PSI - Issue 80

M. Bennebach et al. / Procedia Structural Integrity 80 (2026) 136–145 Author name / Structural Integrity Procedia 00 (2019) 000–000

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1. Introduction Pressure vessels are used in many industrial sectors such as petrochemical, power generation, chemical and pharmaceutical industries. These types of equipment are subjected to harsh operating conditions, which can lead to progressive degradation of their structural integrity, resulting in risks to the safety of people, property and environment. Design, manufacture and operation of pressure vessels are strictly regulated by laws and building codes, making it difficult to replace traditional maintenance strategies with innovative ones. Developing real-time monitoring techniques of industrial equipment is increasingly necessary to avoid unplanned production downtime and reduce maintenance costs. In this context, digital twins are emerging as an effective solution, however, despite their potential, their implementation remains a challenge and requires consideration of several factors such as service loads, material properties, equipment geometry and environmental conditions, which makes modelling complex. In this paper, we present a digital twin-based methodology for real-time residual life and damage monitoring, combining models based on physics and data science. During the last few years, the use of digital twins has grown rapidly due to evolutions in IOT (Internet of Things), HPC (High Performance Computing) and modelling techniques such as ROMs (Reduced Order Models). Several studies have been conducted for pressure vessels, which have shown the potential benefits of digital twins for equipment monitoring and maintenance, however, these approaches suffer from limitations, such as lack of accuracy in damage prediction, and inefficient use of sensors in terms of cost and resources. In addition, the fatigue criteria and approaches used in construction codes like ASME, IIW or CODAP are not directly adapted to in-service equipment monitoring, which can cause difficulties. The proposed approach, schematized in Figure 1, aims to overcome some of these limitations. It proposes an innovative approach using hybridization of finite element and data science techniques, to predict the damage rate and residual life of the equipment in real time, based on minimal information from strain gauges. To improve monitoring efficiency, use of principal component analysis is combined with sensor placement optimization. This approach maximizes the coverage of the strain field with a minimal number of sensors, which reduces the cost and time required for sensor installation and maintenance. Figure describes the global approach.

Fig. 1. Global approach description.

2. Construction of the virtual clone The objective of this phase is to develop a high-fidelity numerical model of the equipment, which allows identification of the critical zones in terms of stress levels, potential defects occurrence and estimate the fatigue damage. The model has been calibrated from experimental test data to ensure that simulation results agree with experimental measurements. By identifying critical areas before instrumentation, the modelling will also allow for sensor placement optimization.