Accurate Fault Detection Research Articles

Cross-condition fault diagnosis of chillers based on an ensemble approach with adaptive weight allocation

The Heating, Ventilation and Air Conditioning (HVAC) systems are complex and prone to failures during operation, often leading to significant energy waste. Timely and accurate Fault Detection and Diagnosis (FDD) can enhance energy efficiency. The HVAC system operates under diverse conditions, data-driven models trained under existing conditions may experience performance degradation when faced with new conditions. Transfer learning offers an effective solution to this issue. This study proposes a novel transfer learning ensemble model based on adaptive weights, leveraging different transfer learning strategies to improve diagnosis performance under new conditions. Multiple cross-condition transfer learning tasks were implemented to test the proposed method, and its effectiveness was validated through multiple experiments to minimize the impact of randomness. Results showed that, compared to fine-tuning (FT), domain-adversarial neural network (DANN), and baseline models, the proposed method outperforms the other models. The average accuracy of multiple experiments improved by 0.21 % to 2.34 % compared to FT. Additionally, modifying DANN to utilize a small amount of labeled information from the target domain has led to greater overlap between the feature distributions of the source and target domains, resulting in improved performance that is close to that of FT. Finally, we analyzed the impact of target domain data volume on the performance of the four methods. The performance of the baseline model improved significantly with the increase in data volume, while the other models showed less improvement. Meanwhile, the diagnostic results of the baseline model were significantly influenced by experimental randomness when there is less training data, whereas the FT diagnostic results were relatively more stable.

A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component

Among the various challenges of transmission line distance protection, it has become increasingly difficult to deal with the symmetrical phenomenon of power swing (PS) and it can lead to maloperation of distance relays and power system blackout. Distance relays have challenges in distinguishing the symmetrical fault during power swing and would send a wrong trip command to the circuit breakers, leading to major blackouts. To solve this issue, the main objective of this study is to provide a fast, simple, and local protection scheme based on differential current component (DCC), which is extracted by the difference in predicted and actual samples of local current component. Autoregression technique is used to predict the future cycle current samples using available samples. Based on the DCC, a novel index named differential current component ratio (DCCR) is introduced to effectively detect the internal symmetrical faults from fast/slow power swing conditions. Several tests are run for different fault types, different power swing frequencies, different fault locations, different fault resistances and different fault inception instants. Finally, the results are compared with the available methods. Results demonstrate the high accuracy of the proposed method in fast and accurate detection of internal symmetrical faults during power swing in different frequency rates in less than one cycle.

Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.

The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Insulator-YOLO: Transmission Line Insulator Risk Identification Based on Improved YOLOv5

This study introduces an innovative method for detecting risks in transmission line insulators by developing an optimized variant of YOLOv5, named Insulator-YOLO. The model addresses key challenges in small-defect detection, complex backgrounds, and computational efficiency. By incorporating GhostNetV2 in the backbone to streamline feature extraction and introducing SE and CBAM attention mechanisms, the model enhances its focus on critical features. The Bibi-directional Feature feature Pyramid pyramid Network network (BiFPN) is applied to enhance multi-scale feature fusion, and the integration of CIoU and NWD loss functions optimizes bounding box regression, achieving higher accuracy. Additionally, focal loss mitigates the imbalance between positive and negative samples, leading to more accurate and robust defect detection. Extensive evaluations demonstrate that Insulator-YOLO significantly improves detection accuracy and efficiency in real-world power line insulator defects, providing a reliable solution for maintaining the integrity of transmission systems.

Evaluating Deep Learning Networks Versus Hybrid Network for Smart Monitoring of Hydropower Plants

One of the main goals of the International Energy Agency (IEA) is to manage and utilize clean energy to achieve net zero emissions by 2050. Hydropower plants can significantly contribute to this goal as they are vital components of the global energy infrastructure, providing a clean, safe, and sustainable power source. Accordingly, there is great interest in developing methods to prevent errors and anomalies and ensure full operational availability. With modern hydropower plants equipped with sensors that capture extensive data, machine learning algorithms utilizing these data to detect and predict anomalies have gained research attention. This paper demonstrates that deep learning algorithms are particularly powerful in predicting time series. Three well-known deep learning networks are examined and compared to previous approaches, followed by the introduction of a new, innovative hybrid network. Using real-world data from two hydropower plants, the hybrid model outperforms individual deep learning models by achieving more accurate fault detection, reducing false positives, offering early fault prediction, and identifying faults several weeks before occurrence. These results showcase the hybrid network’s potential to enhance maintenance planning, reduce downtime, and improve operational efficiency in energy systems.

