Automatic Fault Research Articles

An X-Ray-Based Multiexpert Inspection Method for Automatic Welding Defect Assessment in Intelligent Pipeline Systems

An X-Ray-Based Multiexpert Inspection Method for Automatic Welding Defect Assessment in Intelligent Pipeline Systems

Research and optimization of YOLO-based method for automatic pavement defect detection

<abstract> <p>According to the latest statistics at the end of 2022, the total length of highways in China has reached 5.3548 million kilometers, with a maintenance mileage of 5.3503 million kilometers, accounting for 99.9% of the total maintenance coverage. Relying on inefficient manual pavement detection methods is difficult to meet the needs of large-scale detection. To tackle this issue, experiments were conducted to explore deep learning-based intelligent identification models, leveraging pavement distress data as the fundamental basis. The dataset encompasses pavement micro-cracks, which hold particular significance for the purpose of pavement preventive maintenance. The two-stage model Faster R-CNN achieved a mean average precision (mAP) of 0.938, which surpassed the one-stage object detection algorithms YOLOv5 (mAP: 0.91) and YOLOv7 (mAP: 0.932). To balance model weight and detection performance, this study proposes a YOLO-based optimization method on the basis of YOLOv5. This method achieves comparable detection performance (mAP: 0.93) to that of two-stage detectors, while exhibiting only a minimal increase in the number of parameters. Overall, the two-stage model demonstrated excellent detection performance when using a residual network (ResNet) as the backbone, whereas the YOLO algorithm of the one-stage detection model proved to be more suitable for practical engineering applications.</p> </abstract>

Machine Learning-Driven Maintenance Order Generation in Assembly Lines

The automatic generation of maintenance orders facilitates the prompt detection and root cause analysis of deviations or failures in the assembly process. The aim of this project is to use supervised learning models to recognise deviations in the throughput times of a fully automated assembly process carried out by robots. The model identifies errors, categorises their causes and transmits the information back to the Enterprise Resource Planning (ERP) system. The data collected comes from a development and test assembly station with an industrial robot. The data set from the assembly station was expanded using agent-based simulation in order to train the four models Support Vector Machine, K-Neares Neighbour, Naive Bayes and Decision Tree. The SVM model proved to be the most suitable model for automatic fault detection with an accuracy of 99.51 %. The model was integrated into the assembly station and an algorithm was developed to automatically generate maintenance messages to transmit the failure code to the ERP system.

A Hybrid Bearing Prognostic Method With Fault Diagnosis and Model Fusion

Accurate bearing fault diagnosis and prognosis (FDP) is critical for optimal maintenance schedules, safety and reliability. The existing methods face some problems and challenges in detecting the starting time for prognosis and using a single model to describe fault dynamics. With these motivations, this paper presents a hybrid bearing FDP framework with fault detection and automatic fault model selection. In the proposed approach, convolutional neural network is used to detect fault and select the appropriate fault dynamic model. To improve the performance, power spectrum of vibration signals are fused with operating conditions to built information maps for fault detection and model selection. After a fault is detected, a Bayesian method is triggered to estimate the fault state and predict the remaining useful life. In the prognosis, Dempster-Shafer theory is employed to fuse prediction results from different models if necessary. The proposed approach is verified with bearings under different operating conditions. Experimental results and comparison studies verify the high accuracy and efficiency of the proposed method.

Online Crack Detection of Highly Curved Cylindrical Coils

Many vision-based methods for defect detection have been proposed to replace inefficient manual inspection. However, due to the complex shape and high degree of freedom required to manipulate individual coils, an automatic optical inspection (AOI) method for cylindrical coils remains unsuccessful due to the difficulty of automatically scanning the front, rear, outer, and inner faces of complex spiral-shaped cylindrical coils. This paper presents a turnkey integrated system that can be operated in real time for real coil manufacturers. First, a novel 1-degree-of-freedom rotational progressive system is designed to automatically transport and sort individual spiral coils. Second, a multisensor imaging system is designed to capture images of 360° panoramic views of a cylindrical coil. Then, a robust automatic defect detection algorithm consisting of crack detection and geometric measurement is developed to detect defects in cylindrical coils during manufacturing procedures. The results of a large-scale validation experiment indicate that the proposed AOI system is robust and is practically immune to variations in surface normal vectors, occlusion, and defocus blur, and it has a high sensitivity and specificity of 99.75% and 94.2%, respectively. This validation experiment proved that the software and hardware designed in this study can implement defect detection of spiral coils.

