Accurate Fault Detection Research Articles

Artificial intelligence powered real-time quality monitoring for additive manufacturing in construction

In the manufacturing process of 3D Concrete Printing (3DCP), defects and anomalies have a significant impact on both the success rate and the quality of the final products, underscoring the need for real-time monitoring. Currently, monitoring is primarily based on manual observation and existing automated methods are limited in real-time performance and accuracy. This study introduced a real-time and highly accurate defect detection and measurement system for using deep learning (DL) and computer vision (CV) techniques. A range of improvement methods were applied in YOLOv7, showing better capacities of accuracy and speed for detecting defects in 3DCP than current cutting-edge detectors such as YOLOv8. Notably, the virtual high-fidelity data were produced by DL based data augmentation strategy and their effects were assessed. Replacing real data as the training dataset, the generated virtual data were used in the models to improve measurement accuracy. Applying the proposed method, the comprehensive insights into 3DCP defects were obtained. Consequently, the relationship formula between defect frequency and printer parameters was investigated by the proposed method, guiding operators in effectively controlling printer parameters and preventing breakpoint defects during the printing process.

Fault detection based on a residual intervals multigenerator for nonlinear systems

This paper is devoted to sensor fault detection for nonlinear systems using an interval multiobserver based on uncoupled multimodel. This proposed interval multiobserver is used to detect the faults through residual intervals multigenerator. The [Formula: see text] performance technique is introduced to improve the accuracy of the sensor fault detection. The stability analysis of the fault detection system is carried out using Lyapunov approach under LMI(s) formulation. A numerical simulation example is used to highlight the efficiency of the proposed residual intervals multigenerator and the robustness of the fault detection strategy.

Magnetic focusing sensor and its characterizations of defect non-destructive testing for ferromagnetic steel plate

The fast development in precision manufacturing and product quality inspection of ferromagnetic material desperately demands that the defect determining the performance, service life and reliability of the mechanical part should be accurately sensed, detected and evaluated. However, conventional defect detection methods and related magnetic induction sensors are usually characterized with low accuracy in small defect detection, which makes the non-destructive testing (NDT) for ferromagnetic material full of challenges. Thus, a new magnetic focusing sensor is proposed and designed for the precision defect detection of ferromagnetic steel plate here. First, the theoretical principle analysis of the magnetic focusing sensor is introduced, and the preliminary finite element modeling (FEM) is conducted to demonstrate the feasibility of the magnetic focusing sensor in tiny defect inspection. Then, both the static and coupling dynamic characterizations of the magnetic focusing sensor are investigated using the FEM method. The optimal structural parameters of the magnetic focusing (MF) sensor in static conditions are summarized. Furthermore, the effects of the dynamic movement of the MF sensor on the multi-physical fields of the magnetization field, electric field, electromagnetic attraction force field and thermal field are all revealed. Finally, experiments regarding the performance evaluation and comparison between the MF sensor and other two magnetic flux leakage (MFL) sensors are conducted, and the prominent capabilities of accurate and precise defect detection for the MF sensor is demonstrated through the defect detection with different sizes. The effectiveness and feasibility of the MF sensor in detecting tiny defects on ferromagnetic object, as well as its great potential for practical application is therefore validated. Additionally, the main conclusions, advantages of the MF sensor are summarized and the future work is discussed.

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

Reactive UAV-based automatic tunnel surface defect inspection with a field test

This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control strategies with a grid scanning pattern to capture high-quality image data from multiple views and angles. The image data is then used to generate a 3D point cloud model of the tunnel surface for structural condition assessment. The method is tested in a field experiment in a railway tunnel in Ireland, and the results show that it can achieve stable navigation, high-resolution reconstruction, and accurate defect detection. The paper discusses the advantages and limitations of the method, and suggests improving the control/navigation intelligence, data quality, and defect analysis as the future research directions.

Research on Surface Defect Detection of Strip Steel Based on Improved YOLOv7.

