Enhanced magnetic memory detection signals often contain numerous environmental interferences. Conventional signal processing methods, such as improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) and wavelet threshold de-noising (WTD), face significant challenges in determining parameters such as noise standard deviation (Nstd), number of realizations (Nr), wavelet basis function, and decomposition level. ICEEMDAN suffers from some feature signal loss, complicating the recognition of stress concentration, plastic deformation, and micro-defects. This study addresses these issues by proposing a joint ICEEMDAN-WTD method based on the crested porcupine optimizer (CPO) algorithm called CPO-ICEEMDAN-WTD. The proposed approach leverages CPO to optimally select Nstd and Nr using the minimum entropy value method, performs ICEEMDAN decomposition to obtain the signal, and classifies it through the mutual information entropy (MIE) method. The noise components are then processed with WTD, and the noise-reduced signal is reconstructed. This method reduces noise in enhanced magnetic memory detection signals for Φ2 × 0.5 mm indentation defects, using signal-to-noise ratio (SNR) and information entropy (IE) as evaluation indicators. Experimental results demonstrate that the CPO-ICEEMDAN-WTD method significantly enhances noise reduction, with the SNR of the horizontal component Bx, normal component By, tangential component Bz, and their gradient values in the magnetic field yielding a maximum improvement of 312.49%, and IE minimized by 43.13%.

As critical equipment in the petroleum industry, coiled tubing is prone to safety hazards, including stress concentrations and fatigue failure, under complex operating conditions. An online enhanced metal magnetic memory detection method was employed to reduce noise in surface magnetic field signals from tubing subjected to 35 MPa of internal pressure across different fatigue cycles. Conventional signal processing methods have difficulty effectively extracting characteristic magnetic field signals in high-noise environments; therefore, a comprehensive comparison of the noise reduction effectiveness of five common signal processing techniques in stress-distorted regions was conducted, an in-depth analysis of the limitations of different methods was performed, and a hybrid noise reduction framework combining wavelet threshold denoising (WTD) and sequential variational modal decomposition (SVMD) was established. Concurrently, the whale optimization algorithm (WOA), which possesses global search capabilities and demonstrates good adaptability to multi-parameter coupling issues in hybrid denoising frameworks, was innovatively proposed for key parameter optimization. Using fuzzy entropy (FE) as an evaluation metric, the experimental results demonstrated that magnetic field signals in all directions achieved at least a 1.03% reduction in FE and a minimum increase of 33.1% in integrated side lobe ratio (ISLR). This provided effective technical support for reliably detecting stress-distortion zones on coiled-tubing surfaces and established the engineering necessity of implementing preventive maintenance.