Real Time Quality Detection and Automatic Recognition of Concrete Structures: Image Processing Algorithm Based on Deep Learning

The safety and durability of concrete structure is an important issue in engineering quality management. In this paper, an image processing algorithm based on deep learning is proposed to realize real-time quality inspection and automatic defect identification of concrete structures. This algorithm uses the Convolutional Neural Network (CNN) to automatically extract the features of concrete surface quality images, and then identify the existence of defects, thus improving the detection efficiency and accuracy. In this paper, for this method, data samples with different specific structures are collected and manually labeled to the data set; then, a multi-layer CNN model with convolution layer, pool layer and full connection layer is designed to train the model, and then image enhancement technology is used to reduce information noise, and data enhancement technology is used to improve the problem-solving ability of the model. In addition, the strategy of Dropout is used to close some nodes to reduce parameters and prevent over-fitting, and the learning rate is adjusted to optimize the classification effect. In addition, this study constructs an all-weather real-time detection framework, including data acquisition, preprocessing, feature extraction, classification and identification and decision-making alarm system, to ensure the rapid positioning of the detection system. To sum up, the results of this study show that the deep learning image processing algorithm has good contrast performance in the field of real-time quality inspection of concrete structures. CNN model has better performance than GAN (Generative Adversarial Network) and LSTM (Long Short-Term Memory) models in detection time, defect identification resolution and detection accuracy. The maximum detection time is 366ms and the shortest is 213 ms. The successful development of this algorithm provides a new method for automatic detection of concrete structure quality, which has important application value in engineering practice. This research has a broad development prospect.

Integration of artificial intelligence and augmented reality for assisted detection of textile defects

The Fourth Industrial Revolution conceptualizes the rapid change of industries resulting from the convergence of technologies such as artificial intelligence, genetic editing, and advanced robotics that enable increasing interconnectivity and machines that can analyze and diagnosing problems without human intervention, through intelligent automation. In this scenario, the use of augmented reality technologies is of great interest. The paper aims to explore the use of augmented reality in support of traditional inspections for assisting textile experts in fabric defect detection. The contribution of this study consists of three main phases, necessary for the future development of the system: (1) the analysis of possible automatic defect detection techniques; (2) the analysis of hardware solutions for the realization of a system based on important criteria such as operator comfort, system footprint, and so on; (3) the proposal of a possible comprehensive solution. Considering these aspects this paper identifies and investigate the best scenario for the introduction of artificial intelligence and augmented reality technologies to help the operator in the detection of textile defects.

Machine Vision-Based Lightweight Multiscale Automatic Defect Segmentation Network for MLCC

Machine Vision-Based Lightweight Multiscale Automatic Defect Segmentation Network for MLCC

Object Defect Detection Using Histogram Analysis and Spearman’s Correlation Coefficient

In modern manufacturing industry, Automatic defect detection is becoming an attractive alternative to Human Inspection. Automatic defect detection on object surfaces is a compelling process. For accurate automated inspection and classification, computer vision image processing system has been widely used in manufacturing industries. In this article, we proposed histogram based automatic defect detection that process three objects at a time. In the first step we collect image from the camera, perform preprocessing, segmentation then we used histogram and Spearman’s correlation coefficient to find the defect or non-defect objects. The experimental analysis was evaluated on 300 images including defective and non-defective objects.

Automatic defect detection of carbon fiber woven fabrics using machine vision

In the context of defect detection during the preform preparation stage in carbon fiber composite liquid molding processes, this study proposed three machine vision-based automatic detection methods, i.e. Gabor filter-assisted detection (GFAD), morphological processing-assisted detection (MPAD), and integral image-assisted detection (IIAD). These methods were evaluated on carbon fiber fabric images containing nine different types of defects, considering four evaluation aspects. The experimental results demonstrated the effectiveness of these three methods in defect detection. Among them, the best-performing method achieved a detection accuracy of over 90% for defect sizes and a recognition rate of 98% for defects.

Multi-feature vision transformer for automatic defect detection and quantification in composites using thermography

This paper proposes a novel multi-feature vision transformer model for automatic defect detection and quantification in composites using thermography. Firstly, a sampling strategy for infrared data is proposed to address the issues of data class overlap and imbalance. Secondly, multiple feature fusion is employed to aggregate various important infrared features that exhibit strong correlations with defects. Furthermore, a multi-feature vision transformer (VIT) model that leverages self-attention mechanisms to capture global context and long-range dependencies in the composite images is developed. Experiments are performed to verify the effectiveness of the proposed method. The results show that the proposed method has excellent performance in terms of both accuracy and generalization capability.

