Accurate Fault Detection Research Articles

Lightweight-detection: The strip steel surface defect identification based on improved YOLOv5d

The surface defect of strip steel seriously affects the product quality, the current identification methods have the inherent defects of low detection efficiency and poor detection accuracy, and it is important an improved an algorithm based on the modified YOLOv5 framework to enhance the defect detection effectiveness and accuracy. To create a more lightweight network model, the C3 module and part of the convolutional structure in the original YOLOv5 network were replaced with the GhostBottleneck structure. Additionally, the Coordinate Attention (CA) mechanism was incorporated into the Backbone section to compensate for the original model's lack of attention mechanisms. To address the angular mismatch between the actual frame and the predicted bounding box, a limitation overlooked by the CIOU loss function was further refined. Experimental results demonstrate that the enhanced YOLOv5s-CSG model outperformed the original YOLOv5s by 7.3 %, achieving a significant 37.9 % reduction in model size and an mAP value of 77.5 %. Furthermore, the detection precision and speed were notably higher compared to several widely-used algorithms. The proposed YOLOv5s-CSG model is well-suited for deployment on mobile devices and shows great potential for rapidly and accurately detecting both the type and location of strip surface defects, surpassing the performance of existing methods.

Artificial Hummingbird Algorithm-based fault location optimization for transmission line

Transmission is an important aspect regarding an effective designing of electric supply system. Ensuring reliable and fault-free transmission from the source for effective distribution to the end consumers is very much desirable. In this respect, fast and accurate fault detection, particularly in the overhead transmission lines, is very pertinent. Various algorithms and novel approaches have been formulated by various researchers aligned to this challenge. In this context, a new algorithm influenced by the biotic procedure of flight skills of hummingbird seems to be one of the best algorithms to address the cited problem. This paper focuses on the formulation of this Artificial Hummingbird Algorithm (AHA) and its high accuracy in ameliorating the fault location in transmission line. The most common flight skills being used in the algorithm are foraging schemes, which includes axial, diagonal, and omnidirectional flights. The proposed AHA has been tested using the Simulink prototype in MATLAB for an overhead transmission line having a length of 300 km and system voltage of 400 kV at suitable lengths. Specimen signal of voltages and currents waveforms has been taken at duo ends of the overhead transmission line. The results of the proposed algorithm have been compared with the results obtained from previous studies, and it has been observed that this algorithm yields better results for various kinds of asymmetrical and symmetrical faults.

Automated fabric inspection system development aided with convolutional autoencoder-based defect detection

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

An Efficient and Accurate Surface Defect Detection Method for Wood Based on Improved YOLOv8

Accurate detection of wood surface defects plays a pivotal role in enhancing wood grade sorting precision, maintaining high standards in wood processing quality, and safeguarding forest resources. This paper introduces an efficient and precise approach to detecting wood surface defects, building upon enhancements to the YOLOv8 model, which demonstrates significant performance enhancements in handling multi-scale and small-target defects commonly found in wood. The proposed method incorporates the dilation-wise residual (DWR) module in the trunk and the deformable large kernel attention (DLKA) module in the neck of the YOLOv8 architecture to enhance the network’s capability in extracting and fusing multi-scale defective features. To further improve the detection accuracy of small-target defects, the model replaces all the detector heads of YOLOv8 with dynamic heads and adds an additional small-target dynamic detector head in the shallower layers. Additionally, to facilitate faster and more-efficient regression, the original complete intersection over union (CIoU) loss function of YOLOv8 is replaced with the IoU with minimum points distance (MPDIoU) loss function. Experimental results indicate that compared with the YOLOv8n baseline model, the proposed method improves the mean average precision (mAP) by 5.5%, with enhanced detection accuracy across all seven defect types tested. These findings suggest that the proposed model exhibits a superior ability to detect wood surface defects accurately.

