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.

Technology is constantly evolving, changing how industries work by making processes more efficient. This shift, known as Industry 4.0, connects the real and digital worlds, allowing devices, people, and systems to communicate through the Internet. Key technologies include Internet of Things, big data, additive manufacturing, artificial intelligence, machine learning, augmented reality, and digital twins. This paper focuses on researching and implementing enabling technologies of Industry 4.0 by illustrating the design of DT models and AR experiences of a test bench with a motorized planetary gearbox. The case study starts from the CAD realization and assembly of all the components by using PTC Creo Parametric. Then, sensors are installed and connected to data acquisition devices to acquire and monitor input and output parameters of the system. Finally, AR experiences are developed using Vuforia Studio starting from CAD models of real components. The AR experience showed when the gearbox was not working properly, with the output shaft running about 13% slower than expected. Because of this, the gearbox was disassembled, and the inspection found some rusted parts inside. This paper presents the steps to create a DT model and AR interface using affordable hardware and software. The project shows how these tools can be used in industrial settings and education for activities such as hands-on learning, plant design, real-time monitoring, predictive maintenance, remote support, and sharing knowledge.

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.

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.

Flow regimes of vertical upflow for slightly cohesive Geldart A powders at high solids mass flux ($G_s$$\gtrsim$ 500 kg/m²s) are not fully resolved. In particular, Dense Suspension Upflow (DSU) as a distinct flow regime and its transition boundaries are not broadly accepted. Furthermore, the locus of the pressure gradient minimum, which is the broadly accepted dense–dilute transition at low $G_s,$ requires validation at high $G_s$. In our recent work, by adapting the phase map of Wirth and by Eulerian modeling, DSU was defined as a distinct flow regime with gross upflow of solids and with granular temperature at the wall greater than that in the bulk. This study has further validated the definition of DSU and its transition boundaries by extending the modeling to areas not fully explored in the earlier work. Furthermore, this study has identified (a) the possibility of a phase of DSU between fast fluidization and turbulent regime at all $G_s$; and (b) the need to review the suitability of the locus of the pressure gradient minimum as the dense–dilute transition at high $G_s$. Additionally, our work has demonstrated (a) a new provisional correlation that the upper transport velocity for Geldart A powders is significantly greater than hitherto predicted; and (b) the slip velocity in the transport regimes increases with $G_s$ to peak within fast fluidization and falls thereafter to attain low multiples of the terminal settling velocity within DSU.