## 4 Concluding Remarks  
In this research, we explored the relationship between data quality, as defined by a measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms. Our findings show the nexus between data quality and the reliability of FDI models in the context of complex systems undergoing multi-component degradation scenarios. Our analysis of historical process data, using metrics such as the relative pooled standard deviation (RPSD) and data-quality scores for individual components, provides valuable insights. It was observed that data quality is not a singular, universal determinant of FDI model performance. Although higher data quality appears to benefit data-driven FDI models, it was interesting that, in certain cases, synthetic data with lower data quality could enhance the performance of statistical process control (SPC) models. A better understanding of the impact of data quality on FDI algorithms for PHM applications would provide insights into the deployment of specific prognostic algorithms for a system such as a city-wide water distribution system. Also, from an operational and maintenance perspective, better FDI capabilities increase the overall water distribution system's uptime, as well as the likelihood of developing, for the critical components of such a system, accurate potential failure-functional failure (P-F) degradation curves. The knowledge capture related to P-F curves can also help with improvements in the operations and optimisation of maintenance activities. Furthermore, this non-linear relationship between data quality and FDI performance, as shown in our results and further in our discussion, highlights the need for a targeted approach in the design and implementation of FDI algorithms for multi-component degradation scenarios. The potential instantiation of the MSA process to optimise data quality for FDI models presents an avenue for future research and application. By creating databases with tailored data-quality parameters, training and testing datasets can be optimised to suit the requirements of specific FDI algorithms for multi-component degradation scenarios. This data optimisation approach using the MSA process provides a practical means to enhance the robustness and reliability of FDI systems in real-world industrial applications. In conclusion, this research highlights the multidimensional nature of data quality's impact on FDI model performance. Leveraging MSA principles and suitable network architectures, we can open doors to a new era of precision and effectiveness in fault detection and isolation within complex systems such as a city-wide water distribution system undergoing multi-component degradation. This work contributes to the ongoing evolution of data-driven technologies to enable the monitoring of smart infrastructure. The ability of a city-wide water distribution system to meet the needs of a growing city population requires that assets in the system be monitored with robust FDI algorithms with a clear understanding of the impact of data quality in the FDI model development process. Conceptualisation, A.K.B. and O.N. ; methodology, A.K.B. ; software, A.K.B. ; validation, A.K.B., O.N. and D.M. ; formal analysis, A.K.B. and O.N. ; investigation, A.K.B. ; resources, A.K.B. and O.N. ; data curation, A.K.B. ; writing--original draft preparation, A.K.B. ; writing--review and editing, A.K.B., O.N., A.C. and D.M. ; visualisation, A.K.B. ; supervision, O.N., A.C. and D.M. ; project administration, A.K.B. and O.N. ; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data used for this research contained or presented in the tables in the article. The authors declare no conflict of interest. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A digital twin architecture based on the industrial internet of things technologies.