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Recognition Method of Health Monitoring Point of Quayside Container Crane Based on Fuzzy Evaluation

GUAN-SI LIU, KE-QIN DING, HUI JIN, LI CHEN

Abstract


The quayside container crane is the crucial equipment of port handling operation. With the increasing demand of trade logistics, quayside container cranes are developing towards more and more large and high-parameter to adapt to larger ships. With the increase of logistics demand, quayside container cranes are developing towards large-scale and high-parameter. Nevertheless, the fatigue problem of metal structures has become increasingly prominent due to the long-term and high-strength continuous operation of equipment. The structure of quayside container crane is huge, and there are many types of connection, such as welds, openings, variable crosssection, etc. Therefore, the mechanical condition of equipment structure is complex. How to effectively determine high-risk fragile zones and health monitoring points is an urgent problem to be solved, which is also the key to success or failure of structural health monitoring. Five key factors of quayside container crane is been considering, such as structure type, welding technology, heat treatment, non-destructive testing and load condition, this paper puts forward a method for identifying health monitoring points of quayside container crane. Firstly, the hierarchical structure model of highrisk fragile zone fuzzy recognition is constructed. Secondly, the analytic hierarchy process (AHP) is proposed to determine the weight of each index. Then, the risk value of fragile zone is calculated by using the theory of fuzzy evaluation. Finally, the method is used to identify the high-risk fragile areas and health monitoring points of quayside container cranes. In view of the field application of health monitoring for a port quayside container crane, the method proposed in this paper is used to determine the location of health monitoring points, which coincides with the location of cracks in equipment in the past. The method proposed in this paper provides a scientific and effective means for the location selection of structural health monitoring, and has important value for improving the effectiveness of structural health monitoring.


DOI
10.12783/shm2019/32512

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