Fault Detection Method Research Articles

High-resolution weld defect detection with RSU-MLP and dynamic kernel supervision

The challenges posed by high-resolution X-ray images of weld defects—including fuzzy features, multi-scale variability, small targets, and sample imbalance—create significant obstacles for accurate defect localization. Traditional deep learning methods typically emphasize global image characteristics, often neglecting local and multi-scale information, which leads to the inaccurate detection of small defects. To address this issue, we propose a novel high-resolution weld defect detection method called RSU-MLP. This method combines the RSU-MLP network with multi-scale feature extraction technology to effectively capture both local and global image features. Additionally, we design a dynamic kernel adaptive segmentation head based on a hybrid extended convolutional attention mechanism, which adaptively adjusts the receptive field size of the convolutional kernel, thereby enhancing detection capabilities for defects of varying scales. Furthermore, an in-depth supervision mechanism is introduced to monitor and learn defects at different levels of the network, leading to improved detection performance. Experimental results on real high-resolution welding datasets demonstrate that the proposed method achieves promising results in welding defect detection. Compared to traditional approaches, this method provides more accurate detection and identification of various welding defects, achieving DICE and Jaccard scores of 75.97% and 64.01%, respectively. This represents the best performance, demonstrating both robustness and generalization capabilities. Consequently, it offers a reliable automated detection method for producing high-quality welded products.

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

Defect intelligent recognition of membrane product based on deep learning

Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.

Development of automation and monitoring methods for narrow-gap hot-wire laser welding using camera images

AbstractIn this study, an image-based method was developed for hot-wire laser narrow gap welding. The welding process was monitored based on image information processed using semantic segmentation, a method of classifying images by pixel. To control the welding position, an experimental system was configured that automatically follows the welding position by recognizing the position of the welding groove from the image during welding. In monitoring weld defects, a method was developed to predict the lack of fusion occurring on the wall surface using brightness information near the wall surface. For the lack of fusion occurring at the bottom of the groove, a defect detection method was developed by monitoring the molten pool shape using semantic segmentation. Defects were generated by intentionally reducing the laser power, and the defects were monitored from images taken during processing. In the unstable state where the laser power was reduced, the shape in front of the molten pool became unstable, and the occurrence of defects was monitored by capturing the shape change. In conclusion, this research made it possible to control and monitor the welding process with a single camera.

Fatigue Strength Analysis of Rail Fastening Baseplate

Superstructural elements of a railway track experience dynamic loads varying over time. As a consequence, this is accompanied by the initiation and accumulation of fatigue damages. Defects in intermediate rail fastening are quite difficult to identify using flaw detection methods, and some can be destructed. In particular, rail fastening baseplates often fail due to insufficient fatigue strength.Purpose: To investigate the fatigue strength of rail fastening baseplate.Methodology: Development of the mathematical model of deformation based on the finite element method (FEM). Strength analysis of the baseplate using the model of a variable-thickness slab resting on a nonlinear elastic foundation. The latter is modeled by a set of rods with nonlinear deformation and parameters determined by compression tests of the baseplate. A mathematical model developed in COSMOS software for the FE analysis of the stress-strain state of the baseplate at a time-varying load from rolling equipment and assembly forces from attaching the baseplate to the sleeper and rail.Research findings: The cyclic loading parameters are obtained at dangerous points of the baseplate and its durability is analyzed at different loads with respect to unevenness of track settlement and failure of bolt connections of rail fasteners. The influence of these factors on actual loads on the rail fastener is determined. It is found that the service life of the baseplate mainly depends on the dynamic load.

