Fault Parameters Research Articles

Research on Status Assessment and Operation and Maintenance of Electric Vehicle DC Charging Stations Based on XGboost

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards.

Aircraft engine fault diagnosis based on fused convolutional Transformer

Aircraft engines will face corrosion and erosion problems when working in harsh gas path environments for a long time, and the fault parameter characteristics are not obvious. Therefore, accurate aircraft engine fault diagnosis methods are of great significance to ensure the safe operation of aircraft. In order to improve the prediction accuracy, an aircraft engine fault diagnosis method based on fused convolutional Transformer is proposed. The self-attention mechanism is used to extract useful features and suppress redundant information. The maximum pooling layer (MaxPool) is introduced into the Transformer model to further reduce the model memory consumption and parameter quantity, and alleviate the overfitting phenomenon. The simulation data set of turbofan engines based on GasTurb modeling is used for verification. The results show that compared with the Transformer network and other traditional deep learning models Back Propagation Neural Networks (BP network), Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN), the accuracy rates are improved by 6.552%、28.117%、13.189%and respectively 10.29%, which proves the effectiveness of the proposed method and can provide a certain reference for aircraft engine fault diagnosis.

Adaptive fault estimation and accommodation for distributed parameter systems with coupled parabolic partial differential equations

This paper presents a novel model-based approach for fault detection, estimation and accommodation in linear distributed parameter systems (DPS) governed by parabolic partial differential equations (PDEs), prone to sensor and actuator faults. Unlike traditional methods, our approach utilises a filter-based observer directly employing the PDE representation and relies solely on boundary measurements, eliminating the need for extensive sensor arrays. By generating fault detection residuals from a comparison of measured and observer outputs, our method offers efficient fault detection. Innovative parameter update laws facilitate accurate estimation of fault parameters, enabling precise adjustment of the nominal controller to mitigate fault effects. Rigorous Lyapunov analysis ensures the robustness of our approach. Additionally, we derive a formula for estimating time-to-accommodation (TTA), providing valuable insights into fault resolution timelines. Simulations on a linearised diffusion process validate the effectiveness of our scheme, highlighting its superiority over existing approaches.

Geologic Input Databases for the 2025 Puerto Rico—U.S. Virgin Islands National Seismic Hazard Model Update: Crustal Faults Component

Abstract The last National Seismic Hazard Model (NSHM) for Puerto Rico and the U.S. Virgin Islands (PRVI) was published in 2003. In advance of the 2025 PRVI NSHM update, we created three geologic input databases to summarize new onshore and offshore fault source information in the northern Caribbean region between 62°–70° W and 16°–21° N. These databases, of fault sections, fault-zone polygons, and geologic estimates of fault activity (fault-slip rate and earthquake recurrence intervals) at specific sites, document updates to fault parameters used in prior seismic hazard models in PRVI. Fault sources were reviewed from published studies since 2003, which document substantial changes to the understanding of fault location, geometry, or activity. New fault section sources were added for features that meet the criteria of (1) length ≥7 km, (2) unequivocal evidence of recurrent tectonic Quaternary activity, and (3) documentation that is publicly available in a peer-reviewed source. In addition, we revised several broad areal sources, such as the Mona and Anegada extensional zones. The 2003 model included three fault sections and two fault-zone polygons (areal sources). These databases include 35 fault sections, 6 fault-zone polygons, and 51 earthquake geology sites. To characterize fault activity rates, slip-rate bins were assigned based on landscape expression and paleoseismic trench observations for faults without published slip-rate sites. Additional fault sources were evaluated but not included in these databases due to a lack of published information about fault location, geometry, or recurrent Quaternary activity. The PRVI NSHM 2025 geologic input databases describe crustal faulting; the geometries and coupling of Puerto Rico subduction zone and Muertos Trough models are considered in a separate database. Updates to the fault sections, fault-zone polygons, and earthquake geology databases can help inform the location and recurrence rate of damaging earthquakes in the PRVI NSHM implementation.

