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Research on an intelligent monitoring and diagnosis system for fatigue damage in coke drums based on data-driven approaches

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Veröffentlicht in:Journal of Physics: Conference Series vol. 3141, no. 1 (Nov 2025), p. 012025
1. Verfasser: Chen, Li
Weitere Verfasser: Zhang, Xu, Ding, Keqin
Veröffentlicht:
IOP Publishing
Schlagworte:
Structural health monitoring
Cumulative damage
Multidimensional data
Diagnosis
Damage assessment
Life prediction
Fatigue failure
Data acquisition
Multidimensional methods
Coke
High temperature
Edge computing
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Abstract:As critical high-temperature pressure equipment in the petrochemical industry, coke drums operate for extended periods under complex working conditions. Their structural health status directly impacts production safety and operational efficiency. This paper proposes an intelligent fatigue damage monitoring and diagnosis system based on strain data-driven methods. By collecting and analyzing multidimensional data such as strain and temperature in real time, the system rapidly constructs rainflow matrices and nonlinear cumulative damage models to achieve dynamic diagnosis of fatigue damage, health status classification, and remaining useful life prediction. The system integrates sensing, acquisition, transmission, and edge computing modules. Combined with multi-dimensional data acquisition software and an intelligent diagnosis visualization platform developed on the LabVIEW platform, it enables end-to-end intelligent management from data sensing to condition assessment. Engineering applications demonstrate that the system exhibits excellent reliability, stability, and engineering applicability, providing a feasible solution for structural health management of coke drums and other large-scale pressure equipment.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/3141/1/012025
Quelle:Advanced Technologies & Aerospace Database