Remaining Useful Life Prediction for Pressurized Fluid Pipelines Based on Acoustic Emission Monitoring and an Adaptive Fuzzy Similarity Measure

Pressurized fluid pipelines are among the most crucial components in industrial settings. Operating under high pressure leads to pipeline susceptibility to cracking, rupture, and significant damage. Monitoring the condition and predicting the remaining useful life before the failure of pressurized pipes are essential for informed and timely maintenance decisions. In this work, we propose a novel method for predicting the remaining useful life of pressurized pipelines based on acoustic emission monitoring and similarity-based learning. Specifically, acoustic emission sensors are deployed to record acoustic emission events caused by cracks in the pipeline. A pipeline health indicator is proposed based on accumulated events detected through a constant false alarm rate signal detector. Leveraging the historical run-to-fail trajectories of the health indicator, a similarity measure is introduced to predict the remaining pipeline life. This method computes the similarity between the current health indicator trajectory and past trajectories based on a Euclidean distance in the proposed derivative convolutional domain. Trajectory similarity determines the remaining lifetime similarity, which is weighted using data-adaptive fuzzy rules to estimate the current remaining useful life. Elaborate experimental validations are conducted on a custom pressurized pipeline system in a laboratory setting. Experimental results demonstrate the high efficacy of the proposed method in predicting the remaining useful life of the pipeline, surpassing other commonly used methods in both accuracy and certainty.