Building a General Algorithm for Seismic Hazard Analysis in the Sunda Arc through Geodynamic Simulations

Abstract Because of its well-documented subduction zone and outer island arc, Sumatra provides a unique setting for studying and forecasting earthquakes within the seismically active Sunda Arc. This study builds on previous research that utilized Global Positioning System data and the Akaike information criterion to analyze probabilistic seismic hazard functions. However, this study replaces surface displacement rate data with a forward model derived from previous fault modeling results to create a more broadly applicable earthquake forecasting algorithm. Although the best-fit model patterns generated by this new algorithm are consistent with past studies, the forward model demonstrates a lower degree of fit compared to models utilizing natural surface displacement data. This discrepancy highlights the need to refine the fault parameter models to estimate surface displacement rates. Despite this limitation, the study makes a valuable contribution by developing a general algorithm applicable to other subduction zones within the Sunda Arc region. With further refinement and incorporation of more accurate fault modeling and data, this algorithm has the potential to formulate the best-fit earthquake spatial forecast models. This approach could be applied to other seismically active areas, particularly those near subduction zones.

A hybrid intelligent system for protection of transmission lines connected to PV farms based on linear trends

Conventional relays face challenges for transmission lines connected to inverter-based resources (IBRs). In this article, a single-ended intelligent protection of the transmission line in the zone between the grid and the PV farm is suggested. The method employs a fuzzy logic and random forest (RF)-based hybrid system to detect faults based on combined linear trend attributes of the 3-phase currents. The fault location is determined and the faulty phase is detected. RF feature selection is used to obtain the optimal linear trend feature. The performance of the methodology is examined for abnormal events such as faults, capacitor and load-switching operations simulated in PSCAD/EMTDC on IEEE 9-bus system obtained by varying various fault and switching parameters. Additionally, when validating the suggested strategy, consideration is given to the effects of conditions such as the presence of double circuit lines, PV capacity, sampling rate, data window length, noise, high impedance faults, CT saturation, compensation devices, evolving and cross-country faults, and far-end and near-end faults. The findings indicate that the suggested strategy can be used to deal with a variety of system configurations and situations while still safeguarding such complex power transmission networks.

Addendum to: Research on electromagnetic torque and fault parameter identification of pumped storage machine under power generation state based on optimized network parameter method

Addendum to: Research on electromagnetic torque and fault parameter identification of pumped storage machine under power generation state based on optimized network parameter method

Solving the puzzle of the 1996 Biak, Indonesia tsunami

On February 17, 1996, an earthquake (Mw=\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${M}_{\ ext{w}}=$$\\end{document} 8.2) occurred northeast of Biak Island, Indonesia, and generated a tsunami. Interestingly, the tsunami runup on the southwest side of Biak Island, which did not face the epicenter, was higher than that on the side of the island facing the epicenter. It was earlier suggested that the earthquake triggered submarine landslides. However, this hypothesis remains unexplored and unconfirmed. In this study, tsunami arrival times obtained from eyewitness accounts were used to perform backward tsunami travel time modeling. Based on the backward tsunami travel time results and multibeam bathymetry, five submarine landslide candidates were identified: three located to the southwest of Biak Island and two to the south of the island. Our analysis indicates that tsunamis from a large submarine landslide off the southwest coast of Biak Island (SL 2) and a small submarine landslide off the south side of Biak Island (SL 4) contributed to the 1996 Biak tsunami event and came earlier than the main tsunami from the earthquake fault. Tsunami sources were earlier modeled only by fault parameters without considering submarine landslide sources. As a result, the models could not explain the observed runup and eyewitness tsunami arrival times for the southwest and south of Biak Island. To address this problem, we propose an approach that combines a submarine landslide model with a modified version of a previously proposed fault model. Our combined model explains the observed runup and eyewitness tsunami arrival times well (Aida’s index values of the model are K = 1.00 and κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} = 1.44).Graphical abstract

