Digital twin (DT) technology has become a key enabler for prognostics and health management (PHM) in complex industrial systems, yet scaling predictive models for multi-component degradation (MCD) scenarios remains challenging, particularly when transferring insights from predictive models of smaller systems developed with limited data to larger systems. To address this, a physics-informed neural network (PINN) framework that integrates a standardized scaling methodology, enabling scalable DT analytics for MCD prognostics, was developed in this paper. Our approach employs a systematic DevOps workflow that features containerized PINN DT analytics deployed on a Kubernetes cluster for dynamic resource optimization, a real-time DT platform (PTC ThingWorx™), and a custom API for bidirectional data exchange that connects the cluster to the DT platform. A key contribution of this paper is the scalable DT model, which facilitates transfer learning of degradation patterns across heterogeneous hydraulic systems. Three (3) hydraulic system configurations were modeled, analyzing multi-component filter degradation under pump speeds of 700–900 RPM. Trained on limited data from a reference system, the scaled PINN model achieved 88.98% accuracy for initial degradation detection at 900 RPM—outperforming an unscaled baseline of 64.13%—with consistent improvements across various speeds and thresholds. This work advances PHM analytics by reducing costs and development time, providing a scalable framework for cross-system DT deployment.

This research aims to examine the challenges in developing Prognostics and Health Management (PHM) analytics for Digital Twin (DT) use cases in industrial applications, with a particular focus on Multi-Component Degradation (MCD) scenarios. A hybrid methodology, integrating physics-informed and data-driven models, is employed, using limited asset degradation data for model development. Preliminary work includes an analysis of the impact of data quality on Fault Detection and Isolation (FDI) algorithm performance, as well as the proposal of a weighted ensemble hybrid approach for assets experiencing MCD scenarios Preliminary results indicate enhanced diagnostics in asset health management through the use of Physics-Informed models for FDI in MCD scenarios with limited prior degradation data. Expected contributions for this research are the development of physics-informed PHM analytics for DT applications in MCD scenarios, adaptive PHM analytics for evolving asset lifecycles in DT applications, and interpretable DT model analytics for PHM in systems facing Multi-Component Degradation.

In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, defined as the measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms within smart infrastructure systems. This research employs a comprehensive methodology, starting with an MSA process for data-quality evaluation and leading to the development and evaluation of fault detection and isolation (FDI) algorithms. During the MSA phase, the repeatability of a water distribution system’s measurement system is examined to characterise variations within the system. A data-quality process is defined to gauge data quality. Synthetic data are introduced with varying data-quality levels to investigate their impact on FDI algorithm development. Key findings reveal the complex relationship between data quality and FDI algorithm performance. Synthetic data, even with lower quality, can improve the performance of statistical process control (SPC) models, whereas data-driven approaches benefit from high-quality datasets. The study underscores the importance of customising FDI algorithms based on data quality. A framework for instantiating the MSA process for IIoT applications is also suggested. By bridging data-quality assessment with data-driven FDI, this research contributes to the design of digital twins for IIoT-enabled smart infrastructure systems. Further research on the practical implementation of the MSA process for edge analytics for PHM applications will be considered as part of our future research.

This study seeks to address the challenge of limited degradation data in developing Fault Detection and Isolation (FDI) models for multi-component degradation (MCD) scenarios. Utilizing a small fraction (0.05%) of a previously utilized water distribution testbed dataset in a previous publication, a weighted ensemble hybrid approach is proposed and evaluated against more established modelling approaches used in the previous publication. The proposed approach combines heuristic approximation and Physics-Informed Neural Network (PINN) methods with a recurrent neural network (RNN) model to enhance diagnostic performance for predicting MCD scenarios. The hybrid model generally outperformed other algorithms when tested on an MCD dataset, demonstrating improved diagnostic accuracy in such scenarios. Future research aims to optimize ensemble weights based on model uncertainty, further enhancing diagnostic capabilities.

