Industrial fluid pipeline leak detection and localization based on a multiscale Mann-Whitney test and acoustic emission event tracking

This paper proposes a novel technique for leak detection and localization in industrial fluid pipelines. Artificial intelligence-based supervised methods are a popular adoption for pipeline leak identification. However, supervised techniques need prior knowledge about pipeline failure for training purposes. To address this challenge, a new method for pipeline leak detection independent of prior knowledge is proposed. First, acoustic emission signals from the pipeline are acquired. Then, a multiscale Mann-Whitney test is developed, and the test output statistics are used as a pipeline leak state indicator. After detecting the leak, the proposed method localizes the leak by using a newly developed method called acoustic emission event tracking. A major challenge in leak localization is the false alarms that are generated by the poor identification of leak-related acoustic emission events. The acoustic emission event tracking presented in this work precisely determines the leak-related acoustic emission events. The new method first detects acoustic emission hits by using the variability index constant false alarm rate algorithm. Then, short-term energy is calculated in the hit perceived variability index constant false alarm rate windows. The high-energy acoustic emission events are separated into an event bank. Leak-related acoustic emission events are filtered out from the event bank using the theory of wave propagation. The filtered events obtained from the proposed method elucidate leaks and thus reduce the error in leak localization. The results obtained from the proposed method outperformed the reference methods in terms of accuracy for leak detection and localization under variable pressure and leak conditions.