Subsea pipelines are critical in transporting production in the modern oil and gas industry. However, over time, factors like corrosion or deformations can cause degradation, potentially leading to significant economic and environmental harm if not promptly addressed. As a result, regular inspection of subsea pipelines is essential to prevent catastrophic events. A human cognition-inspired condition management framework has been proposed in an ongoing project. The system is designed to leverage various data sources and integrate them with analytical models and knowledge-based systems to assist in equipment diagnosis and recommend optimized operation and maintenance strategies. Multisensory integration is the process by which the brain combines information from different sensory modalities to form a coherent perception of the environment. We sought an integrated dataset containing various observations, such as equipment, environmental, and operational data, to demonstrate the experimental verification of the multi-sensor integration mechanism and the different stages of condition monitoring stages of the framework. For a comprehensive and optimal case study, we selected the Subpipe inspection dataset, showcasing pipeline localization, fault detection, and fault diagnostic tests performed on the dataset.