Big Data Fast Analysis Method of Random Stress Spectrum for Crane Equipment

Abstract

With the increase of service time, the random stress spectrum of crane equipment causes cumulative damage. Cumulative damage induced crack initiation is one of the important failure modes of crane equipment. Through the rapid analysis of the random stress spectrum obtained in real time by the structural health monitoring system of lifting equipment, the cumulative damage of the structure can be analyzed and the remaining life of the structure can be predicted. In this paper, based on the traditional rain flow counting method, the big data fast analysis method of random stress spectrum is studied by adopting stack data structure. The program of fast rain flow counting method based on stack structure with two parameters is compiled. The fast rain flow counting method based on stack structure is used to analyze and calculate the big data of stress monitoring collected by the structural health monitoring system of metallurgical crane, and the stress amplitude-mean-frequency matrix of the hot spot area of fatigue damage of the main girder is obtained, which lays an important foundation for the health status diagnosis of metallurgical crane equipment.