Surface defect detection of strip steel is an important guarantee for improving the production quality of strip steel. However, due to the diverse types, scales, and texture structures of surface defects on strip steel, as well as the irregular distribution of defects, it is difficult to achieve rapid and accurate detection of strip steel surface defects with existing methods. This article proposes a real-time and high-precision surface defect detection algorithm for strip steel based on YOLOv7. Firstly, Partial Conv is used to replace the conventional convolution blocks of the backbone network to reduce the size of the network model and improve the speed of detection; Secondly, The CA attention mechanism module is added to the ELAN module to enhance the ability of the network to extract detect features and improve the effectiveness of detect detection in complex environments; Finally, The SPD convolution module is introduced at the output end to improve the detection performance of small targets with surface defects on steel. The experimental results on the NEU-DET dataset indicate that the mean average accuracy (mAP@IoU = 0.5) is 80.4%, which is 4.0% higher than the baseline network. The number of parameters is reduced by 8.9%, and the computational load is reduced by 21.9% (GFLOPs). The detection speed reaches 90.9 FPS, which can well meet the requirements of real-time detection.

Residual learning-based robotic image analysis model for low-voltage distributed photovoltaic fault identification and positioning.

With the fast development of large-scale Photovoltaic (PV) plants, the automatic PV fault identification and positioning have become an important task for the PV intelligent systems, aiming to guarantee the safety, reliability, and productivity of large-scale PV plants. In this paper, we propose a residual learning-based robotic (UAV) image analysis model for low-voltage distributed PV fault identification and positioning. In our target scenario, the unmanned aerial vehicles (UAVs) are deployed to acquire moving images of low-voltage distributed PV power plants. To get desired robustness and accuracy of PV image detection, we integrate residual learning with attention mechanism into the UAV image analysis model based on you only look once v4 (YOLOv4) network. Then, we design the sophisticated multi-scale spatial pyramid fusion and use it to optimize the YOLOv4 network for the nuanced task of fault localization within PV arrays, where the Complete-IOU loss is incorporated in the predictive modeling phase, significantly enhancing the accuracy and efficiency of fault detection. A series of experimental comparisons in terms of the accuracy of fault positioning are conducted, and the experimental results verify the feasibility and effectiveness of the proposed model in dealing with the safety and reliability maintenance of low-voltage distributed PV systems.

Automatic Defect Detection of Jet Engine Turbine and Compressor Blade Surface Coatings Using a Deep Learning-Based Algorithm

The application of additive manufacturing (AM) in the aerospace industry has led to the production of very complex parts like jet engine components, including turbine and compressor blades, that are difficult to manufacture using any other conventional manufacturing process but can be manufactured using the AM process. However, defects like nicks, surface irregularities, and edge imperfections can arise during the production process, potentivally affecting the operational integrity and safety of jet engines. Aiming at the problems of poor accuracy and below-standard efficiency in existing methodologies, this study introduces a deep learning approach using the You Only Look Once version 8 (YOLOv8) algorithm to detect surface, nick, and edge defects on jet engine turbine and compressor blades. The proposed method achieves high accuracy and speed, making it a practical solution for detecting surface defects in AM turbine and compressor blade specimens, particularly in the context of quality control and surface treatment processes in AM. The experimental findings confirmed that, in comparison to earlier automatic defect recognition procedures, the YOLOv8 model effectively detected nicks, edge defects, and surface defects in the turbine and compressor blade dataset, attaining an elevated level of accuracy in defect detection, reaching up to 99.5% in just 280 s.

Bearing fault detection technology for automated machinery based on acoustic analysis

Traditional fault detection methods in acoustic signal feature extraction of rolling bearings often make the signal denoising process complex due to low signal-to-noise ratio and weak fault features, making this method difficult to meet real-time requirements. Therefore, a fault detection model based on Fast-Renoriented SIFT feature extraction is proposed, which can quickly extract a large number of features from the original signal without the need for noise reduction processing and can effectively improve the efficiency and accuracy of fault detection. At the same time, to adapt to the fault detection of rolling bearings under multiple working conditions, this study also proposes an adaptive extended word bag model that combines local kurtosis and local 2-dimensional information entropy features, improving the adaptability and flexibility of the new model. It obtained a 100% overall recognition rate and a fault detection time of no more than 0.5 seconds in a 5-fold cross-validation experiment, verifying the excellent recognition accuracy, stability, and operational efficiency of the detection model. Its recognition accuracy in the multi-working condition rolling bearing fault detection experiment was above 97%, which was improved by about 21.25% compared to the traditional word bag model and had significant advantages in fault recognition accuracy and computational efficiency.