Machine Learning Approaches for Fault Detection and Diagnosis in Electrical Machines: A Comparative Study of Deep Learning and Classical Methods

Fault detection and diagnosis in electrical machines are crucial for ensuring their safe and reliable operation. In recent years, machine learning techniques have emerged as powerful tools for addressing this challenge, offering the potential for more accurate and efficient fault detection and diagnosis compared to traditional methods. Among these techniques, deep learning has gained significant attention due to its ability to automatically learn relevant features from raw data. However, the performance of deep learning models in this domain has not been extensively compared to classical methods. This paper presents a comparative study of deep learning and classical methods for fault detection and diagnosis in electrical machines. The study evaluates the performance of various machine learning algorithms, including deep neural networks, support vector machines, decision trees, and ensemble methods, in detecting and diagnosing faults such as stator winding faults, rotor faults, and bearing faults. The experimental evaluation is conducted using real-world datasets obtained from electrical machines in industrial settings. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each approach in detecting and diagnosing faults accurately and efficiently. The results of the study indicate that deep learning approaches, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), outperform classical methods in terms of fault detection and diagnosis accuracy. These deep learning models demonstrate the ability to automatically extract informative features from raw sensor data, enabling them to effectively identify subtle patterns indicative of faults. The study investigates the interpretability of deep learning models compared to classical methods, examining the extent to which the models can provide insights into the underlying causes of faults. While deep learning models typically operate as black boxes, techniques such as layer-wise relevance propagation (LRP) are employed to enhance their interpretability and facilitate the identification of relevant features contributing to fault detection and diagnosis. This comparative study provides valuable insights into the strengths and limitations of deep learning and classical methods for fault detection and diagnosis in electrical machines, offering guidance for practitioners and researchers in selecting appropriate approaches for their specific applications.

LESM-YOLO: An Improved Aircraft Ducts Defect Detection Model.

Aircraft ducts play an indispensable role in various systems of an aircraft. The regular inspection and maintenance of aircraft ducts are of great significance for preventing potential failures and ensuring the normal operation of the aircraft. Traditional manual inspection methods are costly and inefficient, especially under low-light conditions. To address these issues, we propose a new defect detection model called LESM-YOLO. In this study, we integrate a lighting enhancement module to improve the accuracy and recognition of the model under low-light conditions. Additionally, to reduce the model's parameter count, we employ space-to-depth convolution, making the model more lightweight and suitable for deployment on edge detection devices. Furthermore, we introduce Mixed Local Channel Attention (MLCA), which balances complexity and accuracy by combining local channel and spatial attention mechanisms, enhancing the overall performance of the model and improving the accuracy and robustness of defect detection. Finally, we compare the proposed model with other existing models to validate the effectiveness of LESM-YOLO. The test results show that our proposed model achieves an mAP of 96.3%, a 5.4% improvement over the original model, while maintaining a detection speed of 138.7, meeting real-time monitoring requirements. The model proposed in this paper provides valuable technical support for the detection of dark defects in aircraft ducts.

Path planning and inspection of complex-shaped 3D-printed composites based on robotic pulse-echo laser ultrasonic testing

With the recent advancement in manufacturing, there has been an increasing demand for composite workpieces in the aviation industry. The emergence of composite 3D printing technology that enables the simplification and automation of composite manufacturing processes presents the possibility of replacing the manufacturing process of secondary structures in an unmanned aerial vehicle (UAV). To evaluate the applicability of the technology, a solution for automated non-destructive testing (NDT) that can be applied after the 3D printing process is needed. This study proposes an advanced approach to utilizing laser ultrasonic testing (LUT) for complex-shaped structures fabricated through continuous fiber 3D printing. To ensure accurate defect detection and localization, which are crucial factors in NDT, we propose a method that utilizes a six-degree-of-freedom (6-DOF) robot arm to control the position of the inspection target. By calculating the scan points and path through coordinate transformation and spatial rotation using a quaternion, all surfaces of the targets were inspected. This maintains a constant stand-off distance (SOD) while the sensing laser was vertically incident on the target surface, resulting in a high signal-to-noise ratio (SNR) of the ultrasonic signals. Inspection results have demonstrated the proposed technique to be suitable for detecting defects in complex-shaped 3D-printed structures.

