Types Of Faults Research Articles

RAM-YOLOv8: A multi-fault detection method for photovoltaic module

ABSTRACT Given the constraints in fault type identification, localization precision, and detection efficiency inherent in current photovoltaic fault detection methodologies, this paper introduces an innovative detection approach integrating RAM-YOLOv8 with the Swin Transformer. To mitigate the limitations in model extraction capabilities during the detection process and circumvent the vanishing gradient issue encountered during backward information propagation, this study introduces a residual aggregation module as a substitute for the original C2F module, thereby augmenting feature extraction efficiency. Furthermore, within the feature fusion network, the Swin attention mechanism fusion module is designed by integrating the Swin Transformer’s architectural concept to minimize the loss of weak defect information and suppress irrelevant background interference. The model achieves a detection accuracy of 88.5% on the dataset, representing a 7% improvement over the original YOLOv8 algorithm, with a detection rate of 83.33 FPS on an RTX2080 GPU. This method offers a robust and efficient fault detection solution for PV systems, rendering it a practical approach for enhancing system performance and reducing maintenance costs.

Constraints on the Geometry of Peripheral Faults above Mafic Sills in the Tarim Basin, China: Kinematic and Mechanical Approaches

Host rock deformation associated with sill emplacement is used to constrain magma transfer and storage within the upper crust. In contrast to classic models suggesting that the host rock above mafic sills is dominated by elastic bending, recent studies show that bounding faults that limit the uplift area can occur at the peripheries of a mafic sill. However, the accurate dip of this type of fault, named peripheral faults here, is still not well constrained. Their origin is also controversial in some cases. In this study, kinematic modeling and limit analysis are performed to better constrain the structure and mechanical properties of the peripheral faults based on seismic interpretation of a mafic sill from the Tarim Basin, China. The trishear kinematic model successfully reproduces peripheral faulting and associated folding of the host rock by performing a displacement of 58 m on a vertical fault plane with a fault propagation (P) to fault slip (S) ratio of 2.5. The limit analysis also predicts vertical damage at the sill tip by sill inflation. These results suggest that the dip angle of the fault in the case study is 90°, which is more accurate than that from the seismic interpretation with an 88° inward dip. This value may vary in other cases as it depends on the sill geometry (such as diameter and inclination), thickness, depth, and mechanical properties of the host rock. The study supports that peripheral faulting and associated folding can occur at the tips of the mafic sill due to the vertical uplift of the host rock caused by sill inflation. It is also suggested that trishear kinematic modeling and limit analysis are effective methods for studying the geometry of peripheral faults.

Mathematical modelling of induction motors taking into account design-parameter asymmetry

Due to their numerous technical and economic advantages, induction machines (IM) with squirrel-cage rotors are important components of many industrial processes. Their reliability, durability, and ease of operation make them indispensable in various industries, ensuring stable and efficient equipment performance. However, to minimise the risk of costly production failures caused by sudden stoppages, it is important to implement effective diagnostic and monitoring methods. The most common type of fault in squirrel-cage induction motors is the breakage of cage bars and end ring segments, particularly in motors with high inertia loads and frequent stops and starts. Regular maintenance and the implementation of modern control systems in the industry will help ensure the reliable and uninterrupted operation of these machines. In the technical literature, various diagnostic methods have been proposed. Some are based on analysing data collected from the induction motor (IM), detecting characteristic perturbations that indicate faults in the machine. Other methods involve comparing the data obtained from the actual induction motor with its digital model. It is clear that accurate and adequate IM models are necessary for the development and optimisation of fault diagnosis methods. This paper focuses on developing a mathematical model of an induction motor (IM) in a stator-fixed coordinate system, where the squirrel-cage rotor of the IM is represented as a multiphase, symmetrically distributed winding system in space. The adequacy of the obtained model is demonstrated. The developed model allows for the analysis of various types of asymmetries in both stator and rotor of squirrel-cage induction motors

Prognostics and Health Management for Electrified Aircraft Propulsion: State of the Art and Challenges