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

Machine Vision AI in Railroad Safety: Advanced Inspection Techniques

This article explores the transformative impact of machine vision AI in railroad safety, focusing on advanced inspection techniques. It examines the integration of technologies such as line scan cameras, LiDAR, and IoT sensors in enabling comprehensive 360-degree inspections of railway infrastructure and rolling stock. The article discusses key innovations in continuous monitoring, data processing, and AI-driven analysis, highlighting their potential to significantly enhance defect detection accuracy, reduce maintenance costs, and prevent accidents. Real-world applications in structural integrity assessment and overhead line inspection are presented, along with an analysis of current challenges and future developments in the field. The article underscores the paradigm shift from reactive to proactive maintenance strategies, promising a safer and more efficient future for rail transport.

Online and Offline Fault Detection and Diagnostics in a Nuclear Power Plant Condenser

Nuclear power plants (NPPs) face significant financial pressures due to operational and maintenance costs. This research investigates Fault Detection and Diagnostics (FDD) techniques to optimize maintenance schedules and reduce expenses. The NPP condenser plays a critical role in converting turbine exhaust steam back into water for reuse. Condenser tube fouling, a prevalent fault mode, impedes heat transfer efficiency and can lead to decreased plant efficiency and safety risks. This study proposes an FDD framework that leverages raw signal analyses from temperature and pressure monitoring to detect and diagnose condenser tube fouling in both online and offline settings. The online approach facilitates close-to-real-time predictions, enabling proactive maintenance strategies. Additionally, the framework explores incorporating a condenser’s maintenance history for enhanced diagnostics. We employ a dataset obtained from a simulated nuclear power plant condenser using the Asherah Nuclear Power Plant Simulator (ANS). ANS replicates the operational dynamics of a pressurized water reactor (PWR) type NPP. The proposed methodology utilizes an encoder-decoder (E-D) structured 1DCNN model to analyze the raw signals. The research demonstrates consistent and accurate fault detection and diagnostics for condenser tube fouling in both online and offline scenarios. A high potential for generalization to unseen conditions was observed. However, online detection using small data windows necessitates caution due to potential false alarms around the transition points. Our findings pave the way for further exploration of robust diagnostics by accommodating a wider spectrum of fouling rates within degradation classes using ANS. This combined online and offline FDD approach offers a promising solution for promoting operational safety, efficiency, and cost-effectiveness in NPP condensers.

Quantitative characterization of surface defects on bridge cable based on improved YOLACT++

The safety and reliability of cables are directly linked to the safe operation of bridges as crucial load-bearing components. The accuracy and efficiency of current methods are still insufficient to segment and quantitatively characterize surface defects on cables. This paper proposes a novel and efficient method for the refined segmentation and quantitative characterization of bridge cable surface defects based on an improved you only look at coefficients++ (YOLACT++) model. For defect segmentation, several enhancements have been made to the YOLACT++ model, including incorporating the convolutional block attention module (CBAM), optimizing the anchor box generation mechanism, and introducing the smoother Mish activation function, which enhances both the accuracy and speed of defect detection. For quantitative characterization, the method adopts surface correction algorithms, pixel statistics, and crack skeleton extraction, resulting in a more accurate representation of defect areas and the length and width of cracks. Compared to the baseline model, the optimized model achieves a 3.58 % improvement in mean average precision (mAP) and an inference speed of 25.74 frames per second (FPS). The results show that the error is within 10 % compared with the manually measured area, which offers a more objective and comprehensive foundation for cable safety assessment.

Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks

The current Internet of Things (IoT) network structure is evolving from small-scale distributed systems to a large-scale hierarchical collaboration between backbone and access networks. In this context, the dynamic changes in backbone node connections and the surge in service demands, coupled with sluggish fault detection speeds, significantly shorten effective service transmission time. To address this issue, this paper proposes an inverse coupled simulated annealing for enhanced OSPF route convergence in IoT networks (OSPF-ICSA). Initially, the link state is derived from the statistical characteristics of Hello packets, while the aggregated characteristics of the link state are employed to characterize the node state, providing data support for the reverse coupled simulated annealing algorithm. Subsequently, the Hello packet is refined, and a mechanism is designed to synchronize OSPF intervals and transmit node states. This ensures that nodes within the same subnet synchronize their sending intervals and fault detection times while sharing their node states. Finally, building upon this foundation, the reverse coupled simulated annealing algorithm is introduced to jointly optimize the Hello packet sending interval and fault detection time. Compared to the traditional AODV protocol, OSPF-ICSA reduces the average fault detection time by over 37.38%, improves the average fault detection accuracy by more than 3.1%, decreases the average routing overhead by over 20%, and increases the average packet delivery rate by over 5.1%.

Incipient fault detection for the spindle bearing of a cement grinding machine based on vibrational resonance

Abstract The spindle bearing of a circulation fan is an important component of a cement grinding machine. In addition to the faults on the spindle bearing, impeller wear and ash accumulation may cause dynamic unbalance and complex vibration interference as noise, which decreases the accuracy of fault detection based on vibrational signals and traditional signal processing-based methods at the early stage of a bearing fault. To address this issue, this paper presents a new fault detection method for the spindle bearing by utilizing extra injected noise and vibrational resonance. To enhance the fault signature and resonance performance, the nonlinear system of the traditional vibrational resonance is replaced by a new hybrid steady-state system, and the underdamped term is considered in the new system. The proposed system provides more possibilities to achieve resonance by adjusting the system parameters and overcomes the limitations of output saturation caused by the classical bistable system. The proposed method is validated by analyzing the collected vibration signals from a spindle bearing of a circulation fan in practice and is compared with other noise-elimination fault detection methods. The results demonstrate the effectiveness and superior performance of the proposed method.

An ultrasonic Lamb wave-based non-linear exponential RAPID method for delamination detection in composites

Accurate detection of defects, particularly delamination, in carbon-fiber reinforced polymer (CFRP) composites is crucial but challenging. This study proposes a baseline-free Lamb wave damage imaging framework that incorporates an adaptive time-reversal technique, and a nonlinear exponential reconstruction algorithm for probabilistic inspection of defects (NE-RAPID) in composites. The framework combines two image fusion strategies: full-summation and full-multiplication. NE-RAPID enhances the traditional RAPID algorithm by replacing linear weights with faster-decaying exponential weights, which improves the localization of delamination and other defect regions with higher resolution. A nonlinear exponential weight is introduced to address uneven probability distributions caused by the non-uniform density of the sensor network, thereby improving the accuracy and reliability of defect detection, including delamination. Experimental validation on CFRP composite plates demonstrates that NE-RAPID significantly outperforms RAPID. NE-RAPID achieves a maximum detection error of only 5.1 mm across different frequencies, while RAPID shows a much higher error of 34.41 mm. Furthermore, NE-RAPID produces sharper damage images with fewer artifacts, significantly reducing the risk of false positives. These findings indicate that NE-RAPID is a highly promising method for precise and reliable delamination detection in composite materials.

Maximizing Steel Slice Defect Detection: Integrating ResNet101 Deep Features with SVM via Bayesian Optimization

Accurate detection of defects on steel surfaces is crucial for maintaining quality standards in steel production. This paper addresses the challenge of classifying steel sheets into distinct defect categories by presenting a robust method that leverages deep learning and advanced optimization techniques. We propose a novel approach that utilizes the ResNet101 model to extract deep features, which are then classified using a support vector machine (SVM). To enhance the SVM's performance, Bayesian optimization is employed for hyperparameter tuning. Our method is validated using the "Severstal: Steel Defect Detection" dataset from Kaggle, achieving a validation accuracy of 89.1% and a test accuracy of 90.6%, with a classification error of 0.10934. Additionally, the area under the curve (AUC) for each class exceeds 0.95 in both the validation and test sets, demonstrating excellent discriminatory power. Further evaluation on the DAGM dataset achieved flawless results, with an accuracy of 100%, AUC of 1, sensitivity of 100%, specificity of 100%, precision of 100%, MCC of 100%, F1 score of 1, and kappa of 100%. On the NEU dataset, our method achieved an accuracy of 97.92%, sensitivity of 97.92%, specificity of 99.58%, precision of 98.06%, F1 score of 0.9791, MCC of 97.55%, and kappa of 92.50%. These results demonstrate the robustness and adaptability of the proposed method, offering an efficient and reliable solution for automating steel defect detection and surface defect classification in industrial applications.