Defect detection of photovoltaic modules based on improved VarifocalNet

Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet has been proposed to enhance both the detection speed and accuracy of defective photovoltaic modules. Firstly, a new bottleneck module is designed to replace the first bottleneck module of the last stage convolution group in the backbone. This new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules. Secondly, another bottleneck module is also designed and used to replace the original bottleneck module used in the fourth stage convolution group of the backbone. This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy. Besides, an improved intersection over union is proposed and introduced into the loss function to measure the difference between the predicted and ground truth boxes. This is useful for improving defect detection accuracy. Compared to other methods, the proposed method has the highest detection accuracy. Additionally, it also has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method.

A Study on GAN-Based Car Body Part Defect Detection Process and Comparative Analysis of YOLO v7 and YOLO v8 Object Detection Performance

The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, compares and analyzes the object detection performance of the YOLO v7 and v8 models to present an optimal solution for detecting defects in car parts. Through experiments, the dataset was expanded by adding fake defect images generated by the GAN. The performance experiments of the YOLO v7 and v8 models based on the data obtained through this approach demonstrated that YOLO v8 effectively identifies objects even with a smaller amount of data. It was confirmed that defects could be detected. The readout of the detection system can be improved through software calibration.

Design of an optimized sensor fault identification within the limited computing, memory, and energy capabilities

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors’ limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though the diagnosis does not have to be made quickly. This project aims to provide an enhanced sensor-fault detection and diagnostic system for the IoT and CPS with constrained energy, memory, and computation resources. The system’s goals are to promptly detect defects, lessen the computing burden on sensors, and enhance the recall and accuracy of fault detection. This study used a hybrid approach that combined principal component analysis, autoencoder, and gated recurrent unit to create an optimal sensor-fault detection and diagnostic system. There were 1001 sensor readings in the dataset; 112 were defective, while the remaining 888 were normal. The investigation showed that the suggested method, which detected faults with an accuracy of 95% and recall of 92%, achieved high accuracy and recall in recognizing defects in IoT and CPS. With significantly shorter processing times, the system’s potential to reduce computational strain on sensors was also proved. The findings of this study indicate that the suggested optimized sensor-fault detection and diagnosis system successfully detects faults in IoT and CPS with limited computation, memory, and energy resources. The system’s ability to reduce the computing burden on sensors while improving accuracy and recall makes it an appealing choice for industrial and commercial applications.

Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network

AbstractFaults in transmission lines cause instability of power system and result in degrading end users sophisticated equipment. Therefore, in case of fault and for the quick restoration of problematic phases, reliable and accurate fault detection and classification techniques are required to categorize the faults in a minimum time. In this work, 500 kV transmission line (Jamshoro‐New Karachi), Sindh, Pakistan has been modeled in MATLAB. The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. From simulation results, it is found that faults in 500 kV transmission line are classified with 99.9% accuracy. Furthermore, the simulation results of the TCN model compared to bidirectional long short‐term memory (BiLSTM) and Gated Recurrent Unit (GRU) and it has been found that TCN model is capable of classifying faults in 500 kV transmission line with high accuracy due to its ability to handle long receptive field size, less memory requirement and parallel processing due to dilated causal convolutions. Through this work, the meantime to repair of 500 kV transmission line can be reduced.

Transfer learning-based drive-by monitoring of railway tracks

Track quality significantly impacts passenger comfort and safety. The maintenance of track quality involves routine monitoring of track geometry and track component defects. In a drive-by monitoring framework, vibration data are collected from operational trains providing an effective and low-cost track monitoring system. This framework enables each train to inspect multiple lines without the need to install and maintain sensors on each line. In conventional data-driven frameworks that utilize machine learning or statistical models, geometrical defects diagnosis model is often constructed using supervised learning. Supervised learning requires response data and corresponding damage states (i.e., labels) for each railway line to learn the line-specific damage diagnosis model. However, labelled data are frequently unavailable for many lines in practical situations, making it challenging to construct a damage diagnosis model. Furthermore, using data directly from source line to construct a damage diagnosis model for the target line can lead to inaccurate results. To this end, in this paper, a novel drive-by monitoring framework is proposed based on Transfer Learning (TL) concept. This approach transfers the geometrical defects diagnosis model learned from one line to a new line. Firstly, condition-sensitive time domain features are extracted from the raw data. Then, a deep learning model based on the long short-term memory (LSTM) network is trained on a dataset from one line (source domain) and fine-tuned for a new dataset using a small number of samples from another line (target domain). The effectiveness of this framework is compared through the classification performance using real-world monitoring datasets collected from two different lines of the France railways network. The results demonstrate up to a 22% increase in defect detection accuracy compared to the basic method.

