Fault Detection Effectiveness 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.

Fault detection using machine learning based dynamic ICA-distributed CCA: Application to industrial chemical process

Unexpected accidents and events in industrial chemical processes have resulted in a considerable number of causalities and property damage. Safety process management in industrial chemical processes is critical to avoid and ensure casualties and property damage. However, due to the immense scope and high complexity of current industrial chemical processes, the traditional safety process management approaches cannot address these challenges to attain adequate fault detection accuracy. To address this issue, an innovative machine learning-based distributed canonical correlation analysis-dynamic independent component analysis (DICA-DCCA) approach is needed to improve the fault detection effectiveness of complicated systems. The (DICA-DCCA) model could potentially detect anomalies and faults in industrial chemical data by utilizing three essential statistics:Id2,Ie2and squared prediction error (SPE). The practical effectiveness of the proposed frameworks is evaluated and compared using a continuous stirred tank reactor (CSTR) framework as a standard benchmark study. The research findings present that the suggested (DICA-DCCA) approach is more resilient and effective in detecting abnormalities and faults than the ICA and DICA approaches with FDR 100 % and FAR 0 %. The implied research approach is robust, operational, and productive.

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, more and more research has been focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were carried out to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

Spiking autoencoder for nonlinear industrial process fault detection

In recent years, artificial neural networks have been found successful applications in process monitoring within metallurgy, chemical engineering and mechanical manufacturing owing to their superior ability to construct flexible models with varying degrees of nonlinearity and effectively handle large-scale data. However, the model's distinctiveness diminishes due to the high cost associated with neural network training and initializations, resulting in a more fluctuating fault detection performance. To alleviate this problem and inspired by the way biological neurons emit spikes to transmit information, the spiking neurons are employed in the construction of a spiking neural network, introducing a shift in parameter optimization from conventional global parameter tuning to two-stage layer-wise process. Based on this, a brain-like discrete model, spiking auto-encoder (SNNAE) is constructed. The SNNAE's training process is firstly compared with that of artificial neural networks with the same structure through modeling a numerical example, results show the superior efficiency and accuracy of SNNAE in modeling highly nonlinear data. To gauge its effectiveness in fault detection, SNNAE is then compared with the state-of-art methods through the same numerical example and a three-phase flow process, indicating its ability to significantly improve process monitoring performance in nonlinear processes while notably reducing its fluctuation.

TD-Net:tiny defect detection network for industrial products

AbstractThe detection of tiny defects in industrial products is important for improving the quality of industrial products and maintaining production safety. Currently, image-based defect detection methods are ineffective in detecting tiny and variously shaped defects. Therefore, this paper proposes a tiny defect detection network (TD-Net) for industrial products to improve the effectiveness of tiny defect detection. TD-Net improves the overall defect detection effect, especially the detection effect of tiny defects, by solving the problems of downsampling of tiny defects, pre-filtering of conflicting deep and shallow semantic information, and cascading fusion of multi-scale information. Specifically, this paper proposes the Defect Downsampling (DD) module to realize the defect information supplementation during the backbone downsampling process and improve the problem that the stepwise convolution easily misses the detection of tiny defects. Meanwhile, the Semantic Information Interaction Module (SIIM) is proposed, which fuses deep and shallow semantic features, and is designed to interact the fused features with shallow features to optimize the detection of tiny defects. Finally, the Scale Information Fusion Module (SIFM) is proposed to improve the Path Aggregation Network (PANet) for cascading fusion and information focus on different scale information, which enables further improvement of defect detection performance of TD-Net. Extensive experimental results on the NEU–DET data set (76.8$$\%$$ % mAP), the Peking University PCB defect data set (96.2$$\%$$ % mAP) and the GC10-DET data set (71.5$$\%$$ % mAP) show that the proposed TD-Net achieves competitive results compared with SOTA methods with the equivalent parameter quantity.

An adaptive metaheuristic optimization approach for Tennessee Eastman process for an industrial fault tolerant control system.