Neural learning fault-tolerant control of fuel cell–battery–ultracapacitor-based hybrid electric vehicle

This article investigates the energy management and control of a fuel cell hybrid electric vehicle (FCHEV) with parametric uncertainties and sensor faults. The FCHEV in this study consists of a fuel cell, battery, and ultracapacitor connected to the DC-bus via DC–DC power converters, while the DC-bus is connected to the AC motor which drives the electric vehicle via a DC–AC converter. To control the power transfer from the energy sources to the drivetrain, a finite-time fractional-order control is proposed to coordinate the DC–DC power converters. A radial basis function neural network (RBFNN) is employed to estimate and compensate for sensor faults and parametric uncertainties. A minimum learning parameter scheme is used to minimize the computational burden on the RBFNN. The main tasks of the proposed scheme are; tolerating faults and parametric uncertainties, delivering the required power to the load, voltage regulation of the DC-bus, tracking the reference currents for the battery, fuel cell, and ultracapacitor in a short finite time. The closed-loop system is guaranteed to be stable in finite time using the Lyapunov function. The simulation results highlight the validity of the presented control.

An integrated approach for anomaly detection in sensor and system data using soft sensors and cyclic monitoring

Anomaly detection is crucial for maintaining the safety and reliability of industrial systems. However, effectively distinguishing between detected anomalies as signal errors or system faults can be challenging. This categorization presents a significant hurdle in isolating and managing faults. To address this challenge, this paper proposes a novel anomaly detection approach. The proposed approach leverages soft sensors and cyclic monitoring techniques to achieve improved anomaly detection. Specifically, it involves constructing soft sensors to aid in identifying the fault level, creating a parameter reconstruction space, and implementing cyclic monitoring to identify and evaluate fault parameters. The number of monitoring cycles serves as a basis for classifying faults into different levels. Simulation examples are presented to validate our proposed approach.

Modeling of Mode I crack-tip dislocation nucleation in three FCC materials: Ni, Cu and Al

An accurate estimation of the critical stress intensity factor for crack tip dislocation nucleation under Mode I loading is of great importance to determine whether a material is intrinsically ductile or not. Here, shear displacements and energy change at crack tip in FCC nickel, copper and aluminum are investigated during Mode I fracture process using atomistic simulations. In light of our simulation results, a new shear resistance model is formulated by a general Fourier expansion with coefficients identified by the computed energy curve. The new model involving the step formation energy which can be regarded as a new parameter and unstable stable stacking fault energy, reduces to Rice theory if no step exists. The criterion for nucleation is developed based on the idea that crack tip behaviors are controlled by the shear resistance and the maximum point serves as an obstacle to conquer. The predictions of the critical shear displacement corresponding to maximum shear resistance position and the critical nucleation energy show good agreement with simulation results. In addition, the new model can be further utilized to study the effect of complex stress state on Mode I crack tip dislocation nucleation.

Multiple Seismic Slip‐Rate Pulses and Mechanical and Textural Evolution of Calcite‐Bearing Fault Gouges

AbstractNatural fault zones are complex, spatially heterogeneous systems. Rock deformation experimental studies simplify the complexity of natural fault zones either as a surface discontinuity between intact rocks (bare‐rock surfaces) or as a few mm‐thick gouge layer. However, depending on the simplified fault type and its slip history, the response to applied deformation can vary. In this work, we conduct laboratory experiments for investigating the evolution of mechanical parameters of simulated faults made of calcite gouge subjected to multiple (four) identical seismic slip‐rate pulses. We observed that, as the number of applied slip‐rate pulses increased, (a) initial friction and steady‐state friction remained approximatively constant, (b) peak friction and normalized strength excess increased and, (c) the slip distances to achieve peak and steady‐state friction, Da and Dc, decreased. The greatest changes occurred between the first and the second slip‐rate pulse. From this pulse onward, the dissipated energy of the calcite gouge fault was similar to those obtained in bare‐rock surfaces experiments. Microstructural analysis showed that, strain is localized in up to two (recrystallized) principal slip zones (PSZ) with sub‐micrometric grain size, surrounded by low porosity sintered and non‐sintered comminuted gouge domains. We conclude that previous seismic slip episodes impact on both the structure and the strain localization processes within a fault, contributing to its shear fabric evolution. We highlight that the strain localization process identifies the PSZ, dissipating the least amount of energy within the entire experimental fault zone.

