An optimised variational mode decomposition method based on improved coati optimisation algorithm for blockage detection of natural gas pipelines

Enhanced blockage detection is imperative for ensuring the safe operation of natural gas pipelines, as blockages can result in serious accidents. In recent years, the negative pressure wave method has emerged as one of the most widely utilised techniques for detecting blockages. This study aims to apply variational mode decomposition (VMD) to reduce noise in transient signals from natural gas pipelines, thereby enhancing detection accuracy. This study introduces an enhanced VMD signal processing approach. It integrates an improved coati optimisation algorithm (ICOA), used to optimise the penalty term and mode number of VMD, with a fitness function based on composite multiscale attention entropy (CMAE). Subsequently, the optimised VMD is utilised to decompose the original signal, and uncorrelated intrinsic mode functions (IMFs) are eliminated using the correlation coefficient (CC) method. The remaining IMFs are then reconstructed to obtain a denoised signal. The effectiveness of ICOA and optimised VMD is validated through test functions and simulation signals. The blockage detection experiment was conducted on a metal pipe with intricate multiple branches. The findings indicate that under two distinct scenarios, the highest positioning accuracy achieved was 0.24% and −0.66%, respectively. These findings underscore that ICOA-VMD based on CMAE can significantly advance precision in blockage detection, offering practicality and efficacy.