Machine Fault Detection Using Vibration Signals and Improved Fuzzy Clustering Algorithm

被引：0

作者：

Linh Hoai Tran ^{[1
]}

Thanh Duc Nguyen ^{[1
]}

机构：

[1] Hanoi Univ Sci & Technol, Sch Elect & Elect Engn, Hanoi, Vietnam

来源：

2022 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (ICCAIS) | 2022年

关键词：

fault detection; machine vibration; fuzzy clustering; fuzzy reasoning; DIAGNOSIS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper will present a new solution for machine fault detection based on the vibration signals. The solution will used in improved fuzzy Gustaffson - Kessel clustering method to generate the classification data centers characteristic for different states of the machines. The Gustaffson - Kessel method offers a modified euclidian distance, which allows betters separation borders between data clusters. The model will be tested with the vibration signals collected from the standard CASE Bearing Data Sets to show the high accuracy of the results.

引用

页码：578 / 582

页数：5

共 12 条

[1]

BABUSKA R, 1998, FUZZY MODEL IDENTIFI, P53

[2]

Bezdek J. C., 1981, Pattern recognition with fuzzy objective function algorithms

[3]

CASE Bearing Data Center, US

[4]

Gustafson D. E., 1979, Proceedings of the 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, P761