Haiting Zhou

To ensure the welding quality of HDPE pipe butt fusion joints，this paper presents a differential wavenumber filtering method proceses frequency-wavenumber (DWFM-f-k) imaging algorithm. The ultrasonic time domain data is transformed into the frequency-wavenumber domain, and a differential wavenumber filter is constructed to suppress low coherence noise components. Defect image reconstruction is achieved by combining Stolt inverse mapping and nonuniform fast Fourier transform (NUFFT) interpolation. Laboratory tests are conducted using HDPE test block and pipe butt fusion joint specimen. The results demonstrate that the proposed algorithm effectively suppresses noise interference and clearly reconstructs the defect images. Compared with the traditional total focusing method (TFM), the proposed algorithm improves the signal-to-noise ratio (SNR) by 2.35 dB to 6.42 dB and reduces the array performance index (API) by 6.43% to 20.88%. Meanwhile, the imaging efficiency is increased by 69.03% to 70.05%. Furthermore, field tests are conducted on in-service gas HDPE pipes. The field tests results indicate that the proposed algorithm can more clearly reconstruct the butt fusion interface and successfully detect lack of fusion defects. Therefore, the proposed algorithm exhibits greater potential for on-site detection of in-service gas HDPE pipes.

Since hydrogen atoms are enriched in the corrosion defect regions of steel pipelines under stress-induced effects when transporting gaseous hydrogen, this leads to the hydrogen embrittlement (HE) phenomenon. Therefore, this paper conducts hydrogen diffusion simulation experiments on X80 pipeline steel for defective pipelines under different stress fields to observe the combined effects of internal defects, stress fields, and hydrogen. By coupling the stress field and hydrogen concentration field under different internal pressures sequentially, observing the hydrogen diffusion distribution of the pipeline under the effect of corrosion defects and stress field, calculating the hydrogen concentration of the X80 pipeline steel samples under different working conditions, and analyzing the impact of hydrogen damage of the corroded pipeline under the impact of stress-hydrogen coupling. The results show that at 24h, the Von Mises stress at 10MPa internal pressure at the defect increased by 37% compared with 5MPa, and the hydrogen concentration in the 10MPa stress field increased by 43% compared with 5MPa. Therefore, with the increase of internal pressure, the stress field of corroded pipeline increases. Hydrogen diffusion under stress induction causes hydrogen atoms to gather in the stress concentration area, thus endangering the safety of the pipeline.

This paper proposes an improved Yolov8 network (ADC-Yolov8) for ground penetrating radar (GPR) signal recognition and localization of buried pipelines. The method addresses the challenges in processing GPR images with multiple targets, small targets, and weak textures. The key innovations include: (1) constructing an efficient feature fusion structure using the ADown module; (2) enhancing multi-scale features through the DualConv_C2f module with channel attention; and (3) improving complex feature extraction capability by embedding the CA mechanism of HS-FPN. Experimental results demonstrate that the improved model achieves 3.5% and 1.5% higher Precision and mAP@0.5 respectively compared to the original Yolov8, while reducing FLOPs by 24% and parameter quantity by 50%. These advancements enable the proposed method to deliver superior performance in complex underground environments, particularly for detecting small and weakly textured targets, where it shows a remarkable 0.4% accuracy improvement (from 93.1% to 97.1%).

Due to the complex changes in the physical and chemical properties of rolling bearings from degradation to failure, most model-driven and data-driven methods generally suffer from insufficient accuracy and robustness in predicting the remaining useful life of rolling bearings. To address this challenge, this paper proposes a data-driven artificial neural network method, namely the CNN-LSTM bearing remaining life prediction model based on the fruit fly optimization algorithm (FOA). This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. In addition, introducing FOA enables the model to dynamically adjust the hidden layers and thresholds while optimizing the optimal path, thereby finding the best solution. This article conducts ablation experiments on the model using the acceleration life dataset of IEEE PHM 2012 rolling bearings. The experimental results show that the FOA-CNN-LSTM model proposed in this paper significantly outperforms other comparative methods in RUL prediction accuracy and stability, verifying its effectiveness and innovation in dealing with complex degradation processes. This method helps to take preventive measures before faults occur, thereby reducing economic losses and having important practical significance for predicting the remaining life of rolling bearings.