Defect monitoring system of the internal structures of a sodium fast reactor using an artificial intelligence model

This study developed a defect-monitoring system with an artificial intelligence model, YOLOv7, which is tailored for processing image data from an ultrasonic visualization system within sodium fast reactor (SFR) internal structures. For the safety of SFR internal structures, although it is a crucial inspection for defect monitoring, it is difficult to identify structural defects because of the invisible environment. Therefore, we applied the YOLOv7 model in this study; however, we encountered challenges including decreased accuracy with complex defect shapes and complications from data augmentation during pre-training. To solve these problems, we additionally applied the enhanced super-resolution generative adversarial network for higher resolution and a Sobel noise-filtering algorithm to enhance the defect detection accuracy. And we evaluated our system by comparing it with a confidence score. This underscores the effectiveness of the approach in enhancing the defect detection capabilities. Therefore, this defect-monitoring system should be designed to preemptively identify internal structure deformations and enhance SFR safety and maintenance practices.

A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

YOLOv8-QR: An improved YOLOv8 model via attention mechanism for object detection of QR code defects

Defect detection in Quick Response (QR) codes has important implications for downstream tasks. However, QR code defects include small objects and complex backgrounds, which makes their recognition effect poor. To address the above problems, we proposed a model based on YOLOv8, called YOLOv8-QR, to detect QR code defects. Specifically, first, the non-local attention (Non-local) module is introduced into the backbone of the YOLOv8 to enhance the interactive ability of the feature. The Non-local captures the correlation information of long-distance dependencies between features by calculating the attention weight between any positions. In addition, the contextual information required for representation learning of different defective objects is different. To extract multi-scale features, a Large Selective Kernel Network (LSKNet) was introduced. LSKNet dynamically adjusts the convolution receptive field of the neck fusion network and effectively uses the receptive field to capture the background information of different objects, thereby improving the representation ability of the model. To improve the defect detection accuracy of small objects in QR codes, the Normalized Gaussian Wasserstein Distance (NGWD) is introduced to replace the Intersection over Union (IoU) optimization function that is sensitive to the position deviation of objects and is not conducive to the regression of multi-scale objects. To verify the effectiveness of the model, the QR dataset was constructed and a series of experiments were conducted based on this dataset. The results show that the mAP50 and mAP50:95 of the YOLOv8-QR reach 95.5% and 65%, which are 3.8% and 2.3% higher than YOLOv8 respectively. The proposed YOLOv8-QR can better adapt to the needs of QR code defect detection in actual industrial environments. Our code is available at https://github.com/Code-of-Liujie/YOLOv8-QR.git.

Rail Surface Defect Detection Based on Dual-Path Feature Fusion

With the rapid development of rail transit, the workload of track maintenance has increased, making the intelligent identification of rail surface defects crucial for improving detection efficiency. To address issues such as low defect detection accuracy, the loss of feature information due to single-path architecture backbones, and insufficient information interaction in existing rail defect detection methods, we propose a rail surface defect detection method based on dual-path feature fusion (DPF). This method initially employs a dual-path structure to separately extract low-level and high-level features. It then utilizes a combination of attention mechanisms and feature fusion techniques to integrate these features. By doing so, it preserves richer information and enhances detection accuracy and robustness. The experimental results demonstrate that the comprehensive performance of the proposed model is superior to mainstream algorithms.

