for manufacturing process anomaly detection and recognition
	Zhou XM(周小敏); Peng W(彭威); Shi HB(史海波)
	2006
会议名称	1st International Symposium on Digital Manufacture
会议日期	October 15-17, 2006
会议地点	Wuhan, China
关键词	data mining clustering quality management anomaly detection and recognition
页码	1036-1041
中文摘要	Anomaly detection and recognition are of prime importance in process industries. Faults are usually rare, and, therefore, predicting them is difficult . In this paper, a new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Based on the clustering result of historical disqualification product data in manufacturing process which generated by the Improved-K-means algorithm, a prediction model which is used to detect and recognize the abnormal trend of the quality problems is constructed. This simple and robust alarm-system architecture for predicting incoming faults realizes the transition of quality problems from diagnosis afterward to prevention beforehand indeed. In the end, the alarm model was applied for prediction and avoidance of gear-wheel assembly faults at a gear-plant.
收录类别	EI ; CPCI（ISTP）
产权排序	1
会议主办者	Int Inst Prod Engn Res, Chinese Mech Engn Soc, Natl Nat Sci Fdn China, Hong Kong Polytechn Univ, Wuhan Univ Technol, Hubei Digital Mfg Key Lab, Natl Soc Intelligent Mfg
会议录	Journal of Wuhan University of Technology, SUPPL. 1
会议录出版者	WUHAN UNIV TECHNOLOGY PRESS
会议录出版地	WUHAN
语种	英语
ISSN号	1671-4431
WOS记录号	WOS:000244372300208
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/7693]
专题	沈阳自动化研究所_自动化系统研究室
推荐引用方式 GB/T 7714	Zhou XM,Peng W,Shi HB. Improved K-means algorithm for manufacturing process anomaly detection and recognition[C]. 见:1st International Symposium on Digital Manufacture. Wuhan, China. October 15-17, 2006.