Research on Data Acquisition Scheme in PHM System Based on Data Driven

Abstract:

In this paper, a kind of data driven PHM system architecture based on data driven is came up; then a field data acquisition scheme which is very important in PHM system is presented; with a case, the block diagram of the scheme realization, which includes OPC server software configurations, and client software development process is shown. In this case, the data acquisition software has been developed by using the C #language and the local database and the data center database has been created to save data. The data acquisition software can read data from the PLC and save data to the database, which provides a stable and accurate data source for PHM system and has good stability and compatibility.

• Periodical: Applied Mechanics and Materials (Volume 868)
• Pages: 311-315
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.868.311
• Online since: July 2017
• Authors: Zhang Wei, Zhi Xing Tian, Nie Xin, Li Na Hao*, Shu Bing Liu, Bing Yin Qu
• Keywords: Data Acquisition, Data Driven, PHM, Productions System

References

[1] Vichare N M, Pecht M G. Prognostics and health management of electronics [J]. IEEE Transactions on Components & Packaging Technologies, 2006, 29(1): 222-229.

DOI: 10.1109/tcapt.2006.870387

[2] Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance [J]. Mechanical Systems & Signal Processing, 2006, 20(7): 1483-1510.

DOI: 10.1016/j.ymssp.2005.09.012

[3] O'Donovan P, Leahy K, Bruton K, et al. A data pipeline for PHM data-driven analytics in large-scale smart manufacturing facilities [C]. Conference of the Prognostics and Health Management Society. (2015).

DOI: 10.1186/s40537-015-0034-z

[4] Liu, Jie, Wang, Golnaraghi (2009). A multi-step predictor with a variable input pattern for system state forecasting. Mechanical Systems and Signal Processing. 23 (5): 1586–1599. doi: 10. 1016/j. ymssp. 2008. 09. 006.

DOI: 10.1016/j.ymssp.2008.09.006

[5] Mosallam, A., Medjaher, K; Zerhouni, N. (2014). Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing. doi: 10. 1007/s10845-014-0933-4.

DOI: 10.1007/s10845-014-0933-4

[6] Shifeng Y, Tao Y. Networked Monitoring and Control System of Tyre Building Machine Based on Virtual Instrument [J]. (2015).

[7] Xavier Desforges, Abdallah Habbadi, Laurent Geneste et al. Distributed machining control and monitoring using smart sensors/actuators [J]. Journal of Intelligent Manufacturing, 2004, 15(1).

DOI: 10.1023/b:jims.0000010074.22952.de