The Tennessee Eastman Process (TEP) is widely recognized as a standard reference for assessing the effectiveness of fault detection and false alarm tracking methods in intricate industrial operations. This paper presents a novel methodology that employs the Adaptive Crow Search Algorithm (ACSA) to improve fault identification capabilities and mitigate the occurrence of false alarms in the TEP. The ACSA is an optimization approach that draws inspiration from the observed behavior of crows in their natural environment. This algorithm possesses the capability to adapt its search behavior in response to the changing dynamics of the optimization process. The primary objective of our research is to devise a monitoring strategy that is adaptable in nature, with the aim of efficiently identifying faults within the TEP while simultaneously minimizing the occurrence of false alarms. The ACSA is applied in order to enhance the optimization of monitoring variables, alarm thresholds, and decision criteria selection and configuration. When compared to traditional static approaches, the ACSA-based monitoring strategy is better at finding faults and reducing false alarms because it adapts well to changes in process dynamics and disturbances. In order to assess the efficacy of our suggested methodology, we have conducted comprehensive simulations on the TEP dataset. The findings suggest that the monitoring strategy based on ACSA demonstrates superior fault identification rates while concurrently mitigating the frequency of false alarms. In addition, the flexibility of ACSA allows it to efficiently manage process variations, disturbances, and uncertainties, thereby enhancing its robustness and reliability in practical scenarios. To validate the effectiveness of our proposed approach, extensive simulations were conducted on the TEP dataset. The results indicate that the ACSA-based monitoring strategy achieves higher fault detection rates while simultaneously reducing the occurrence of false alarms. Moreover, the adaptability of ACSA enables it to effectively handle process variations, disturbances, and uncertainties, making it robust and reliable for real-world applications. The contributions of this research extend beyond the TEP, as the adaptive monitoring strategy utilizing ACSA can be applied to other complex industrial processes. The findings of this study provide valuable insights into the development of advanced fault detection and false alarm monitoring techniques, offering significant benefits in terms of process safety, reliability, and operational efficiency.

L[formula omitted]OP-ART: A linear-time adaptive random testing algorithm for object-oriented programs

Object-oriented (OO) programming offers useful and desirable characteristics in the resulting code. These OO characteristics, however, demand an effective testing method. Adaptive random testing (ART) serves as an effective testing technique where test cases are distributed uniformly according to a proper metric for difference (distance) between test cases. Recently proposed object and method invocation sequence similarity (OMISS) distance metric was incorporated into a new ART algorithm (OMISS-ART) for testing OO software. Unfortunately, OMISS-ART’s high effectiveness in finding faults comes at significantly higher computational overheads. To alleviate the problem, we present in this paper a linear-time ART algorithm called L′OP-ART (Linear-time Object-oriented Program testing by ART) based on a lightweight OMISS metric. L′OP-ART designs specific data structures that store information about executed test cases, thus, the differences between candidates and executed test cases can be obtained with reduced overheads. Empirical studies show that L′OP-ART detects failures more effectively than random testing (RT), and has comparable fault-detection effectiveness to other ART algorithms for OO testing (according to the F-measure, or the expected number of test case executions required to find a failure). Furthermore, the test case generation overhead of L′OP-ART is low, close to that of RT.

Multiscale Kernel Entropy Component Analysis With Application to Complex Industrial Process Monitoring

Modern industrial processes are characterized by numerous measurement points and wide operating ranges, resulting in extremely complex correlations among variables. Therefore, an effective monitoring system should balance diverse process characteristics such as nonlinearity, non-Gaussianity, and multiscale simultaneously. Moreover, it should have the ability to detect and diagnose faults in the incipient stage, thus avoiding accident escalation. With these goals in mind, this paper proposes an integrated monitoring solution based on multiscale kernel entropy component analysis (MSKECA). Specifically, process variables are first decomposed into approximations and details at different scales in real-time using the moving window-based wavelet, and contributions from each scale are collected in separate matrices. Then, KECA-based local-scale models are built to sift out important detail scales for reconstruction along with the approximate scale. Lastly, a KECA-based global-scale model is developed to monitor the reconstructed data. To improve the fault detection performance, a novel monitoring index based on the angle metric called angle variance index (AVI) is designed. In addition, to achieve effective diagnosis, MSKECA-based contribution plots are constructed, which depict the contributions of variables to faults at each scale, thus comprehensively revealing the root causes. Finally, the effectiveness and superiority of the proposed solution are validated by comparisons with other advanced counterparts in two industrial scenarios. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper proposes an integrated monitoring solution for complex industrial processes, i.e., MSKECA-based fault detection and diagnosis. The solution takes into account the diversity of process characteristics, the effectiveness of incipient fault detection, and the richness of diagnostic information. Specifically, 1) MSKECA can simultaneously handle the nonlinear, non-Gaussian, and multiscale characteristics that are prevalent in real process data. It enables on-line detection of significant events occurring at different scales and extraction of fault-sensitive features for monitoring; 2) based on the angular structure of KECA, the AVI statistic is designed, which exhibits low autocorrelation and is sensitive to faults. Leveraging the statistic, MSKECA allows reliable and prompt responses to faults; 3) MSKECA-based contribution plots not only convey diverse diagnostic information including fault variable, type, and grade but also are not susceptible to the smearing effect, which is helpful for practitioners to achieve fault repair. The solution has proven useful for a real hot rolling process. It can also be extended to other industrial processes.

