This paper presents the results of transient tests conducted on the Trieste subsea pipeline to detect faults. The study focuses on identifying wall deterioration and other anomalies using a combination of a 1-D numerical model and analytical relationships. The transient tests involved generating safe pressure waves to simulate the effects of anomalies such as leaks and wall deterioration. The results were compared with data collected by divers, confirming the presence of wall deterioration in certain sections of the pipeline. The 1-D numerical model was used to simulate the transient response of the pipeline, incorporating the effects of anomalies. The model was validated against experimental data, showing good agreement in identifying the characteristics of pressure waves caused by different types of anomalies. The analysis revealed that wall deterioration caused a distinct pressure wave pattern, which was different from that of leaks or partial blockages. The study also highlights the limitations of transient test-based techniques (TTBTs) in accurately identifying all types of anomalies, particularly in complex pipeline systems with multiple branches and connections. The results were corroborated by diver inspections, which confirmed the presence of wall deterioration in specific sections of the pipeline. The findings suggest that wall deterioration is a significant issue in the Trieste subsea pipeline, requiring further monitoring and maintenance. The research underscores the importance of using advanced analytical and numerical methods for fault detection in subsea pipelines. The integration of machine learning with TTBTs is proposed as a promising approach for improving the accuracy and efficiency of fault detection in future studies. The study contributes to the understanding of transient flow behavior in subsea pipelines and provides a framework for the application of TTBTs in real-world scenarios.This paper presents the results of transient tests conducted on the Trieste subsea pipeline to detect faults. The study focuses on identifying wall deterioration and other anomalies using a combination of a 1-D numerical model and analytical relationships. The transient tests involved generating safe pressure waves to simulate the effects of anomalies such as leaks and wall deterioration. The results were compared with data collected by divers, confirming the presence of wall deterioration in certain sections of the pipeline. The 1-D numerical model was used to simulate the transient response of the pipeline, incorporating the effects of anomalies. The model was validated against experimental data, showing good agreement in identifying the characteristics of pressure waves caused by different types of anomalies. The analysis revealed that wall deterioration caused a distinct pressure wave pattern, which was different from that of leaks or partial blockages. The study also highlights the limitations of transient test-based techniques (TTBTs) in accurately identifying all types of anomalies, particularly in complex pipeline systems with multiple branches and connections. The results were corroborated by diver inspections, which confirmed the presence of wall deterioration in specific sections of the pipeline. The findings suggest that wall deterioration is a significant issue in the Trieste subsea pipeline, requiring further monitoring and maintenance. The research underscores the importance of using advanced analytical and numerical methods for fault detection in subsea pipelines. The integration of machine learning with TTBTs is proposed as a promising approach for improving the accuracy and efficiency of fault detection in future studies. The study contributes to the understanding of transient flow behavior in subsea pipelines and provides a framework for the application of TTBTs in real-world scenarios.