RIME-ICEEMDAN-WPD-Based Denoising for MFL Sensor Signals in Pipeline Defect Detection

Magnetic Flux Leakage (MFL) sensors are pivotal for the non-destructive inspection of oil and gas pipelines. However, the accuracy of defect quantification is severely compromised by pervasive noise in field-acquired MFL sensor signals, leading to substantial measurement uncertainty. To address this, we introduce a novel hybrid denoising framework that synergizes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Wavelet Packet Decomposition (WPD). The key innovation is the employment of the Rime Optimization Algorithm (RIME) to automatically fine-tune the critical parameters of ICEEMDAN—the signal-to-noise ratio (SNR) and the number of noise additions—thereby customizing the decomposition for superior sensor signal enhancement. This optimization effectively suppresses mode aliasing and yields intrinsic mode functions that faithfully represent underlying defect features. The framework’s efficacy is rigorously validated through mathematical modeling, COMSOL Multiphysics 6.3-based finite element simulation, and real-field MFL sensor data. Results demonstrate remarkable improvements in sensor signal quality: a 53.69% increase in the SNR and reductions of 61.03% in MAE and 62.05% in RMSE over conventional methods. Crucially, the method achieved a Feature Preservation Rate (FPR) of 97.18% on simulated defects, underscoring its exceptional capability to retain critical metrological features for defect sizing. This work provides a robust signal-processing framework that significantly advances the measurement fidelity of MFL sensors, enabling more reliable pipeline integrity assessment.