PFEI-Net: A profound feature exploration and interaction network for ceramic substrate surface defect detection

Ceramic substrates serve as the foundational material for numerous electronic devices, and their surface quality directly affects performance and longevity. Therefore, surface defect detection on ceramic substrates is an indispensable task during the manufacturing process. Ceramic substrate surface defects exhibit low contrast, along with intraclass difference and interclass similarity, posing substantial challenges for automated visual inspection. Existing deep learning-based methods utilize carefully crafted backbone networks and feature pyramid structures that perceive multilevel information to deal with the above challenges. However, these methods still suffer from insufficient extraction of discriminative features and low effectiveness in feature fusion, thereby hindering the detection performance. To address these issues, we propose the profound feature exploration and interaction network (PFEI-Net). Initially, we propose the discriminative feature mining backbone (DFM-backbone) to progressively explore the effective discriminative features. In the DFM-backbone, the global information grated perception module is devised to construct long-range feature dependencies and aggregate rich features, whereas the contextual semantic flexible extraction module is designed to meticulously perceive contextual semantic information in a targeted manner, enhancing the comprehensive representation of the discriminative features. Then, we propose the multipath semantic interaction guided-feature pyramid network (MSIG-FPN) to facilitate the fusion and interaction of the valuable information. In the MSIG-FPN, the hierarchical refocus–reaggregation module is constructed to refocus on task-relevant features and provide reliable guidance for the network, whereas the cross-stage semantic deep fusion module is designed to integrate multi-level contextual information deeply across stages and is employed to construct the semantic complementary paths, thereby preventing the loss of crucial features. Finally, built upon the backbone of DFM-backbone and MSIG-FPN, we establish the PFEI-Net, achieving accurate detection of surface defects on ceramic substrates. Experimental results highlight that the PFEI-Net achieves excellent performance with a remarkable 89.3 % mean average precision (mAP) and an inference time of 26.1 ms, which offers a promising and viable solution for automated surface defect detection of ceramic substrate.

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

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

Finite element simulation and experimental study of the coupling acoustic field characteristics of ultrasonic waves with internal defects inside microelectronic packaging

The miniaturization, ultra-thin and multi-layer complex structure of microelectronic packaging complicates the coupling acoustic field of ultrasonic waves and internal defects in the packaging, making accurate defect detection very difficult. In this paper, the finite element models of flip chip (FC) packaging and ball grid array (BGA) packaging are established to investigate the coupling acoustic field characteristics of ultrasonic waves and defects. In addition, based on the ultrasonic pitch and catch technique, the coupling laws of ultrasonic waves of different frequencies and the defects of different types, positions and sizes are analyzed by simulation, and the relationship between the relative amplitudes of the bottom waves and the sizes of different defects is revealed. Two specimens of microelectronic packaging are designed and fabricated to carry out the experimental studies using an ultrasonic signal acquisition system. The simulation and experimental results show that the relationship between the defects with small changes in the same location and the relative amplitudes of the bottom waves is basically linear, while the relationship between the solder ball extension defects with large changes and the relative amplitudes of the bottom waves is basically logarithmic, which provides a theoretical guidance for accurate evaluation of the type, size and location of defects in the practical detection.

Stabilization system for solar loading thermography applied on cultural heritage objects exposed outdoors: the contribution of advanced algorithms and dual-branch U-Net

AbstractThis study investigates the use of solar loading thermography (SLT) for thermal non-destructive testing (TNDT) and image stabilization of cultural heritage objects, specifically focusing on a century-old ancient book. The irregular contours and deteriorated areas of the book posed significant challenges for feature extraction due to non-uniform temperature variations. To address these challenges, a convolutional neural network (CNN) based dual-branch network of U-Net was used to stabilize the dataset across three degrees of freedom with the ancient book. The stabilization process involved tracking feature lines across each frame of the time-domain datasets, correcting for frame misalignment caused by sample movement during prolonged data acquisition. The effectiveness of this stabilization technique was evaluated by comparing the results of principal component analysis (PCA), fast Fourier transform (FFT), and fast iterative filtering (FIF) algorithms before and after stabilization. Significant improvements were observed, particularly in the clarity and accuracy of defect detection, indicating that this technique provides a robust foundation for further analysis and processing of SLT datasets in cultural heritage preservation. This research demonstrates the potential of combining advanced image processing techniques with SLT to enhance the quality and reliability of NDT in preserving valuable historical artifacts.

