Conference Agenda

Most operators are pursuing the exploitation of nuclear power plants (NPPs) beyond their initial design lifetime, introducing new challenges for the in-service inspection of metallic components. Pipes in NPPs are subject to various ageing mechanisms, including crevice corrosion, flow-assisted corrosion, fatigue, and stress-corrosion cracking, which can produce similar ageing effects such as wall thinning, crack initiation, and crack growth. Current non-destructive examination (NDE) practices, conducted during refuelling outages every one to two years, are limited in their ability to capture rapidly evolving degradation, are constrained by outage schedules, require costly and time-consuming preparations, and can expose personnel to irradiation and other hazards.

The Euratom-funded FIND project (Future Instrumentation and coNtrol based on innovative methods and Disruptive technologies for higher safety level) addresses these challenges by identifying metallic components that would benefit from Structural Health Monitoring (SHM) and developing corresponding monitoring solutions. Selected use-cases target components representative of the primary (transporting the reactor cooling water at 15 MPa and 330 °C), secondary (power-generating steam at 8 MPa, 290 °C), and tertiary (heat sink with low pressure and temperature) circuits of NPPs, encompassing a wide range of operating conditions and safety-relevant systems.

Performance objectives for SHM systems were defined for specific degradation mechanisms, accounting for the severe constraints of the nuclear environment. The tertiary circuit is often buried or difficult to access, spans hundreds of meters without electricity or network connectivity, and may feature complex coatings. Primary and secondary circuits are thermally insulated and experience high acoustic noise. In addition, the primary circuit transports a radioactive fluid, with limited access for cables and instrumentation within a compact and air-tight confinement building.

FIND partners are developing SHM systems leveraging guided waves, acoustic emission, strain gauges, thermal monitoring, and leak detection, with objectives including defect detection, stress-field reconstruction, and breach localisation. This presentation will outline the overall goals of FIND and provide detailed specifications of SHM systems designed for the selected use-cases.

Structural health monitoring (SHM) of pressure vessels is critical to ensure safe operation in industrial environments. This study presents experimental and simulation-based framework for the detection of damage in steel pressure vessels using Fiber Bragg Grating (FBG) sensors, looking for the small changes in the strain field caused by a damage. SHM is always sensors+algorithms, by collecting the strain distribution under increasing pressures and comparing the data by PCA algorithms will do the damage detection. We were using unsupervised learning methods because they do not require any hypothesis about damage position and size, neither to collect data on pristine and damaged cylinders to train the algorithm. A FEM model of the structure is done, and virtual experiments are run with different positions of the damages and the sensors, until an adequate POD is achieved. Laboratory experiments are conducted on 15 kg LPG cylinders with/without damages, to verify/validate the model and the approach. How to improve the sensitivity, by improving the SNR, and the influence of the EOC (environment and operational conditions), and particularly temperature changes, will be discussed.

The implementation of structural health monitoring (SHM) systems capable of detecting early-stage damage in bridges would be of great value to infrastructure managers, particularly if timely warnings could support efficient maintenance interventions. Achieving such capability requires detection methods that are sensitive to gradual changes in structural behavior while maintaining reliability under realistic conditions. Yet, full-scale datasets containing both undamaged and progressively damaged states are uncommon, as civil structures are generally designed to remain in service without deterioration. This paper presents a new experimental campaign conducted at the Hell Bridge Test Arena (HBTA), where damage was introduced stepwise to represent the early development of typical degradation mechanisms in steel railway bridges. An uncertainty-aware Mahalanobis-based framework is applied using autoregressive (AR) features and their estimator covariance to evaluate its capacity to detect incremental impositions of damage. The study contributes to defining the limits of data-driven damage detectability on real structures.

Bridge construction is booming in developing countries. Almost all of these mega-projects may be equipped with structural health monitoring systems (SHM). In practice there are no clear objectives and the gap between bridge construction and life-cycle operation is not addressed. Bridges are designed for at least 100 years of operation and the probability of non-linear performance with small changes in the first half and increasing risk in the second half is small.

What sense does it make to provide extensive SHM on new bridges? Innovative concepts or signature bridges, which are rare, deserve this attention for design verification. On the other hand, ordinary bridges should be equipped with useful minimum systems that allow operations on a reasonable level of effort. It is always useful to get an initial identification of bridge performance after construction is finished. Nevertheless, this usually does not require an extensive permanent monitoring system.

Considering the fact that the lifetime of a monitoring system will be definitely less than 20 years, it will record the less important period of bridge life. Anyhow, considering the small costs of SHM compared to the construction costs, it is recommended to monitor a minimum, namely the dynamic performance with accelerometers and to measure inclinations and settlements as well as the expansion joint performance.

A major issue experienced in practice was that SHM systems focus on hardware and rarely on data management, storage and exploitation. As we do not know which type of change might happen over the lifetime of a bridge, we need continuous data repositories that allow us to look for respective indicators. This is management, not rocket science.

The presentation will show a proposal on categorization of new bridges with instrumentation proposals supported by actual cases.

Structural Health Monitoring (SHM) plays a critical role in ensuring the durability, reliability, and safety of industrial infrastructures operating under severe conditions. High-temperature environments, such as furnaces and refractory linings, challenge conventional monitoring techniques. Traditional approaches, including thermocouples, provide only localized and intermittent measurements, limiting the ability to fully understand material behavior over time and space. As industrial processes evolve towards higher performance and sustainability requirements, the need for continuous, real-time, and spatially representative monitoring solutions becomes essential.

To address these limitations, advanced sensing technologies based on Fiber Bragg Gratings (FBG) and Electrical Time Domain Reflectometry (ETDR) offer a complementary approach. FBG sensors, embedded within optical fibers, enable high-precision local measurements of temperature and strain at critical locations. Their multiplexing capability allows several sensing points along a single fiber, providing a quasi-distributed sensing approach. In contrast, ETDR systems rely on high-frequency electromagnetic signals propagating through conductive transmission lines, enabling distributed measurements over long distances, even in conditions where optical fibers may not be viable.

This paper presents an integrated SHM framework combining FBG and ETDR technologies, designed for harsh industrial environments. Optical fibers equipped with FBG sensors can operate at temperatures up to approximately 700 °C, offering accurate and stable measurements. These sensors provide insights into processes such as the drying and sintering of refractory concretes, as well as thermal gradients and mechanical behavior. Beyond this range, ETDR solutions extend sensing capabilities to extreme conditions, ensuring continuity of monitoring where optical solutions become limited. The complementary use of these technologies enables both localized high-accuracy sensing and large-scale monitoring.

In addition to temperature measurements, FBG sensors can capture strain-related information, while ETDR detects parameters such as moisture evolution and phase changes. This multi-parameter approach improves understanding of structural behavior and supports predictive maintenance strategies. By detecting early signs of degradation or abnormal conditions, operators can anticipate failures, optimize maintenance, and improve safety. These condition-based strategies contribute to extending equipment lifetime and reducing costs.

A complete digital ecosystem has been developed to exploit data from FBG and ETDR sensors. Available on-premise or in the cloud, it integrates data acquisition, analytics, and visualization tools to support real-time decision-making. Combined with sensing technologies, it enables digital twins of monitored assets for simulation and optimization.

The paper presents results from industrial deployments where FBG instrumentation and ETDR sensing have been implemented in real conditions. These case studies demonstrate robustness, accuracy, and scalability, confirming suitability for large-scale deployment in smart manufacturing environments.

By integrating sensing technologies with digital platforms, this work contributes to safer, more efficient, and sustainable industrial operations, aligned with Industry 4.0.