Effective control of the micro- and nanostructure of thermal barrier coatings is essential to enhance the thermal radiation performance of the coating, which helps to determine the remaining service life of the coating. This paper proposed a method to measure the radiation properties of thermal barrier coatings by terahertz nondestructive testing technique, using APS-prepared thermal barrier coatings as the object of study. Radiative properties were a comprehensive set of properties characterized by the diffuse reflectance, transmittance, and absorptance of the thermal barrier coating. The coating data in actual service were obtained by scanning electron microscopy and metallographic experiments, and the data were used as the simulation model critical value. The terahertz time-domain simulation data of coatings with different microstructural features were obtained using the finite-different time-domain (FDTD) method. In simulating the real test signals, white noise with a signal-to-noise ratio of 20 dB was added, and fast Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) were used to reduce the noise and compare their noise reduction effects. Different machine learning methods were used to build the model, including support vector machine algorithm (SVM) and k-nearest neighbor algorithm (KNN). The principal component algorithm (PCA) was used to reduce the dimensionality of terahertz time-domain data, and the SVM algorithm and KNN algorithm were optimized using the particle swarm optimization algorithm (PSO) and the ant colony optimization algorithm (ACO), respectively, to improve the robustness of the system. The K-fold cross-validation method was used to construct the model to improve the adaptability of the model. It could be clearly seen that the novel hybrid PCA-ACO-SVM model had superior prediction performance. Finally, this work proposed a novel, convenient, nondestructive, online, safe and highly accurate method for measuring the radiation performance of thermal barrier coatings, which could be used for the judgment of the service life of thermal barrier coatings.

To improve the accuracy of gear fault diagnosis and overcome the low diagnostic accuracy of the model caused by manual parameter selection, a combined diagnostic model based on time-frequency fusion features is combined with the improved global search whale optimization algorithm (GSWOA) to optimize the fault diagnosis capability of the kernel extreme learning machine (KELM). First, the time-domain and frequency-domain features of the gear fault state are extracted separately, and feature vectors are constructed through feature fusion, which overcomes the limitations of single features. Second, the GSWOA based on three strategies is used to optimize the regularization coefficient C and kernel function parameter γ of KELM, and a GSWOA-KELM fault diagnosis model is built to avoid the problem of low fault diagnosis accuracy caused by the manual selection of KELM parameters. Finally, the public dataset from Southeast University is taken to verify the performance of the proposed model by comparing it with KELM, SSA-KELM, and WOA-KELM models. The experimental results demonstrate that the improved time-frequency fusion features-based GSWOA-KELM model shows faster convergence speed and stronger global search ability. Compared with KELM, SSA-KELM, and WOA-KELM models, the performance of the proposed model has been improved by 11.33%, 8.67%, and 1.33%, respectively.

Hydrogen diffuses in the material and accumulates in dislocations, holes, etc. which will lead to deterioration of the mechanical properties of the material and affect the health state of the structural parts of hydrogen service. In this paper, a characterisation method for the degradation degree of hydrogen-induced properties of materials based on eddy current response signal is proposed. Based on the semi-infinite planar source diffusion model and hydrogen permeation model, an improved hydrogen diffusion and distribution analysis model was established, and an early hydrogen damage eddy current detection model for multilayer conductive structure materials was constructed. The evolution of material properties under the action of hydrogen was analysed, and a linear relationship between hydrogen content and conductivity was obtained. The loss of elongation was selected as the damage index and the relationship between the eddy current response signal and hydrogen embrittlement performance index is established. Experiments verify that the eddy current response signal is sensitive to the performance index and increases with the intensification of hydrogen-induced performance degradation.

To ensure the thermal stability of aero-engine blades under high temperature and harsh service environments, it is necessary to quickly and accurately evaluate the thickness of thermal barrier coatings (TBCs). In this work, it was proposed to use the terahertz nondestructive testing (NDT) technique combined with the hybrid machine learning algorithm to measure the thickness of TBCs. The finite difference time-domain (FDTD) method was used to model the optical propagation characteristics of TBC samples with different thicknesses (101–300 μm) in the frequency band. To make the terahertz time-domain signal obtained simulation more realistic, uniform white noise was added to the simulation data and wavelet denoising was conducted to mimic the real testing environment. Principal components analysis (PCA) algorithm and whale optimization algorithm (WOA) combined with an optimized Elman neural network algorithm was employed to set up the hybrid machine learning model. Finally, the hybrid thickness regression prediction model shows low error, high accuracy, and an exceptional coefficient of determination R² of 0.999. It was demonstrated that the proposed hybrid algorithm could meet the thickness evaluation requirements. Meanwhile, a novel, efficient, safe, and accurate terahertz nondestructive testing method has shown great potential in the evaluation of structural integrity of thermal barrier coatings in the near future.