Mechanical Fault Diagnosis Based on Data Mining Technology

Abstract:

Data mining is a variety of analysis tools using the data found in the mass relationship between model and data technology, and relationships of these models can be used to predict. Organically with the combination of expert system, can solve the problem of automatic acquisition of knowledge. It from a large number, incomplete, noisy, fuzzy, random data mining the hidden decision-making has important reference value to generate the information. Data mining technology used in machinery fault diagnosis system, the scene of large quantities of raw data into valuable knowledge for specialists to explore the information they are interested in, but also describes the evolution trend of mechanical malfunction, the expert decision-making support information. And, after years of development, data mining technology has matured and widely used. The purpose of this study is that data mining technology used in machinery fault diagnosis and to study its feasibility. If applicable, must be able to open up a mechanical fault diagnosis, a new diagnostic method, which greatly promote the development of mechanical fault diagnosis technology.

• Periodical: Advanced Materials Research (Volume 320)
• Pages: 663-668
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.320.663
• Online since: August 2011
• Authors: Wei Hua Shen
• Keywords: Data Mining (DM), Diagnosis, Feasibility, Mechanical Fault

References

[1] Zhang Xiaomei. Fault diagnosis in feature extraction. Sensor technology, 2004, 3: 3 4.

[2] Zhengyou money fountain. Calculation of multi-resolution information and entropy in the fault detection. Electric Power Automation Equipment. 2001, 21 (5): 9 ~ 11.

[3] HUANG Xi Yue Ma Huang Min Xiao laugh and so on. The diagnosis based on information entropy analysis of cognitive information flow. Chongqing University, 2002, 25 (5): 25 ~ 28.

[4] Chih-Chien Huang. Mechanical fault diagnosis of the methodology. Metallurgical Series, 2002, 1: 10 ~ 14.

[5] Feng Zhipeng. Computational Intelligence in Fault Diagnosis of Machinery in the application. Turbine Technology, 2002, 4: 16 ~ 19.

[6] Zhu Jianqiu. Data mining discussion groups, http: /www. dmgroup. org. cn.

[7] Shi Liping Zhang Ying. Data mining technology in machinery fault diagnosis. Industrial control computer, 2006. 12: 17 22.

[8] Chen Li Ruqiang Chen Yiming Wu Xing. Based on Data Mining equipment condition monitoring and fault diagnosis. Vibration and shock, 2004. 04: 6 8.

[9] Yang Jing Zhang Shaobing Zhang Jianpei. Optimization and Data Mining in Fault Diagnosis. Coal Mine Machinery, 2005. 09: 23 25.

[10] SHI Guodong. Data Mining in Fault Diagnosis. Jiangsu Institute of Petrochemical Technology, 2001. 04: 7 11.