Predicting leak aperture in the pipeline of ultra-long coal mine working faces under strong noise interference based on joint denoising and random forest

Abstract

Ultra-long coal mine working faces are characterized by harsh conditions, making water supply pipelines susceptible to leakage incidents, posing risks to both personnel and equipment safety. However, detecting leaks using infrasound signals is challenging due to complex environmental noise interference. Additionally, spatial constraints make inspections difficult for maintenance personnel. Hence, it is vital to enhance the clarity of leakage data and accurately assess leak severity for effective pipeline maintenance. This study combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Sparse Representation (SR) techniques to create a signal processing model. This model serves two purposes: reducing extraneous noise interference and enhancing leak-related signals. Subsequently, we analyse leak signal characteristics across various operational scenarios and formulate a leak severity assessment model using the Random Forest (RF) algorithm. Empirical experiments confirm its effectiveness, with a root mean square error (RMSE) of 0.1135 millimeters between predicted and actual leak severity values. This result validates the method’s efficacy in estimating leak severity in water supply pipelines, offering a dependable approach to enhance pipeline maintenance and operations.