EHIR: Energy-based Hierarchical Iterative Image Registration for Accurate PCB Defect Detection

Printed Circuit Board (PCB) Surface defect detection is crucial to ensure the quality of electronic products in manufacturing industry. Detection methods can be divided into non-referential and referential methods. Non-referential methods employ designed rules or learned data distribution without template images but are difficult to address the uncertainty and subjectivity issues of defects. In contrast, referential methods use templates to achieve better performance but rely on precise image registration. However, image registration is especially challenging in feature extracting and matching for PCB images with defective, reduplicated or less features. To address these issues, we propose a novel Energy-based Hierarchical Iterative Image Registration method (EHIR) to formulate image registration as an energy optimization problem based on the edge points rather than finite features. Our framework consists of three stages: Edge-guided Energy Transformation (EET), EHIR and Edge-guided Energy-based Defect Detection (EEDD). The novelty is that the consistency of contours contributes to aligning images and the difference is highlighted for defect location. Extensive experiments show that this method has high accuracy and strong robustness, especially in the presence of defect feature interference, where our method demonstrates an overwhelming advantage over other methods.

Development of overcurrent relay based on wavelet transform for fault detection in transmission line

This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.

A new relaying approach for protecting TCSC compensated transmission line connected to DFIG based wind farm

In modern power systems, Thyristor Controlled Series Capacitor (TCSC)-compensated transmission lines are crucial in transporting bulk power from large wind farms. However, the performance of conventional distance relays is adversely impacted by the typical fault characteristics of the Doubly Fed Induction Generator (DFIG) and TCSC. To address this challenge, this paper presents a new relaying algorithm such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based Dominant Mode Algorithm-Hilbert Transform (CEEMDAN-DMA-HT) for fault detection and CEEMDAN assisted Enhanced Jaya Optimization-based Random Vector Functional Link Network (EJAYA-RVFL) for fault classification. In proposed fault detection algorithm, the differential current from both ends of the line is subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, leading to the extraction of distinct intrinsic mode functions (IMFs). Employing the Dominant Mode Algorithm, the IMF with the highest Pearson correlation coefficient, referred to as the dominant IMF, is identified. Subsequently, a comparison between this dominant mode IMF and the original input signal is conducted after passing the obtained signal through the Hilbert Transform (HT), enabling effective fault detection. The proposed classifier leverages a straightforward feature set that quantifies the energy of each phase and the ground. These features serve as inputs for the proposed classifier, facilitating the categorization of different fault types. To evaluate the effectiveness of the proposed relaying algorithm, several fault and non-fault scenarios are simulated on a test power system using MATLAB/Simulink. The results demonstrate that; the proposed fault detection and classification algorithm are able to detect and classify faults in less than a half-cycle. The proposed method gives 100% fault accuracy for fault detection and 99.97 % accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.998), Recall (0.997) and F1-Score (0.996), providing quantitative insights into its accuracy and dependability. Finally, a comparative analysis is carried out with existing approaches.

An early-fault diagnostic method based on phase plane for lithium-ion batteries under complex operation conditions

Timely and accurate diagnosis of lithium-ion battery faults is crucial for ensuring the safety of electric vehicles. While incremental capacity (IC)-based diagnostic methods are effective, they can only be employed under charging conditions. To address this issue, a fault diagnostic method based on phase plane analysis is proposed to extend the application to various operating conditions. In a phase plane, the battery voltage is plotted on the x-axis, while the differential in voltage is plotted on the y-axis. During charging, diagnostic vectors indicating the occurrence and type of faults are extracted from the trajectories in the phase plane. Under complex operating conditions, early faults can be detected based on the morphological and scale deviation of the trajectories. Experimental results have shown that the proposed method can effectively detect minor faults. The fault detection rate and detection accuracy rate under charging conditions are 96.25 % and 97.73 %, respectively, which are 17.5 % and 8.54 % higher than those of the IC-based diagnostic method. Furthermore, the fault detection rate and accuracy rate under UDDS (Urban Dynamometer Driving Schedule) are 91.76 % and 97.23 %, respectively. Additionally, the diagnostic speed of the proposed method is twice as fast as that of the IC-based method.