Big Data Analysis Method of Random Stress Spectrum for Crane Equipment

DOI

10.2991/acsr.k.191223.002How to use a DOI?

Keywords

big data analysis method, random stress spectrum, crane equipment

Abstract

Fatigue damage is one of the most important failure modes of crane equipment. It is an important means to judge the fatigue damage of crane equipment structure by analyzing the random stress spectrum big data collected by the structural health monitoring system of crane equipment. Rain flow counting method is the main method for big data analysis of random stress spectrum, but it has not been applied in the on-line data analysis of crane equipment structural health monitoring system. In this paper, the big data analysis method of random stress spectrum of crane equipment is studied. The arithmetic of rain flow counting method is improved. The program of fast rain flow counting method with two parameters is compiled. The online real-time analysis of big data of random stress spectrum is realized. In this paper, the proposed method is used to analyze and calculate the random stress spectrum big data collected by the structural health monitoring system of metallurgical crane, and the effective stress amplitude-frequency histogram of the hot spot area of fatigue damage of the main girder is obtained, which lays an important foundation for the subsequent analysis of fatigue damage and health status of crane equipment.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Chen
AU  - Keqin Ding
PY  - 2019
DA  - 2019/12/24
TI  - Big Data Analysis Method of Random Stress Spectrum for Crane Equipment
BT  - Proceedings of the 2019 International Conference on Big Data, Electronics and Communication Engineering (BDECE 2019)
PB  - Atlantis Press
SP  - 8
EP  - 11
SN  - 2352-538X
UR  - https://doi.org/10.2991/acsr.k.191223.002
DO  - 10.2991/acsr.k.191223.002
ID  - Chen2019
ER  -