Multi-modal adaptive feature extraction for early-stage weak fault diagnosis in bearings

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection.

Detection of Apparent Defects in HPLC/Dual Mode Portable Sorting Device Based on Deep Learning and Image Processing

Abstract As the growth of automated detection technology, traditional manual detection has gradually been replaced. To improve the effectiveness of defect detection, a HPLC/dual mode portable sorting device with deep learning and image processing is raised for apparent defect detection. The product image is segmented using flood filled mean shift method, and defect detection is achieved using Yolo v3 algorithm. An LME2918 chip is the main component of the communication module in the portable device. Based on experimental data, image segmentation accuracy can range from 80% to 100%, and image matching accuracy can range from 85% to 95%. Under the power line carrier mode, the average success rate of apparent defect detection in portable sorting devices can reach 85%, and the change in success rate is not significant, indicating that the detection is relatively stable; Under wireless communication mode, the average success rate can reach 83%, and the change in success rate is relatively obvious, because wireless communication is easily affected by the external environment. The experimental data shows that the defect detection effect of the HPLC/dual mode portable sorting device based on deep learning and image processing meets the design requirements.

Cascaded adaptive global localisation network for steel defect detection

Defect detection is crucial in ensuring the quality of steel products. This paper proposes a novel deep neural network, cascaded adaptive global location network (CAGLNet), for detecting steel surface defects. The main objective of this study is to address the challenges associated with the irregular shape and dense spatial distribution of defects on steel. To achieve this goal, CAGLNet integrates a feature extraction network that combines residual and feature pyramid networks, a cascade adaptive tree-structure region proposal network (CAT-RPN) that eliminates the need for prior knowledge, and a global localisation regression for steel defect detection. This paper evaluates the effectiveness of CAGLNet on the NEU-DET dataset and demonstrates that the proposed model achieves an average accuracy of 85.40% with a fast frames per second of 10.06, outperforming those state-of-the-art methods. These results suggest that CAGLNet has the potential to significantly improve the effectiveness of defect detection in industrial production processes, leading to increased production yield and cost savings. Abbreviations: AT-RPN, adaptive tree-structure region proposal network; CAGLNet, cascaded adaptive global location network; CAT-RPN, cascade adaptive tree-structure region proposal network; CNN, convolutional neural network; DNN, deep neural network; EPNet, edge proposal network; FPN, feature pyramid network; FCOS, fully convolutional one-stage detector; FPS, frames per second; GMM, Gaussian mixture model; IoU, intersection-over-union; ROIAlign, region of interest align; RPN, region proposal network; ResNet, residual network; ResNet50_FPN, residual network and feature pyramid network; SABL, side aware boundary localisation; SSD, single-shot multiBox detector; TPE, Tree-structured Parzen estimator

Frequency adaptive fault detection by feature pyramid network with wavelet transform

