Non-intrusive detection of concrete pipe defects using active impact excitation and fiber-optic distributed acoustic sensing

The detection of defects in pipes is of very importance to the oil, chemistry, gas industry etc. The guided wave method that detects refection waves from defects is developed and proves to be effective. The disadvantages such as low speed and high cost in current non-destructive testing for defects in pipes, the influences of varying exciting frequency on the detection of defects are numerically simulated and analyzed both in straight and curved pipes. And the results suggest that, under the condition of certain wall thickness of pipes, the testing sensitivity increases at first as the exciting frequency increases along, and then it begins to decrease when the frequency arrives at a specific value. It is also concluded that the error in determining the defect locations increases too as the exciting frequency increases. These results have instructive significance on realizing long distance and rapid non-destructive testing for longitudinal ultrasonic guided waves in pipes. At last, the structure of ring transducer that can excite guided waves is also presented in the paper.

On-power detection of wall-thinned defects using lock-in infrared thermography

Detection and classification of pipe defects based on pipe-extended feature pyramid network

A combination of finite element method simulations and experiments using a non-contact ultrasonic scanning system were used to study the near-field interactions of ultrasonic waves incident on surface-breaking defects in both flat sheets and pipes. Enhancements in the frequency content of Lamb waves in sheets arising from interactions between incident waves and waves reflected from defects propagating into the sheet from the opposite side to that on which the inspection was performed were studied, and used to position the defects and estimate their severity. Similar enhancements were shown to arise in the near-field of defects in pipes, for defects that propagate from both the internal and external pipe surfaces. Enhancements of the ultrasonic waves were shown to be capable of positioning defects in pipes and estimating their severity.

Defects in underground pipeline images are indicative of the condition of buried infrastructures like sewers and water mains. This paper entitled automated assessment Tool for the depth of pipe deterioration presents a three step method which is a simple, robust and efficient one to detect defects in the underground concrete pipes. It identifies and extracts defect-like structures from pipe images whose contrast has been enhanced. We propose to use segmentation and feature extraction using structural elements. The main objective behind using this tool is to find the dimensions of the defect such as the length, width and depth and also the type of defect. The detection of defects in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal operators. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousands of miles of pipeline because of fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the defects in underground pipes.

Analytical model and optimal focal position selection for oblique point-focusing shear horizontal guided wave EMAT

This paper aims to provide an overview of the experimental and simulation works focused on the detection, localisation and assessment of various defects in pipes by applying fast-screening guided ultrasonic wave techniques that have been used in the oil and gas industries over the past 20 years. Major emphasis is placed on limitations, capabilities, defect detection in coated buried pipes under pressure and corrosion monitoring using different commercial guided wave (GW) systems, approaches to simulation techniques such as the finite element method (FEM), wave mode selection, excitation and collection, GW attenuation, signal processing and different types of GW transducers. The effects of defect parameters on reflection coefficients are also discussed in terms of different simulation studies and experimental verifications.

This paper deals with the detection of circumferential cracks of different areas in elbow pipes using the guided waves in mode of L(0,2) which are excited by a piezoelectric transducer. The experimental results show that the circumferential cracks can be located in axial position for bend straight side by the reflected echo of defects in the pipes. The detection sensitivity depends on the location of the cracks in bend areas of the pipes. It is easier to detect cracks in the extradose of elbow while harder to detect those in the other locations of the bend. There is a relationship between the detection sensitivity and the frequency of guided waves. The signal-to-noise ratio when detecting the crack before and beyond elbows is highest for the frequency ranges from 120kHz to 130kHz; the crack on the intradose of elbow can be inspected more effectively when using frequencies near 80kHz. Therefore, the combination of high-frequency and low-frequency is used to inspect elbows. The propagation behaviors in elbows are investigated by using numerical simulation. The results of simulation intuitively explain the experimental phenomenon.