High-performance surface defect detection of aluminum substrate based on event camera

Abstract Traditional industrial surface defect detection often employs CCD/CMOS cameras, but they are difficult to detect the minute defects on aluminum substrates in highly dynamic industrial scenes due to their nature. Event camera is a novel high-resolution vision sensor which measures per-pixel brightness changes in an asynchronous manner and outputs as event information flow (EIF). Small and weak defects on aluminum substrate can be captured by event camera effectively, but the EIF contain a large amount of noise, making it difficult to perform accurate and high-precision defect detection. To address this problem, we propose a frame aggregation method to realize good event information flow processing (EIFP), and then use an improved circle detection method to locate the aluminum substrate in each frame, removing abundant event information outside the aluminum substrate. Subsequently, we enhance the event signals under different frames based on optical flow tracking using multiple features, and construct a semi-supervised detector based on pseudo-labels to achieve high-precision defect localization. Finally, considering the small inter-class differences in defects on the surface of aluminum substrates, we construct a defect class corrector based on ensemble learning to enhance the ability to determine defect classes, achieving high-precision automatic quality inspection of aluminum substrate surfaces. The performance of our method is compared with other advanced methods based on event camera data of aluminum substrates in real industrial scenarios. The experimental results show that our method has improved the detection accuracy by about 10% and the classification accuracy by about 25% compared to the original state-of-the-art methods.

Enhancing Industrial Valve Diagnostics: Comparison of Two Preprocessing Methods on the Performance of a Stiction Detection Method Using an Artificial Neural Network

The detection and mitigation of stiction are crucial for maintaining control system performance. This paper proposes the comparison of two preprocessing methods for detecting stiction in control valves via pattern recognition via an artificial neural network (ANN). This method utilizes process variables (PVs) and controller outputs (OPs) to accurately identify stiction within control loops. The ANN was comprehensively trained using data from a data-driven model after processing them. Validation and testing were conducted with real industrial data from the International Stiction Database (ISDB), ensuring a practical assessment framework. This study evaluated the impact of two preprocessing methods on fault detection accuracy, namely, the D-value and principal component analysis (PCA) methods, where the D-value method achieved a commendable overall accuracy of 76%, with 86% precision in stiction prediction and a 66% success rate in nonstiction scenarios. This signifies that feature reduction leads to a degraded stiction detection. The data-driven model was implemented in SIMULINK, and the ANN was trained in MATLAB with the Pattern Recognition Toolbox. These promising results highlight the method’s reliability in diagnosing stiction in industrial settings. Integrating this technique into existing control systems is expected to enhance maintenance protocols, reduce operational downtime, and improve efficiency. Future research should aim to expand this method’s applicability to a wider range of control systems and operational conditions, further solidifying its industrial value.

Research of Design Technology of Printed Circuit Board

This paper delves into various methodologies for enhancing the quality, design, and optimization of Printed Circuit Boards (PCBs). With the development of the technology, the importance of Printed Circuit Board(PCB) is recently mentioned by more and more people who is trying to make progress of human technology. The classification of PCB defects, a crucial step in ensuring product reliability, is discussed on several categories of defects in PCB, highlighting recent advancements such as fuzzy c-means segmentation, which significantly improves defect detection accuracy. When producing, the layout is quite significant. In a conference, a group of scientists used a thermal layout modelling and optimization routine to make the board having more space to put circuit on it. Methods to ensure PCB quality during production are examined, focusing on optical and electrical testing. The PCB design process is also explored, emphasizing the importance of those considerations before production. Finally, the paper reviews innovative approaches to PCB layout optimization, particularly in managing heat dissipation and dynamic behavior through advanced simulation techniques.