Abstract In recent years, the aviation industry has witnessed a transformative wave of innovation in electrified aircraft propulsion (EAP), driven by sustainability and efficiency goals. Integration of novel electrical subsystems, including high-voltage power electronics, motors/generators, and energy storage devices, has introduced intricate complexities. In this context, an intensified focus on Prognostics and Health Management (PHM) is imperative, considering the heightened reliability needs in a transportation propulsion application. This paper analyzes the current state-of-the-art in PHM applicable to various EAP systems and components crucial for the functioning of electric aircraft. Typical fault modes and fault management strategies are analyzed at various levels of systems hierarchy. An integral aspect of our investigation involves the identification of critical gaps within existing PHM frameworks, guiding the research agenda for enhanced reliability and performance. Moreover, the distributed nature and complexity of EAP systems underscore the importance of Model-Based Systems Engineering (MBSE). We advocate for the exploration of MBSE not only to inform the design of PHM solutions but also to facilitate certification and Verification and Validation activities. Additionally, the paper offers insights into existing tools and simulation software capable of integrating traditional gas turbine modules with electric subsystems, as well as simulating various faulty conditions in EAP relevant to PHM development. Key gaps in these tools are emphasized, drawing attention to areas that require further refinement and development. This comprehensive exploration aims to pave the way for future advancements in PHM tailored for the unique challenges posed by EAP systems.

Morphometry and kinematics correlation of wrinkle ridges on Mars: Insights from Trishear modelling

Wrinkle ridges are among the most common and controversial compressional tectonic structures on terrestrial planets. While their origins are well inferred to be related to crustal shortening driven by compressional stress, their subsurface characterization is still a matter of debate. Open questions remain about the geometry, number, structural style and kinematics of faults promoting wrinkle ridges. We use the Trishear and Fault-Parallel-Flow integrated forward kinematic modelling to model wrinkle ridges related faults. This is achieved through a series of balanced cross sections and a consequent set of narrow 3D models. We perform a detail kinematic analysis on nine wrinkle ridges: six are located in the circum-Tharsis regions of Lunae Planum and Solis Planum, while three are located in the Hellas Planitia, Hesperia Planum and Syrtis Major Planum, respectively. The applied methodology allows us to quantitatively assess wrinkle ridges geometry and kinematics, and to correlate them with morphometric parameters (i.e., width and relief). Our results indicate how wrinkle ridges tectonics can be characterized by a more complex array of faults than previously modelled. This leads to a total amount of horizontal shortening accommodated differently depending on the number and type of faults (i.e., main fault, backthrust, synthetic faults). The location and geometry of the modelled faults suggest the presence of multiple detachments at different depths and with different mechanical behaviors such as weaker and more frictional décollements, which are likely found within sedimentary interlayers. The amount of shortening, the fault geometry and spacing, as well as the upper faults tips depth are positively correlated with major morphometric parameters of wrinkle ridges topography.

Improving Transmission Line Fault Diagnosis Based on EEMD and Power Spectral Entropy.

The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis.

An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings

Early bearing fault diagnosis plays a crucial role in ensuring the reliability and safety of wind turbines. This process encounters two primary challenges, that is, weak fault characteristics and strong background noise. Consequently, they restrict the effectiveness of conventional diagnosis methods. To address these difficulties, a two-step early fault diagnosis model is proposed based on an anomaly monitoring index [Formula: see text] and improved successive variational mode decomposition-fast spectral correlation (ISVMD-FSC). Initially, [Formula: see text] enables early anomaly detection. Subsequently, the fault type is further identified using ISVMD-FSC. The ISVMD adaptive decomposition of early warning signals can effectively reduce the noise and isolate the fault impact characteristics. Then, the fault characteristic frequency is extracted using spectral coherence analysis to achieve the purpose of fault diagnosis. This model is tested and validated on simulated data, laboratory accelerated bearing life span data, and real high-speed bearing fault data of wind turbines. Notably, the comparative study shows that [Formula: see text] is capable of detecting the fault earlier than the spectral L2/L1 norm, the sum of the weighted normalized square envelope, index 8, and index 9. Additionally, the fault feature extraction effect of ISVMD-FSC is better than many more advanced time-frequency analysis methods. These results emphasize the model’s excellent performance in early bearing fault diagnosis and its good generalization ability.

