_applied sciences_  
_Article_  
**Data-Quality Assessment for Digital Twins Targeting Multi-Component Degradation in Industrial Internet of Things (IIoT)-Enabled Smart Infrastructure Systems**  
**[PERSON] 1,*\(\copyright\), [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
1 Department of Applied Science, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow C4 0 BA, UK; [EMAIL_ADDRESS]  
2 Department of Mechanical Engineering, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0 BA, UK; [EMAIL_ADDRESS] (D.M.); [EMAIL_ADDRESS] (A.C.)  
* Corresponding: [EMAIL_ADDRESS]; Tel.: +44-141-273-1816  
**Featured Application: The paper highlights the steps taken in checking the required data quality (both real and synthetic) before it is used for the development of services in the context of IIoT-enabled smart infrastructure systems. A case study of a scaled-down version of a water distribution system will be presented in detail and data from healthy and faulty conditions will be used to demonstrate the details of the data qualification process and the impact on various health assessment techniques meant to support fault detection and isolation of single and multi-component degradation scenarios. The paper also proposes an IIoT architecture for the instantiation of measurement system analysis.**  
**Abstract: 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.**  
**Keywords: digital twins; industrial internet of things (IIoT); instrumentation; data quality; statistical process control; machine learning**