Optimization of Diesel Engine Based on the Dependency of Rough Set Theory

Abstract:

To get an effective way of monitoring the diesel engine vibration signal, the author puts forward an optimized method based on rough set attribute dependency. Decision table formed by different data collection form, their decision attribute dependence of condition is different, by comparing the different points of attribute dependence and take it as the quality criteria of measuring points, we can realize the optimization of measuring points. Take a high power diesel engine as an example, the site of rough set theory are used to calculate the typical attribute dependence, the results show that the method can effectively distinguish between different measuring point position and the sensitive degree of different fault types, the vibration signal monitoring position, with better effect is obtained at the same time reducing the effects of noise on fault diagnosis, to improve the accuracy and efficiency of fault diagnosis.

• Periodical: Advanced Materials Research (Volumes 753-755)
• Pages: 2135-2138
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.753-755.2135
• Online since: August 2013
• Authors: Jing Yi Tian, Ye Yang, Si Zhang, Wen Jing Zhao
• Keywords: Attribute Dependence, Optimization of Measuring Points, Rough Set (RS), Wavelet Packet Energy Spectrum

References

[1] Thomas. WNN knock on Diesel Engine Fault Diagnosis[J]. IEEE Transactions on the Neural Networks, 2003, 3(6): 889~898.

[2] Leo O'Connor. Diagnosing Diesel Engines Mechanical Engi-neering[M]. March (1992).

[3] Pawlak Z. Rough Sets [J]. International Journal of Information and Computer Science, 1982, (11): 341～356.

[4] Pawlak Z, Skowron A. Rough sets and boolean reasoning [J]. Information Sciences, 2007 , 177 : 41～73.

DOI: 10.1016/j.ins.2006.06.007

[5] Roman W, Swiniarski L H. Rough Sets as a front end of neural networks texture classifiers[J]. Neurocomput-ing, 2001, 36: 85～102.

DOI: 10.1016/s0925-2312(00)00337-4

[6] Z.D. LAW,Z. PAWLAK. Reasoning about Data- A Rough Set Perspective Rough Sets and Current Trends in Computing. First International Conference, RSCTC 98, 1998, 25~34.

DOI: 10.1007/3-540-69115-4_4

[7] Duoqian Miao, Daoguo Li. Rough set theory, algorithms and applications[M]. Bei Jing：Qinghua University Press, (2008).

[8] Gary G, Yen Kuo-Chung. Wavelet Packet Feature Extraction for Vibration Monitoring. IEEE Transactions on Industrial Electronics, 2000, 47(3): 12~14.

DOI: 10.1109/41.847906