Abstract
In consequence of the characteristics of interdependence, inter-constraint, and highly coupling for complex equipment systems, there are complex correlations between equipments. Then a large number of diversified fault records are produced from the operating process of systems, and these fault records become reflected forms about correlation failures. Determining how to use the fault information feature words—mined from failure records—of equipment effectively to obtain the correlation failure relationship between the equipment becomes the focus of this paper. Given this, we propose the improved FP-Growth algorithm which is obtained through optimizing the classical correlation rule mining algorithm, with experimental results proving it to be actually efficient on running time for mining frequent patterns; based on the improved FP-Growth, correlation analysis is carried out based on the fault information feature words of equipment, then correlation failure rules are extracted, and a correlation failure model is established. At last, taking the CRH2 EMU traction system as example, the correlation failure rules are obtained and a correlation failure model for this system is established, thereby verifying the validity and practicability of the improved algorithm.
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Acknowledgments
The project is supported by the independent subject (Grant No. I14K00451) and National High Technology Research and Development Program of China (863 Program) (Grant No. T13B200011).
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Wang, Y., Wang, S., Lin, S. (2016). Correlation Failure Analysis Based on the Improved FP-Growth Algorithm. In: Qin, Y., Jia, L., Feng, J., An, M., Diao, L. (eds) Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Lecture Notes in Electrical Engineering, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49370-0_14
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DOI: https://doi.org/10.1007/978-3-662-49370-0_14
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