Research on Surface Defect Detection Method of Photovoltaic Power Generation Panels——Comparative Analysis of Detecting Model Accuracy

INTRODUCTION: Research on intelligent defect detection technology using machine vision was conducted to address the challenging problem of detecting and localizing PV defects in photovoltaic power generation system operation and maintenance. OBJECTIVES: The aim is to improve the accuracy of PV defect detection and enhance the operation and maintenance efficiency of PV power plants. METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection, and deep learning algorithm based detection are discussed and compared, and analyzed respectively. It is finally concluded that the deep learning based detection is more efficient in comparison. Then further analysis and simulation experiments are done through several detection algorithms based on deep learning. RESULTS: The experiment yields a high accuracy of the detection model based on the Faster-RCNN algorithm. Its mAP value reaches 92.6%. The detection model based on the YOLOv5 algorithm reaches a mAP value of 91.4%. But its speed is as much as 7 times faster than the model based on the Faster-RCNN algorithm. CONCLUSION: Comprehensive speed and accuracy index. Combining the needs of PV defect detection in the operation and maintenance of PV power generation systems with the results of simulation experiments. It is concluded that the detection model based on the YOLOv5 algorithm can provide better detection capability. Modeling with this algorithm is more suitable for PV defect detection.

Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.
 OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.
 METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.
 RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.
 CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

Synthetic data augmentation for high-resolution X-ray welding defect detection and classification based on a small number of real samples

Deep learning has become the dominant technology in most computer vision tasks. These methods often rely on a large number of labeled sample datasets for training, and in the field of non-destructive testing of welds in industrial manufacturing, weld images with defects are very scarce, and it is still a challenging challenge to construct high-resolution weld defect datasets that meet the requirements. To overcome this limitation, a new data augmentation method for high-resolution X-ray welding defect classification and synthesis based on a small number of real samples is proposed to realize the data augmentation of industrial nondestructive inspection X-ray film defect images. Firstly, to overcome the scarcity of the weld X-ray defect classification dataset, the weld X-ray defect classification dataset (Weld Defect Classification, WDC) is constructed. Secondly, the performance of 16 common deep classification models on WDC datasets is explored. Then, the images of the real local welding defects and the non-defective weld area are fused at random locations, and two data augmentation modes, (Single Image Single Defect, SISD) and (Single Image Multi Defects, SIMD), can generate defect files and annotation files (Visual Object Classes, VOC) at the same time, which can save a lot of time for manual marking. Finally, compared with the traditional data augmentation method, the proposed method can effectively improve the accuracy of defect detection and generalization, the mAP (Mean Average Precision, mAP) @0.5 of YOLOV8X (You Only Look Once, YOLO) and YOLOV5.6.1X is 66.6% and 72.8%, which provides an effective solution for data sample generation in the industrial field.

Comparative assessment of fault detection methods in transmission line during power swing: analyzing single and hybrid networks for the southern Kerala grid

The blocking function of power oscillation is a highly effective feature found in distance relays. Its purpose is to prevent unintentional tripping of transmission lines. It is crucial to accurately differentiate between faults and power oscillations to minimise the occurrence of widespread power outages and monetary losses. Therefore, the detection and classification of faults arising from power swings are critical challenges in ensuring the smooth operation and overall health of transmission lines, particularly in extra-high-voltage (EHV) and ultra-high-voltage (UHV) power systems. Modern methods rely on machine learning methods which are in their infancy. The paper proposes to identify various fault conditions in transmission lines during power swing by using a hybrid Neural Network architecture. In this research, different designs such as 2-D convolution neural network (2D-CNN), 1-D convolution neural network (1D-CNN), long short-term memory (LSTM), and CNN-LSTM hybrid networks are investigated to understand the effect of various deep learning approaches. Along with the southern Kerala grid, the standard 9-bus system is selected as a test case to examine various fault circumstances of the system during power swing. Multiple tests were conducted to determine the optimal deep learning architecture, including model parameters and configurations, for accurate fault detection. For the 9 bus system, the 1D CNN network performs better with an accuracy of 98.70%, and for the Kerala grid, both networks are competitive, but the CNN-LSTM Hybrid method slightly outperforms the 1D CNN with an accuracy of 92.40%.