In recent times, the oil and Gas industry has faced many challenges resulting from a tightening climate policy environment on oil and gas exploration, as well as the increasing risk of oversupply due to new discoveries globally. This has given stakeholders in the industry an incentive to integrate new technologies to optimize the operational efficiency of their assets, leading to the optimal recovery of hydrocarbons especially in marginal fields. Various Original Equipment Manufacturers (OEM) now provide different service offerings using data driven methods to provide condition monitoring of assets for oil and gas operators. However, a significant part of the value proposition by OEMs in their service delivery focuses on value generated at the component level with a reduction in asset downtime. This limits the broad economic benefits that a condition-based approach can provide, at the enterprise level. Therefore, the purpose of this paper is to develop a cost benefit analysis framework for assessing the implementation of condition and performance monitoring of oil and gas assets used in surface applications. The framework utilizes a combined technical-economic approach to determine a minimum predictive requirement for the implementation of condition-based principles to maintenance of assets in a hydrocarbon project from first oil to abandonment. This financial analysis framework uses a condition monitoring approach based on prognostics as well as a regression approach for fault detection and system performance. The paper will present a case study to evaluate the costs and benefits associated with implementing a condition-based maintenance approach for a set of valves in a Christmas tree subsystem, as part of a typical onshore production system. The framework illustrated using the case study compares a constant failure rate Time-Based approach to the PHM enabled condition-based maintenance. The results demonstrate that a prognostic enabled system can provide commercial benefits at the component level for a Condition Based Maintenance strategy but not necessarily at the enterprise level for an oil and gas project. The cumulative reduction in downtime at the component level over a project lifecycle offsetting the present value of the total cost of integrating a PHM enabled system into the overall maintenance strategy creates the ideal situation for commercial viability. However, the commercial viability of the PHM integration would depend on the accuracy of predicting failure events and monitoring asset degradation by the PHM enabled system which ultimately defines the performance of the condition-based maintenance approach. The accuracy level of asset failure therefore provides OEMs with a benchmark for executing their condition-based maintenance services with a minimum performance threshold. Secondly, an enterprise level financial viability, as well as OEM profitability in the implementation of a condition-based maintenance approach, requires an Optimal Service Point (OSP) which is a function of the minimum predictive requirement of the PHM system. The utility that the OSP provides is that, it gives the minimum value of the framework’s decision criteria that an operator can use a basis for incorporating a condition-based approach in its maintenance strategy. It also provides the maximum Annual Service Fee (ASF) derived from the cumulative OEM NPV needed for structuring and pricing servitization agreements with operators. This OSP cost benefit analysis approach ultimately provides OEMs and operators with a practical guide in the provision as well as the adoption of condition-based maintenance strategies respectively. It balances the risk of PHM integration by operators with a minimum PHM system performance threshold required for commercial viability for project lifecycle.

Digital Twins (DTs) have become central to intelligent asset management within Industry 4.0, enabling real-time monitoring, diagnostics, and predictive maintenance. However, implementing Prognostics and Health Management (PHM) strategies within DT frameworks remains a significant challenge, particularly in systems experiencing multi-component degradation (MCD). MCD occurs when several components degrade simultaneously or in interaction, complicating detection and isolation processes. Traditional data-driven fault detection models often require extensive historical degradation data, which is costly, time-consuming, or difficult to obtain in many real-world scenarios. This paper proposes a model-based, control-driven approach to MCD detection, which reduces the need for large training datasets by leveraging reference tracking performance in closed-loop control systems. We benchmark the accuracy of four control strategies—Proportional-Integral (PI), Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and a hybrid model—within a Digital Twin-enabled hydraulic system testbed comprising multiple components, including pumps, valves, nozzles, and filters. The control strategies are evaluated under various MCD scenarios for their ability to accurately detect and isolate degradation events. Simulation results indicate that the hybrid model consistently outperforms the individual control strategies, achieving an average accuracy of 95.76% under simultaneous pump and nozzle degradation scenarios. The LQR model also demonstrated strong predictive performance, especially in identifying degradation in components such as nozzles and pumps. Also, the sequence and interaction of faults were found to influence detection accuracy, highlighting how the complexities of fault sequences affect the performance of diagnostic strategies. This work contributes to PHM and DT research by introducing a scalable, data-efficient methodology for MCD detection that integrates seamlessly into existing DT architectures using containerized RESTful APIs. By shifting from data-dependent to model-informed diagnostics, the proposed approach enhances early fault detection capabilities and reduces deployment timelines for real-world DT-enabled PHM applications.