Automatic flaw detection of carbon fiber prepreg using a CFP-SSD model during preparation

As an intermediate material for carbon fiber composites, surface flaws inevitably occur during carbon fiber prepreg preparation, which will seriously affect the quality of carbon fiber composite products. The current approaches for identifying flaws on carbon fiber prepreg have the drawbacks of being labor-intensive and inefficient. This research puts forward a novel model for identifying surface flaws on carbon fiber prepregs using an improved single-shot multibox detector (SSD), called CFP-SSD model. A machine vision-based platform for surface flaws identification on carbon fiber prepreg is created. Additionally, the modified-Resnet50 backbone employed in the proposed CFP-SSD model can enhance the effectiveness of network feature extraction. Then, the multi-scale fusion remote context feature extraction module is designed to efficiently fuse the information from the shallow and deep layers. The findings of performance comparison experiments and ablation experiments indicate that the proposed CFP-SSD model achieves 86.63% mean average precision and a detection speed of 47 frames per second, which is sufficient for real-time automatic identification of carbon fiber prepreg surface flaws.

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

EXplainable artificial intelligence for automatic defect detection in additively manufactured parts using CT scan analysis

AbstractAdditive Manufacturing (AM) and in particular has gained significant attention due to its capability to produce complex geometries using various materials, resulting in cost and mass reduction per part. However, metal AM parts often contain internal defects inherent to the manufacturing process. Non-Destructive Testing (NDT), particularly Computed Tomography (CT), is commonly employed for defect analysis. Today adopted standard inspection techniques are costly and time-consuming, therefore an automatic approach is needed. This paper presents a novel eXplainable Artificial Intelligence (XAI) methodology for defect detection and characterization. To classify pixel data from CT images as pores or inclusions, the proposed method utilizes Support Vector Machine (SVM), a supervised machine learning algorithm, trained with an Area Under the Curve (AUC) of 0.94. Density-Based Spatial Clustering with the Application of Noise (DBSCAN) is subsequently applied to cluster the identified pixels into separate defects, and finally, a convex hull is employed to characterize the identified clusters based on their size and shape. The effectiveness of the methodology is evaluated on Ti6Al4V specimens, comparing the results obtained from manual inspection and the ML-based approach with the guidance of a domain expert. This work establishes a foundation for automated defect detection, highlighting the crucial role of XAI in ensuring trust in NDT, thereby offering new possibilities for the evaluation of AM components.

Parametric study of anomaly detection models for defect detection in infrared thermography

In the current NDT 4.0 revolution, machine learning and artificial intelligence have emerged as the major enablers for non-destructive testing and evaluation (NDT&E) of industrial components. However, recent developments in active thermal NDT (TNDT) support its use as a practical method for checking a range of industrial components. Additionally, recent post-processing research in TNDT has developed several machine learning models to replace human interaction and offer automatic defect detection. However, the smaller area of the flaws and their related few thermal profiles than the wide sound area, leading to imbalanced datasets, make it difficult to train a supervised deep neural. Recently added to TNDT are anomaly detection models and one-class classifiers, both of which are commonly applied machine learning models to real-world issues. The accuracy and other important metrics in autonomous defect detection are influenced by the hyper-parameters of these models, such as contamination factor, volume of training data, and initialization parameter of the relevant model. The current paper investigates how initialization parameters affect these models’ TNDT capabilities for automated flaw detection. Using quadratic frequency modulated thermal wave imaging (QFMTWI), a carbon fiber-reinforced polymer specimen with variously sized artificially produced back-holes at different depths is examined. A good hyper-parameter for automatic flaw identification is chosen after qualitatively comparing testing accuracy, precision, recall, F-score, and probability.

Research progress on bearing fault diagnosis with signal processing methods for rolling element bearings

As a prerequisite for rotating machinery to operate effectively, rolling element bearings play an essential role. The focus of condition monitoring has initially been on defect identification, then on its measurement, and eventually on automatic defect prediction. The improvement in signal processing has made this breakthrough possible. The quality of characteristics taken from the bearing signals strongly impacts how effective these techniques are. Aiming to provide the researchers with the option to choose and implement the optimum signal analysis method, the authors have described numerous signal processing techniques used to diagnose faults in rolling element bearings. The research study examines several important studies and explains their relevance to locating rolling bearing defects. It analyzed recent research, ones from the past, and developments in the field of diagnosing bearing defects. The main goal of the research is to investigate different vibration signal processing and analysis methods for locating and evaluating bearing faults. After that, each of these subjects is rigorously analyzed in order to draw conclusions, spot new trends, and pinpoint areas that still need more research. This article is meant to serve as a guide for those who operate in the condition monitoring domain.