A Classification and Segmentation Model for Diamond Abrasive Grains Based on Improved Swin-Unet-SAM

The detection of abrasive grain images in diamond tools serves as the foundation for assessing the overall condition of the tools, encompassing crucial aspects of diamond abrasive grains like the quantity, size, morphology, and distribution. Given the intricate background textures and reflective characteristics exhibited by diamond images, diamond detection and segmentation pose a significant challenge. Recently, numerous defect detection methods based on machine learning and deep learning have emerged. However, several issues persist, such as detection accuracy and the interference caused by intricate background textures. The present work demonstrates an efficient classification and segmentation network algorithm that combines Swin-Unet with SAM (Segment Anything Model) to alleviate the existing problems. Specifically, four embedding structures were devised to bridge the two models for iterative training. The transformer blocks within the Swin-Unet model were enhanced to facilitate classification and coarse segmentation, and the mask structure in SAM was refined to enable fine segmentation. The experimental results show that under a small sample dataset with complex background textures, the average index values of ACC (accuracy), SE (Sensitivity), and DSC (Dice Similarity Coefficient) for the classification and segmentation of diamond abrasive grains reached 98.7%, 92.5%, and 85.9%, respectively. Compared with the model before improvement, its ACC, SE and DSC increased by 1.2%, 15.9%, and 7.6%, respectively. The test results, based on four different datasets, consistently indicated that this model has excellent segmentation performance and robustness and has great application potential in the industrial field.

A fault detection method for automated industrial equipment based on multi attribute decision fusion in knowledge graph

A fault detection method for automated industrial equipment based on multi attribute decision fusion in knowledge graph

AI-Augmented Fault Detection and Diagnosis in VLSI Circuits: A Step toward Intelligent Chip Design

Fault detection and diagnosis are critical challenges in Very Large Scale Integration (VLSI) circuits, where even minor defects can cause significant performance degradation or system failure. Traditional fault detection methods often struggle with scalability and real-time analysis as circuits grow increasingly complex. This paper proposes an AI-augmented framework that leverages machine learning algorithms and neural networks to enhance the fault detection process in VLSI circuits. The proposed model not only identifies and classifies faults with high accuracy but also provides root-cause diagnostics, significantly reducing testing time and maintenance costs. Simulation results demonstrate the effectiveness of the AI-based approach in comparison with conventional techniques, showcasing improved fault coverage, faster detection, and scalability for modern chip designs. This study serves as a step toward the integration of intelligent diagnostics into chip manufacturing, paving the way for more robust and self-healing electronic systems.

Defect Detection of Polyethylene Gas Pipeline Based on Convolutional Neural Networks and Image Processing

Abstract In this paper, a method based on image recognition was proposed to detect the defects of polyethylene (PE) gas pipeline, especially the deformation due to the indentation. First, the pipeline -detection VGG (PD-VGG) model was established based on the convolutional neural network (CNN), and appropriate model parameters were optimized through model training. The defect recognition rate of the improved model can reach 94.76%. Following, the weighted average graying algorithm was used to separate the defects characterized by deformation. Then, an improved gamma correction algorithm was applied to achieve image enhancement, and the interference of impurities adhered on intersurface of pipeline was also removed by using multilayer filters. The edge detection of the defect image was completed by using the Canny operator, and following the screening between the target contour and the interference contour by using top-contour. Finally, the algorithm for minimum outer rectangle algorithm was used to fit the defect contour, and the eigenvalues of deformation defects were extracted. The results indicate that the above defect detection method can better extract the deformation contour of the dented pipeline. The high agreement with the experimental results provides a basis for the research of effectively recognizing whether the pipeline has undergone ductile failure only through profile detection of defects.

Dynamic model-based intelligent fault diagnosis method for fault detection in a rod fastening rotor

A complete fault sample database is of great significance for the intelligent fault diagnosis method of rod fastening rotor. However, the lack of fault samples makes the fault diagnosis results unbelievable. To solve this issue, the dynamic model-based intelligent fault diagnosis method is established for a rod fastening rotor, and the fault sample database is enriched by numerical simulations. First, the lumped parameter model of the rod fastening rotor system is constructed and further updated using Euclidean Distance between measurement and numerical simulation of the intact system. Second, mathematical models of various fault types are incorporate into the updated model to obtain numerical simulation fault samples. Thirdly, the utilization of numerical simulation fault samples is severed as training data to the artificial intelligence (AI) models and the unknown measurement test samples will be finally classified. In this paper, Support Vector Machine, Random Forest, Bayesian Network and Decision Tree are selected as the typical AI models. Subsequently, the feasibility of classification is validated by the test bench of the rod fastening rotor system, and the problem of insufficient fault samples can be solved.

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data.