Electrical surface breakdown characteristics of micro- and nano-Al2O3 particle co-doped epoxy composites

Abstract Epoxy microcomposites are basic materials for gas-insulated switchgear (GIS) spacers that are subjected to huge electrical stress. Previous works have indicated that nanoparticles are beneficial to dielectric performance. However, surface electrical breakdown, a typical fault in GIS of co-doped micro- and nanoparticles in epoxy composites, is seldom studied. In this work, numerous concentrations of micro- and nano-Al2O3 are co-doped into an epoxy matrix; the surface traps, surface charging, and surface breakdown voltages (V sb) of the co-doped composites are studied, and the influence of micro- and nano-Al2O3 on the electrical surface breakdown is clarified. The results show that V sb first decreases and then increases with the microparticles, and V sb decreases from 25.34 kV to 19.52 kV. As the number of nanoparticles increases, V sb increases and then decreases when the microparticle loading is low, but decreases and then increases when the microparticle loading exceeds 40 wt%. Micro-Al2O3 particles introduce surface shallow traps into epoxy composites, while small amounts of nano-Al2O3 introduce deep traps. Two different mechanisms dominate the surface charging and V sb of epoxy micro-nanocomposites. When the surface conductivity is lower than 7 × 10−14 S, the surface charges are reduced by the suppression of electrode injection as the trap depth increases, and V sb increases. When the surface conductivity exceeds 7 × 10−14 S, the surface charge dissipation rate increases with the surface conductivity and V sb increases as the surface conductivity increases. Our work indicates that co-doped micro- and nano-particles should keep the surface conductivity away from the specic value (7 × 10−14 S) to safeguard insulation properties for GIS spacers.

Fault location and type identification method for current and voltage sensors in traction rectifiers

Fault location and type identification method for current and voltage sensors in traction rectifiers

A novel phaselet-based approach for fault detection and location in HVDC lines

Recently, the popularity of direct current (DC) transmission has surged due to a 30 % reduction in power electronics equipment costs over the last decade. High-Voltage Direct Current (HVDC) transmission lines experience fluctuations in both nominal and faulty states, posing challenges for fault location methods, particularly those based on traveling waves, resulting in imprecise outcomes with an average error margin of ±2 %. This paper investigates a novel fault detection and location approach utilizing wavelet and phaselet transforms. The proposed method focuses on traveling waves in HVDC transmission lines, where the phaselet transform effectively eliminates undesired fluctuations in line currents, improving fault detection accuracy by up to 15 %. By leveraging the traveling time in the faulty line, the exact fault location is calculated with an accuracy of 98.5 %, independent of transmission line parameters, making it applicable to any DC line. Additionally, the method's precision is minimally affected by fault resistance, with <0.5 % deviation even in nonlinear scenarios. Sensitivity analysis conducted on various parameters, including mother wavelet patterns, suggests the optimal solution for each fault type, enhancing detection speed by 20 %. The proposed method is validated using the Cigre HVDC benchmark, confirming its accuracy, speed, and robustness in fault detection and location in HVDC lines. The main contribution of this paper is an accurate, fast, and robust phaselet-based algorithm that reduces fault location errors to <1 % and is applicable to all types of bipolar and monopolar HVDC systems, delivering reliable results for both ground and line faults.

Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Multi-sensor time-series data at different locations contains not only temporal correlation information but also spatial correlation information which is treasure for machine fault diagnosis. Existing graph construction methods mainly apply different data analysis methods to connect nodes and edges. Few works, however, consider the location of the sensor itself and temporal correlation information of multi-sensor time-series data. To mine the relationship between spatial information and temporal information, the multi-sensor temporal-spatial graph is constructed in this paper. Hereinto, the different data points of multi-sensor are severed as different nodes which represents the spatial feature information. The temporal information is contained between different nodes of the same sensor. Moreover, an empirical mode decomposition graph convolution network (EGCN) is proposed to extract the feature. Specifically, the traditional graph convolution operator is changed to empirical mode decomposition which can decompose the input features into multiple intrinsic modal features to achieve adaptive feature extraction and improve the representation capability of the network. Finally, the different fault types can be classified by fully connected layers. Experiments from different test rigs demonstrate that the proposed method achieves a diagnostic accuracy exceeding 99 % under limited fault samples.