Deep Learning-Based Automatic Defect Detection of Additive Manufactured Stainless Steel

Accumulating interest from academia and industry, the part of quality assurance in metal additive manufacturing (AM) is achieving incremental recognition owing to its distinct advantages over conventional manufacturing methods. In this paper, we introduced a convolutional neural network, YOLOv8 approach toward robust metallographic image quality inspection. Metallographic images accommodate key information relating to metal properties, such as structural strength, ductility, toughness, and defects, which are employed to select suitable materials for multiple engineering execution. Therefore, by comprehending the microstructures, one can understand insights into the behavior of a metal component and make predictive assessments of failure under specific conditions. Deep learning-based image segmentation is a robust technique for the detection of microstructural defects like cracks, inclusion, and gas porosity. Therefore, we improvise the YOLOv8 with dilated convolution mechanisms to acquire automatic micro-structure defect characterization. More specifically, for the first time, the YOLOv8 algorithm was proposed in the metallography dataset from additive manufacturing of steels (Metal DAM) to identify defects like cracks and porosity as a novel approach. A total of 414 images from ArcelorMittal engineers were used as an open-access database. The experimental results demonstrated that the YOLOv8 model successfully detected and identified cracks and porosity in the metal AM dataset, achieving an improved defect detection accuracy of up to 96% within just 0.5 h compared to previous automatic defect recognition processes.

Skin Imaging: A Digital Twin for Geometric Deviations on Manufactured Surfaces

Closed-loop manufacturing is crucial in Industry 4.0, since it provides an online detection–correction cycle to optimize the production line by using the live data provided from the product being manufactured. By integrating the inspection system and manufacturing processes, the production line achieves a new level of accuracy and savings on costs. This is far more crucial than only inspecting the finished product as an accepted or rejected part. Modeling the actual surface of the workpiece in production, including the manufacturing errors, enables the potential to process the provided live data and give feedback to production planning. Recently introduced “skin imaging” methodology can generate 2D images as a comprehensive digital twin for geometric deviations on any scanned 3D surface including analytical geometries and sculptured surfaces. Skin-Image has been addressed as a novel methodology for continuous representation of unorganized discrete 3D points, by which the geometric deviation on the surface is shown using image intensity. Skin-Image can be readily used in online surface inspection for automatic and precise 3D defect segmentation and characterization. It also facilitates search-guided sampling strategies. This paper presents the implementation of skin imaging for primary engineering surfaces. The results, supported by several industrial case studies, show high efficiency of skin imaging in providing models of the real manufactured surfaces.

SPEED:Semantic Prior and Extremely Efficient Dilated Convolution Network for Real-Time Metal Surface Defects Detection

Automatic defect detection on the metal surface is a vital task for product inspection in industrial assembly lines or production processes. Owing to miscellaneous patterns of defects, inter-class similarity, intra-class difference, and fewer defect samples, achieving accurate and automatic detection remains a big challenge. What's more, since the rising demand for production efficiency, real-time detection is increasingly desirable. This article proposes a semantic prior and extremely efficient dilated convolution network, named SPEED, for pixel-wise detection on the metal surface, which aims to address the aforementioned issues. The architecture of SPEED involves the following: (i) a semantic prior (SP) branch, with shallow layer and prior mapping module to capture low-level details; and (ii) an extremely efficient dilation (EED) branch, with lightweight bottleneck to obtain high-level context. Furthermore, an aggregation module is designed to fuse both types of feature representation. Additionally, different level features of bottleneck are fused to improve the segmentation performance. Experimental results on three metal surface defect datasets indicate that the proposed method outperforms the state-of-the-art approaches in terms of the mean intersection of union, model parameters, FLOPs, and FPS. More specifically, SPEED achieves 92.34% mIoU on NEU-Seg, 88.65% mIoU on Severstal Strip Steel, and 63.91% mIoU on MT Defect.

Automatic histogram-based defect detection in glass fibre reinforced polymer composites using terahertz time-domain spectroscopy reflection imaging

We developed a method for automatic detection of defects in glass fibre reinforced polymer (GFRP) based on a terahertz time-domain spectroscopy (TDS) system. Two GFRP specimens with round defects of different sizes (inclusions and delaminations) were measured by means of a high-speed TDS reflective scanner with an electronically controlled optical sampling scheme. The algorithm provided a series of C-scans, which were then pre-selected to identify only those with features potentially associated with defects. A thresholding method based on Gaussian fitting of the histogram was developed. The results showed that 87 % of defects were detected for the specimen with inclusions and 100 % for the specimen with delaminations. The algorithm also provided information about the depth of defects, as each C-scan was assigned to a time slice. The results demonstrate that the TDS-based system, along with the proposed histogram-based method, can automatically detect defects in GFRP. © 2017 Elsevier Inc. All rights reserved.