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model's detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

Quality-related fault detection for dynamic process based on quality-driven long short-term memory network and autoencoder

Fault detection consistently plays a crucial role in industrial dynamic processes as it enables timely prevention of production losses. However, since industrial dynamic processes become increasingly coupled and complex, they introduce uneven dynamics within the collected data, posing significant challenges in effectively extracting dynamic features. In addition, it is a tricky business to distinguish whether the fault that occurs is quality-related or not, resulting in unnecessary repairing and large losses. In order to deal with these issues, this paper comes up with a novel fault detection method based on quality-driven long short-term memory and autoencoder (QLSTM-AE). Specifically, an LSTM network is initially employed to extract dynamic features, while quality variables are simultaneously incorporated in parallel to capture quality-related features. Then, a fault detection strategy based on reconstruction error statistic squared prediction error (SPE) and the quality monitoring statistic Hotelling T2 (H2) is designed, which can distinguish various types of faults to realize accurate monitoring for dynamic processes. Finally, several experiments conducted on numerical simulations and the Tennessee Eastman (TE) benchmark process demonstrate the reliability and effectiveness of the proposed QLSTM-AE method, which indicates it has higher accuracy and can separate different faults efficiently compared to some state-of-the-art methods.

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD (Concrete Defect Detection) combining convolutional neural network and transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a bot-transformer module that can effectively extract the global information of defect features is proposed to improve the network’s ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a convolutional triplet attention module is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.

Semiconductor Wafer Defect Detection Based on Machine Learning

Semiconductor wafers are widely used in integrated circuit chips. Because of the complex production process, it is easy to cause various defects. Therefore, the defect detection of semiconductor wafers is an important means to ensure their yield and productivity. This paper mainly expounds on the detection method of wafer defects combined with the machine vision algorithm, including the CNN model, and the classification of the learning-based methods. Current problems and future prospects for total crystalline circle defect detection are discussed in the end.

Surface defect inspection of industrial products with object detection deep networks: a systematic review

One of the focal points in industrial product defect detection lies in the utilization of deep learning-based object detection algorithms. With the continuous introduction of these algorithms and their refined models, notable achievements have been attained. However, challenges persist in industrial settings, such as substantial variations in defect scales, the delicate balance between accuracy and speed, and the detection of small objects. Various methods have been proposed to address these challenges and propel the advancement of defect detection. To comprehensively review the latest developments in deep learning-based industrial product defect detection algorithms and foster further progress, this paper encompasses typical datasets and evaluation metrics used in industrial product defect detection, traces the development history of supervised one-stage and two-stage object detection algorithm-based and unsupervised algorithm-based industrial defect detection methods, discusses major challenges, and outlines future directions. It highlights the potential for further improving the accuracy, speed, and reliability of defect detection systems in industrial applications.

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

Neural network-enhanced internal leakage analysis for efficient fault detection in heavy machinery hydraulic actuator cylinders

This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Cyclic shift short-distance attention for defect detection of fine etching mesh

Abstract Based on the deficiency of the existing Swin Transformer in the detection of surface defects in metal etched grids, i.e. the isolation of information transmission between different windows, and the limitations of image matrix size and network structure,which make it not suitable for long sequence data processing and dense prediction scenarios, a defect detection method for metal etching nets based on Cyclic shift Long Short Distance Attention (C-LSDA) is proposed. In the proposed method, cross-scale features are used to capture long and short distance relationships,and enhance the feature transfer between windows. Tracking position bias (TPB) is also used to improve relative position bias (RPB), in order to help the model to better understand the spatial relationships in the input data. In addition, the image is downsampled at various scales, so that the proposed method can be suitable for long sequence data processing and dense prediction scenarios. In order to improve the detection accuracy of small targets. a multi-feature fusion pyramid network (MFFPN) for feature fusion is also proposed. The experimental results show that the proposed method is significantly superior to the traditional CNN recognition method and the transformer recognition method. The proposed methodhas good identification accuracy and low computational cost.

A Hybrid Multimodel-Based Condition Monitoring and Sensor Fault Detection Method for Aero Gas Turbine

A Hybrid Multimodel-Based Condition Monitoring and Sensor Fault Detection Method for Aero Gas Turbine