Fault detection is a key step in seismic structure interpretation. Current research has achieved good results in fault detection by using synthetic training data to train deep-learning models. However, there is an inevitable difference in frequency bandwidth between synthetic training data and real seismic data, which makes it difficult for deep-learning models to obtain ideal fault detection results on real seismic data. To solve this problem, the feature pyramid network (FPN) is introduced to obtain multiscale deep-learning features, which can reduce the impact of seismic data frequency bandwidth differences on fault detection. Then, we apply the multiscale wavelet transform to extract multiscale frequency spectral features of the seismic data and combine them with the multiscale deep-learning features through concatenation operation. Furthermore, the seismic data is decomposed into signals with different frequency bands through the wavelet transform, and we use the energy of these signals as the network weights of multiscale mixed features to further improve the frequency adaptability of our method. Based on these works, we not only improve the fault detection effect in a specific work area but also improve the generalization ability of the deep-learning model in different work areas, thus further promoting the application of deep learning in actual production. Compared with the fault detection results by the traditional deep-learning model U-Net and the traditional FPN on multiple real seismic data and synthetic seismic data, experimental results demonstrate the effectiveness of our method.

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors.

Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

To accurately and sensitively track the stator current of an induction motor (IM) and detect faults, stochastic resonance (SR) and the Teager energy operator (TEO) are combined to detect a fault in the residual stator current of a sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. First, a new reaching law is constructed to establish an SMO for better state tracking. Second, SR is used to absorb noise and amplify the detection residuals of the SMO, and the output results are estimated by the TEO in the time domain to achieve fault detection. Finally, the detection results of the IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of the stator current is processed by SR and the TEO, and the effectiveness of fault detection is enhanced.

Route : Roads Not Taken in UI Testing

Core features (functionalities) of an app can often be accessed and invoked in several ways, i.e., through alternative sequences of user-interface (UI) interactions. Given the manual effort of writing tests, developers often only consider the typical way of invoking features when creating the tests (i.e., the “sunny day scenario”). However, the alternative ways of invoking a feature are as likely to be faulty. These faults would go undetected without proper tests. To reduce the manual effort of creating UI tests and help developers more thoroughly examine the features of apps, we present Route , an automated tool for feature-based UI test augmentation for Android apps. Route first takes a UI test and the app under test as input. It then applies novel heuristics to find additional high-quality UI tests, consisting of both inputs and assertions, that verify the same feature as the original test in alternative ways. Application of Route on several dozen tests for popular apps on Google Play shows that for 96% of the existing tests, Route was able to generate at least one alternative test. Moreover, the fault detection effectiveness of augmented test suites in our experiments showed substantial improvements of up to 39% over the original test suites.

Developing a Deep Learning-Based Defect Detection System for Ski Goggles Lenses

Ski goggles help protect the eyes and enhance eyesight. The most important part of ski goggles is their lenses. The quality of the lenses has leaped with technological advances, but there are still defects on their surface during manufacturing. This study develops a deep learning-based defect detection system for ski goggles lenses. The first step is to design the image acquisition model that combines cameras and light sources. This step aims to capture clear and high-resolution images on the entire surface of the lenses. Next, defect categories are identified, including scratches, watermarks, spotlight, stains, dust-line, and dust-spot. They are labeled to create the ski goggles lenses defect dataset. Finally, the defects are automatically detected by fine-tuning the mobile-friendly object detection model. The mentioned defect detection model is the MobileNetV3 backbone used in a feature pyramid network (FPN) along with the Faster-RCNN detector. The fine-tuning includes: replacing the default ResNet50 backbone with a combination of MobileNetV3 and FPN; adjusting the hyper-parameter of the region proposal network (RPN) to suit the tiny defects; and reducing the number of the output channel in FPN to increase computational performance. Our experiments demonstrate the effectiveness of defect detection; additionally, the inference speed is fast. The defect detection accuracy achieves a mean average precision (mAP) of 55%. The work automatically integrates all steps, from capturing images to defect detection. Furthermore, the lens defect dataset is publicly available to the research community on GitHub. The repository address can be found in the Data Availability Statement section.