INTELLIGENT FAULT DETECTION AND LOCATION IN ELECTRICAL HIGH-VOLTAGE TRANSMISSION LINES

Fault location in transmission lines is critical to ensure power systems' reliable and efficient operation. Accurate fault detection and localization are essential to minimize downtime, prevent cascading failures, and maintain the overall stability of the electrical grid. Over the years, various fault location methods have been proposed, ranging from traditional model-based approaches to more sophisticated artificial intelligence techniques. This research presents two fault location methodologies: the Atom search optimization metaheuristic approach (ASO) and machine learning (ML) with cubic spline models. We evaluate the performance of both approaches by considering different fault types, fault distances, and fault resistance. We analyze accuracy and computational efficiency. The findings reveal that the Metaheuristic Approach demonstrates robustness in fault detection and localization under diverse conditions but may suffer from higher computational overhead. In contrast, the hybridization of machine learning and metaheuristic exhibits promising potential in achieving real-time fault localization with improved accuracy.

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

Fault imaging using earthquake sequences: a revised seismotectonic model for the Albstadt Shear Zone, Southwest Germany

In Germany, the highest seismic hazard is associated with the Albstadt Shear Zone (ASZ) in the western Swabian Jura, a low mountain range in southwest Germany. The region is affected by continuous micro-seismic activity with the potential for damaging earthquakes (nine events with ML≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} 5 in the 20th\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{th}$$\\end{document} century). Within the AlpArray and StressTransfer projects nine temporary seismic stations have been installed in the region of the ASZ to densify the permanent seismic monitoring. In October 2018 and September 2019, the state seismological survey (LED) detected two low-magnitude earthquake sequences with hundreds of events in the area. The temporarily densified local network allows us to systematically analyze these sequences and to search for other sequences by applying a template-matching routine on data from 2018 to 2020. In total, six earthquake sequences could be identified with at least 10 events. The four largest sequences (> 50 events) consist of two fore- and aftershock sequence and two earthquake swarms. Earthquake swarms were so far not observed around the ASZ. Precise relative hypocenter relocations and the determination of fault-plane solutions allow us to propose a seismotectonic model based on the three imaged fault types: (a) The well-known NNE-SSW striking sinistral strike-slip ASZ at depths of 5-10 km, (b) a NW-SE striking dextral strike-slip fault zone at depths of 11-15 km beneath the Hohenzollerngraben (HZG), a shallow, apparently aseismic NW-SE striking graben structure; this NW-SE fault zone possibly is an inherited zone of weakness in the basement and facilitated the development of the HZG and (c) at the intersection of the ASZ with the NW-SE fault zone, complex faulting in form of NNW-SSE striking sinistral strike-slip and normal faulting possibly to accommodate local stresses.

Survey of fault management techniques for edge-enabled distributed metaverse applications

The metaverse, envisioned as a vast, distributed virtual world, relies on edge computing for low-latency data processing. However, ensuring fault tolerance – the system’s ability to handle failures – is critical for a seamless user experience. This paper analyzes existing research on fault tolerance in edge computing over the past six years, specifically focusing on its applicability to the metaverse. We identify common fault types like node failures, communication disruptions, and security issues. The analysis then explores various fault management techniques including proactive monitoring, resource optimization, task scheduling, workload migration, redundancy for service continuity, machine learning for predictive maintenance, and consensus algorithms to guarantee data integrity. While these techniques hold promise, adaptations are necessary to address the metaverse’s real-time interaction requirements and low-latency constraints. This paper analyzes existing research and identifies key areas for improvement, providing valuable research guidelines and insights to pave the way for the development of fault management techniques specifically tailored to the metaverse, ultimately contributing to a robust and secure virtual world.