Regular inspection of urban drainage pipes can effectively maintain the reliable operation of the drainage system and the production safety of residents. Aiming at the shortcomings of the CCTV inspection method used in the drainage pipe defect detection task, a PDS-YOLO algorithm that can be deployed in the pipe defect detection system is proposed to overcome the problems of inefficiency of manual inspection and the possibility of errors and omissions. First, the C2f-PCN module was introduced to decrease the model sophistication and decrease the model weight file size. Second, to enhance the model’s capability in detecting pipe defect edges, we incorporate the SPDSC structure within the neck network. Introducing a hybrid local channel MLCA attention mechanism and Wise-IoU loss function based on a dynamic focusing mechanism, the model improves the precision of segmentation without adding extra computational cost, and enhances the extraction and expression of pipeline defect features in the model. The experimental outcomes indicate that the mAP, F1-score, precision, and recall of the PDS-YOLO algorithm are improved by 3.4%, 4%, 4.8%, and 4.0%, respectively, compared to the original algorithm. Additionally, the model achieves a reduction in both the model’s parameter and GFLOPs by 8.6% and 12.3%, respectively. It saves computational resources while improving the detection accuracy, and provides a more lightweight model for the defect detection system with tight computing power. Finally, the PDS-YOLOv8n model is deployed to the NVIDIA Jetson Nano, the central console of the mobile embedded system, and the weight files are optimized using TensorRT. The test results show that the velocity of the model’s inference capabilities in the embedded device is improved from 5.4 FPS to 19.3 FPS, which can basically satisfy the requirements of real-time pipeline defect detection assignments in mobile scenarios.

A framework for synthetic image generation and augmentation for improving automatic sewer pipe defect detection

Quantitative detection of lamination defect in thin-walled metallic pipe by using circumferential Lamb waves based on wavenumber analysis method

The magnetic flux leakage (MFL) method has been widely utilized for steel pipe inspection, demonstrating its effectiveness in examining continuous ferromagnetic structures. However, MFL signals are often contaminated by various noise sources in field environments, complicating defect detection and analysis. To address this challenge, this study proposes an advanced signal processing method combining Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and a multi-feature-based Intrinsic Mode Function (IMF) selection strategy. Key features such as energy entropy, permutation entropy, and harmonic ratio were used to evaluate IMFs, with their relative importance optimized using a Genetic Algorithm (GA). Selected IMFs were further refined through Wavelet Threshold Denoising (WTD) to suppress residual noise and enhance signal fidelity. The proposed method was experimentally validated using a portable MFL sensor device designed for high usability in field applications. Achieving a signal-to-noise ratio (SNR) of 35.52 dB and a correlation coefficient (CC) of 0.94, the method demonstrated precise detection of small-scale defects in steel pipes, even under significant noise interference. These results highlight the method’s ability to enhance defect detection accuracy by suppressing noise while preserving critical defect-related signals, paving the way for more efficient and reliable steel pipe inspections in real-site applications.

The motion visualization in a structural component was studied for defect detection. Elastic motions were excited by hammer impacts at multiple points and received by an accelerometer at a fixed point. Reciprocity in elastodynamics is only valid under certain conditions. Its validity under given experimental conditions was derived from the elastodynamic reciprocity theorem. Based on this, the dynamic motion of the structural component was obtained for fixed-point excitation from measurements performed using multipoint excitations. In the visualized eigenmodes, significant additional deformation was observed at the wall thinning inserted as an artificial defect. To prevent the dependence of defect detection on its position within the mode shape, another approach was proposed based on the extraction of guided wave modes immediately after impact excitation. It is shown that this maximum intensity projection method works well in detecting defects.

There are many kinds of defects in pipes, which are difficult to detect with a low degree of automation. In this work, a novel omnidirectional vision inspection system for detection of the morphology defects is presented. An active stereo omnidirectional vision sensor is designed to obtain the texture and depth information of the inner wall of the pipeline in real time. The camera motion is estimated and the space location information of the laser points are calculated accordingly. Then, the faster region proposal convolutional neural network (Faster R‐CNN) is applied to train a detection network on their image database of pipe defects. Experimental results demonstrate that system can measure and reconstruct the 3D space of pipe with high quality and the retrained Faster R‐CNN achieves fine detection results in terms of both speed and accuracy.

In-line detection of defects in steel pipes using flexible GMR sensor array