An Evaluation of Ranking-to-Learn Approaches for Test Case Prioritization in Continuous Integration

Continuous Integration (CI) environments is a practice adopted by most organizations that allows frequent integration of software changes, making software evolution more rapid and cost-effective. Such environments require dynamic Test Case Prioritization (TCP) approaches that adapt better to the test budgets and frequent addition/removal of test cases. In this sense, Ranking-to-Learn approaches have been proposed and are more suitable for CI constraints. By observing past prioritizations and guided by reward functions, they learn the best prioritization for a given commit. In order to contribute for improvements and direct future research, this work evaluates how far the solutions produced by these approaches are from optimal solutions produced by a deterministic approach (ground truth). To this end, we consider two learning-based approaches i) RETECS, which is based on Reinforcement Learning; and ii) COLEMAN, an approach based on Multi-Armed Bandit. The evaluation was conducted with twelve systems, three test budgets, two reward functions, and six measures concerning fault detection effectiveness, early fault detection, test time reduction in the CI cycles, prioritization time, and accuracy. Our findings have some implications for the approaches application and reward function choice. The approaches are applicable in real scenarios and produce solutions very close to the optimal ones, respectively, in 92% and 75% of the cases. Both approaches have some limitations to learn with few historical test data (a small number of CI Cycles) and deal with a large test case set, in which many failures are distributed over many test cases.

Incremental Testing in Software Product Lines—An Event Based Approach

One way of developing fast, effective, and high-quality software products is to reuse previously developed software components and products. In the case of a product family, the software product line (SPL) approach can make reuse more effective. The goal of SPLs is faster development of low-cost and high-quality software products. This paper proposes an incremental model-based approach to test products in SPLs. The proposed approach utilizes event-based behavioral models of the SPL features. It reuses existing event-based feature models and event-based product models along with their test cases to generate test cases for each new product developed by adding a new feature to an existing product. Newly introduced featured event sequence graphs (FESGs) are used for behavioral feature and product modeling; thus, generated test cases are event sequences. The paper presents evaluations with three software product lines to validate the approach and analyze its characteristics by comparing it to the state-of-the-art ESG-based testing approach. Results show that the proposed incremental testing approach highly reuses the existing test sets as intended. Also, it is superior to the state-of-the-art approach in terms of fault detection effectiveness and test generation effort but inferior in terms of test set size and test execution effort.

Towards an Automatic Test Generation Method for Systems of Systems based on Fault Injection and Model-Based Systems Engineering

The emergence and development of systems of systems (SoSs) have expanded the complexity and adaptability of systems engineering. Due to the heterogeneity of its constituent systems, designing and analyzing an SoS faces enormous challenges. Therefore, the verification of an SoS is important in its design phase. However, related methods and techniques are still in the preliminary research state, mainly for requirements verification and system verification, and a lack of efforts in design verification for SoSs. Aiming to provide the ability to detect hazardous states at the design phase, we worked on a novel method for automatically generating test cases for model verification in SoSs. Considering the characteristics of SoSs, the method adopts fault injection and model-based system engineering as the foundations to extend the automated generation capability of test cases. This paper proposes an automatic test generation (ATG) method for an SoS based on fault injection and model-based systems engineering (MBSE). It is meant to generate test cases that can be used to check the effectiveness of fault detection or identification in SoSs and for the effective testing of their constituent systems (CSs). This paper discusses scenario generation and ATG in linear temporal logic and designs an algorithm to generate traces as test suites, taking into account the constraints of the models. By establishing a concept alignment example (CAE) as the experimental use case, three test scenarios are generated automatically, showing that the use of the proposed ATG method provides a reasonable hazardous detection capability for verification in SoS design. The main contributions of the paper are (1) the description of the proposed two-phase ATG approach with fault-centric modeling activities and generation-related activities; (2) the explanation of the designed ATG algorithm at both high and low levels; and (3) the discussion of the experiment in an emergency response CAE with three generated test scenarios.

Ground Fault Protection Methods in Microgrid Systems

Microgrids, which independently configure power supply and demand from large‐scale power grids, are attracting attention for improving resilience and increasing the rate of local production and consumption of renewable energy. The establishment of a protection system against ground fault phenomena to protect the public and equipment is a microgrid implementation challenge that has not been given sufficient attention. This study evaluated the applicability of zero‐phase potential devices (ZPDs) and earthed voltage transformers (EVTs) for real‐world ground fault detection. We evaluated the effect of the number of ZPDs and EVTs installed on the effectiveness of ground fault detection. In addition, we analyzed the dependencies of ground fault occurrence on variable ground fault resistances and ground capacitances to determine the feasibility of detecting ground faults using ZPDs and EVTs in the real world. Based on the results of this study, ground fault detection using ZPD and EVT is feasible when applied to existing distribution lines in microgrid systems. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.