Three-phase balance relay using numerical techniques experimentally verified on synchronous machines

In this paper, a multifunction three-phase balance relay based on normalized correlation coefficients is proposed to detect and estimate imbalances and perturbations in synchronous machine output signals. Furthermore, fresh definitions of imbalance and disturbance indicators derived using the correlation estimators are introduced, taking into account the changes in the waveform phase displacement, frequency, amplitude, and shape of the machine three-phase waves. Experimental tests are performed on a motor–generator set connected to a three-phase load, which is used to identify and evaluate the imbalance and disturbance conditions of the voltage and current measurements. Extensive tests for different fault types have been presented. The practical results show that the proposed protection can respond quickly to faults, and assess online the level of the imbalance/disturbance with high accuracy. Its running time is within one cycle. In addition, the proposed algorithm’s reliability and accuracy are its most significant attributes, whose percentages exceed 96.6%. The present algorithm considers the impact of both negative and zero sequence components when measuring di-symmetry factors, while some conventional approaches merely rely on the negative sequence component computed for the machine voltages and currents.

Expert label for explainable fault diagnosis and for unknown fault generalization

This study proposes the expert label that incorporates specific process knowledge into sample labels to enhance the explainability of diagnostic results. Traditional sample label is a one-hot encoding vector without inherent physical meaning, limiting the diagnostic scope to predefined fault types without explainability. In contrast, expert label as multi-label provides multi-dimensional information including disturbance type, unit location, and associated variable type. Since multi-label can accommodate an exponential number of fault types, the diagnosis models based on expert label have the potential to generalize unknown faults. Meanwhile, we develop the process multi-scale feature mixture network (PMFMN), which excels in deep feature extraction through modules including multi-scale decomposition, multiple pattern representation, and Fourier attention mechanism. The effectiveness of PMFMN with expert label is validated through case studies in two chemical processes, which further improves the fault diagnosis rate, provides reliable diagnostic results with explainability, and can diagnose unknown faults.

A Literature Review of Fault Detection and Diagnostic Methods in Three-Phase Voltage-Source Inverters

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.

Review on Short-Circuit Protection Methods for SiC MOSFETs

SiC MOSFETs have been a game-changer in the domain of power electronics, thanks to their exceptional electrical traits. They are endowed with a high breakdown voltage, reduced on-resistance, and superior thermal conductivity, which make them supremely suitable for high-power and resilient applications across aviation, automotive, and renewable energy sectors. Despite their intrinsic advantages, SiC MOSFETs also necessitate advanced safeguarding mechanisms to counteract the vulnerability to short-circuit conditions due to their lower short-circuit robustness. This review paper offers an in-depth analysis of the array of short-circuit protection (SCP) methods applied to SiC MOSFETs. This paper scrutinizes techniques such as desaturation detection, di/dt detection, gate charge characteristics monitoring, two-dimensional monitoring, Rogowski coil-based detection, and two-stage turn-off strategies. The paper meticulously explores the operational principles, merits, and limitations of each method, with an emphasis on their adaptability to various fault types, including hard switching faults and load-induced faults. This review acts as a thorough compendium, guiding the choice of pertinent SCP strategies, ensuring the secure and efficient functioning of SiC MOSFETs in demanding applications.

Research on Fault Diagnosis of Drilling Pump Fluid End Based on Time-Frequency Analysis and Convolutional Neural Network

Operating in harsh environments, drilling pumps are highly susceptible to failure and challenging to diagnose. To enhance the fault diagnosis accuracy of the drilling pump fluid end and ensure the safety and stability of drilling operations, this paper proposes a fault diagnosis method based on time-frequency analysis and convolutional neural networks. Firstly, continuous wavelet transform (CWT) is used to convert the collected vibration signals into time-frequency diagrams, providing a comprehensive database for fault diagnosis. Next, a SqueezeNet-based fault diagnosis model is developed to identify faults. To validate the effectiveness of the proposed method, fault signals from the fluid end were collected, and fault diagnosis experiments were conducted. The experimental results demonstrated that the proposed method achieved an accuracy of 97.77% in diagnosing nine types of faults at the fluid end, effectively enabling precise fault diagnosis, which is higher than the accuracy of a 1D convolutional neural network by 14.55%. This study offers valuable insights into the fault diagnosis of drilling pumps and other complex equipment.