Pixel-wise crack defect segmentation with dual-encoder fusion network

Automatic crack defect detection plays an important role in early road maintenance. However, the crack defect detection accuracy is seriously affected by some challenging factors, such as complex background, class imbalance issue, poor texture, etc. In this paper, a deep crack defect segmentation network with dual-path encoding and hierarchical fusion schemes, named DEHF-Net, is presented to accurately and effectively segment the crack defects generated in crack images. In order to capture rich feature information of crack defects, a dual-path encoder unit is built to extract the spatial information and context information from crack images simultaneously. On the basis, to strengthen the features extracted from two encoding paths, an attention fusion (AF) block and feature enhancement (FE) block are proposed for effective feature learning to achieve information complementarity of dual-branch encoding paths. Meanwhile, to alleviate the problem of semantic gaps arising from large information differences between the encoding path and decoding path, a residual refinement unit (RRU) is also introduced to enable effective refinement of edges and details. Finally, to make the segmentation results more significant on multi-scale cracks, a weighted hierarchical fusion (WHF) block is introduced in the decoding stage to fuse the shallow texture information with the deep semantic information more effectively. Experimental results show the proposed model has obtained an excellent performance comparison with advanced methods.

Advanced wind turbine blade inspection with hyperspectral imaging and 3D convolutional neural networks for damage detection

In the context of global efforts to mitigate climate change by pursuing sustainable energy sources, wind energy has emerged as a critical contributor. However, the wind energy industry faces substantial challenges in maintaining and preserving the integrity of wind turbine blades. Timely and accurate detection and classification of blade faults, encompassing issues such as cracks, erosion, and ice buildup, are imperative to uphold wind turbines' ongoing efficiency and safety. This study introduces an inventive approach that amalgamates hyperspectral imaging and 3D Convolutional Neural Networks (CNNs) to augment the precision and efficiency of wind turbine blade fault detection and classification. Hyperspectral imaging is harnessed to capture comprehensive spectral information from blade surfaces, facilitating exact fault identification. The process is streamlined through Incremental Principal Component Analysis (IPCA), reducing data dimensions while maintaining integrity. The 3D CNN model demonstrates remarkable performance, achieving high accuracy in detecting all fault categories in full-band hyperspectral images. The model retains high accuracy even with dimensionality reduction to 20 spectral bands. The reduced processing time of the 20-band image enhances the practicality of real-world applications, thereby reducing downtime and maintenance expenditures. This research represents a significant advancement in wind turbine blade inspection, contributing to the sustainability and dependability of wind energy systems and furthering the cause of a cleaner and more sustainable energy future as part of the broader fight against climate change.

Bayesian hierarchical hyper-Laplacian priors for high-resolution defect imaging in pipe structures

The sparse defect imaging using guided waves for pipe structures has attracted a lot of attention due to its intuitive way of defect localization. However, there exist few attempts to employ the lp norm (p < 1) in sparse imaging to improve imaging performance. In this study, a new sparse imaging method based on Bayesian hierarchical hyper-Laplacian prior is proposed for pipe defect detection. A hyper-Laplacian prior is considered to obtain a sparser resolution, which aims at improving the imaging performance including defect imaging resolution and defect detection accuracy. A solid theoretical framework is constructed and derived to represent the hyper-Laplacian prior and to adaptively estimate all model parameters. Meanwhile, a two-step discretizing strategy is designed to reduce the computational cost. The experimental results demonstrate the superiority of the proposed method.

