Pipeline damage identification in nuclear industry using a particle swarm optimization-enhanced machine learning approach

Abstract

The safety of nuclear industry pipelines is of paramount importance due to their vulnerability to damage from a variety of environmental factors. Ultrasonic guided wave inspection technology presents advantages such as high efficiency, low cost, and convenience, making it a widely adopted method for detecting damage in pipelines. However, the propagation of guided waves and the sensitivity of piezoelectric sensors can be significantly affected by environmental and operational conditions, leading to interference with damage identification. To address this challenge, we propose a robust machine learning-based method for identifying pipeline damage. Our approach integrates a model that comprises a particle swarm optimization-enhanced bidirectional gated recurrent unit-attention mechanism. Leveraging the attention mechanism, our method allocates more attention to significant feature dimensions, highlighting the influence of critical damage features in the identification process. Experimental tests of our model on a nuclear industry circulating water cooling pipeline demonstrate its efficacy in identifying pipeline damage under varying temperature and pressure conditions. Our proposed model outperforms other data-driven models, such as gated recurrent networks, long short-term memory, and bidirectional gated recurrent networks. Specifically, our model achieved an increase in accuracy of 2.2%, indicating the effectiveness and superiority of our proposed method.