Preliminary Study of PHM System Based on Data Driven

Abstract:

Complex mechanical and electrical equipment contains a large amount of data with the implicit information. According to the development of PHM (Prognostics and Health Management) technology at home and abroad, and the wide application prospects of data driving methods, the overall framework of data driven PHM system for complex electromechanical equipment was designed. The data driven PHM implementation process of the complex mechanical and electrical equipment was described step by step, which provides important theoretical significance and application value for the PHM research of the complex mechanical and electrical equipment. Finally, the development trend and research challenges of data driven PHM method were analyzed.

• Periodical: Applied Mechanics and Materials (Volume 868)
• Pages: 299-304
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.868.299
• Online since: July 2017
• Authors: Zhi Xing Tian, Zhang Wei, Li Na Hao*, Shu Bing Liu, Bing Yin Qu
• Keywords: Complex Mechanical and Electrical Equipment, Data Driven, Overall Architecture, PHM

References

[1] Xiang-qian Li. Research on Key Technologies of fault prediction and health management for complex equipment [D], Beijing Institute of Technology. 2014, pp.107-110.

[2] En-peng Liu, Zhan-cai Yang and Xiao-bo Jin. Review on the development platform of fault prediction and health management system in foreign countries [J], Measurement and control technology. 2014, 33(9): 1-4.

[3] Yu Peng and Da-tong Liu. Data driven fault prediction and health management review [J], Journal of instrument and meter. 2014, 35(3): 481-495.

[4] Meng-yao Cheng. PHM technology driven by big data [J], Software and integrated circuit. 2016(5).

[5] Zhi-wei Liu, Rui Liu and Jing-song Xu, etl. Fault prediction and health management (PHM) technology research of complex system [J], Computer measurement and control. 2010, 18(12): 2687-2689.

[6] Sheng-kui Zeng, Michael G. Pecht and Ji Wu. Current status and development of fault prediction and health management (PHM) technology [J], Aeronautical Journal. 2005, 26(5): 626-632.

[7] Tian-rui Zhang Tian-biao Yu, Hai-feng Zhao and Wan-shan Wang. Application of data mining technology in fault diagnosis of full face tunnel boring machine [J], Journal of Northeastern University (Natural Science Edition). 2015, 04: 527-531+541.

[8] Cheng-lin Wen, Fei-ya Lv, Zhe-jing Bao and Mei-qin Liu. Summary of micro fault diagnosis method based on data driven [J], Journal of automation. (2016).

[9] Chao Han. Research on the application of data mining in the fault diagnosis of the Complex mechanical and electrical equipment [D], University of science and technology of Shenyang. 2011. pp.38-41.

[10] Zhong-xian Su. A survey of industrial process fault diagnosis method based on data driven [J], Software Guide. 15(3): 149-150, (2016).