Uncertainty Quantification of Data-driven Quality Prediction Model For Realizing the Active Sampling Inspection of Mechanical Properties in Steel Production

Pre-production quality defect inspection is a crucial step in industrial manufacturing, and many traditional inspection strategies suffer from inefficiency issues. This is especially true for tasks such as mechanical performance testing of steel products, which involve time-consuming processes like offline sampling, specimen preparation, and testing. The inspection volume significantly impacts the production cycle, inventory, yield, and labor costs. Constructing a data-driven model for predicting product quality and implementing proactive sampling inspection based on the prediction results is an appealing solution. However, the prediction uncertainty of data-driven models poses a challenging problem that needs to be addressed. This paper proposes an active quality inspection approach for steel products based on the uncertainty quantification in the predictive model for mechanical performance. The objective is to reduce both the sampling frequency and the omission rate on the production site. First, an ensemble model based on improved lower and upper bound estimation is established for interval prediction of mechanical performance. The uncertainty of the specific value prediction model is quantitatively estimated using interval probability distributions. Then, a predictive model for the mechanical performance failure probability is built based on the prediction interval size and probability distribution. By determining an appropriate probability threshold, the trade-off between prediction accuracy and defect detection accuracy (recall rate) is balanced, enabling the establishment of an active sampling strategy. Finally, this functionality is integrated into the manufacturing execution system of a steel factory, realizing a mechanical performance inspection approach based on proactive sampling. The proposed approach is validated using real production datasets. When the probability threshold is set to 30%, the prediction accuracy and recall rate for failure mechanical performance samples are 75% and 100%, respectively. Meanwhile, the sampling rate is only 5.33%, while controlling the risk of omission. This represents a 50% reduction in sampling rate compared to the inspection rules commonly used in actual production. The overall efficiency of product quality inspection is improved, and inspection costs are reduced.

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

AbstractOf late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

Advancing Crack Detection Using Deep Learning Solutions for Automated Inspection of Metallic Surfaces

The deep Convolutional Neural Network (CNN) architecture used in this research study provides a proof of concept for crack detection on the metallic surface of a hex nut. The goal is to create an automated receiving inspection process to supplement human inspections conducted on-site. Conventional image processing techniques (IPTs) have been extensively used for mechanical infrastructure fault detection. These techniques focus on image modification to extract typical features, such as surface fractures in materials like steel and concrete. However, obstacles presented by a variety of real-world variables, such as changes in lighting and shadows, make it difficult to use IPTs. Our suggested vision-based method employs a deep learning CNN to overcome these difficulties, eliminating the need to explicitly compare fault features. CNNs are more resilient to shifting real-world situations than IPTs since they are naturally trained to identify characteristics in images. Following training on a dataset of 1081 images with dimensions of 256 x 256 pixels, the VGG16 CNN architecture achieved an impressive accuracy of around 94.17%. Additional CNN architectures, including ResNet, MobileNet, AlexNet, and LeNet-5, are employed to assess and compare fault detection accuracies in order to select the most appropriate architecture for the model. To evaluate the robustness and flexibility of the suggested method in various situations, we conducted tests with 206 images from an alternative structure that was not part of the training dataset. These images depicted a range of circumstances, such as intense light patches and tiny fissures. The outcomes showed that our proposed method outperforms current approaches, highlighting its usefulness in practical situations involving the identification of metallic defects.

Lightweight detection network for bridge defects based on model pruning and knowledge distillation

Deep learning techniques improve the detection performance of objects by expanding the depth of network and increasing the complexity of model structure, however, this makes it difficult to deploy as an application on embedded devices. In this study a lightweight model was put forward to better identify bridge defects combining with two advanced feature fusion modules to increase the multi-scale information perception and extraction capability of the network. Sparse training and slimming pruning were used to remove redundant parameters. Then, fine-tuning and knowledge distillation (KD) enabled the network to recover its detection accuracy and robustness. Furthermore, the unsupervised learning CycleGAN was used to enhance the generalization performance of the dataset and improve the detection accuracy. The experimental results show that compared with the original model, the precision rate, mAP@ 0.5 and detection speed (FPS) is increased by 8.07%, 6.15% and 85.06%, respectively. Moreover, the model volume of proposed network is 43.04% of the original ones. The results indicates that the proposed network can be deployed on devices with limited storage to achieve accurate and real-time detection of bridge defects. These findings represent a major step forward in the automation of detection and quantification of structural defects.