A study on yield calculation method using suspension power chart in beam pumping systems

Abstract Accurate production prediction plays a crucial role in well fault diagnosis and parameter optimization, significantly contributing to the advancement of oilfield digital construction. This paper focuses on the dynamic analysis of beam pumping machines to establish a calculation model for the power diagram of rod pumping systems. The solution involves employing the finite difference method and Fourier series method, facilitating the transition from the suspension point work diagram to the pump work diagram. The effective stroke of the plunger is determined by analyzing the opening and closing points of the Ver using the maximum curvature analysis method. Subsequently, the leakage and volume coefficients are derived by integrating on-site production data, culminating in the realization of an oil well production calculation method based on the suspension work diagram. The compilation of the pump power chart and oil well production calculation program was executed using QT 6.2.3. Comparative analysis with measured production data revealed an average accuracy of 90.05% using the finite difference method, surpassing the performance of the Fourier series algorithm. The established model not only offers a reliable means of predicting production but also provides valuable technical support for the digital and intelligent applications within the oilfield.

Diagnosing Incipient Faults for a Faster Adoption of Sustainable Aerospace Technologies

Next-generation aircraft require the development and integration of a deal of innovative green technologies to meet the ambitious sustainability goals set for aviation. Those transformational efforts are associated with a tremendous increase in the complexity of the onboard systems and their multiphysics-coupled behaviors and dynamics. A critical aspect relates to the identification of the coupled faults resulting from the integration of those green technologies, which introduce damage identifiability issues and demand new approaches for the efficient and accurate identification of non-nominal fault conditions. Model-based fault detection and identification (FDI) methodologies have been proven essential to identify onboard the damages affecting the systems from physical signal acquisitions, but existing methods are typically computationally expensive and fail to capture incipient coupled faults, which prevents their adoption and scaling with the increasing complexity of novel multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed, and the exact identification of the incipient fault condition was achieved one order of magnitude faster than with standard algorithms.

Complex plane based adaptive method for protection of series compensated transmission line using phasor measurement unit data

Though differential relays are preferred for the protection of series-compensated transmission line (SCTL), they may mal-operate during current inversion, high resistance internal faults and current infeed/outfeed situations. Therefore, a new protection scheme for SCTL using Phasor Measurement Unit data, that adaptively manages the operating region in the current ratio complex plane according to the percentage compensation level, is presented. Boundaries of the operating region are determined adaptively based on the estimated impedance value of the series compensation. Usefulness of the technique is confirmed by generating cases covering wide range of fault parameters, compensation level, and load angle on an existing 400 kV Indian SCTL using PSCAD software as well as through Hardware in Loop simulation employing the Real Time Digital Simulator. Reported outcomes reveal the ability of the suggested algorithm in sensing all types of internal faults. Its performance remains unaffected during current/voltage inversion, operation of MOV and/or airgap during high fault current conditions, current infeed/outfeed situation, and external fault with current transformer saturation condition. Impact of the presence of noise in the acquired signals is also analyzed. The suggested technique is applicable for SCTLs having different voltage levels and power flow conditions. The proposed method has higher responsiveness during high resistance internal faults up to 500 Ω and better steadiness during external faults compared to numerous prevailing methods.

Thermal behaviour of a transformer mineral oil‐tank surface under incipient turn‐to‐turn short‐circuit fault

AbstractA slight turn‐to‐turn short‐circuit fault in the incipient stage does not trigger fuse protection, and the regular transmission and transformation functions of the transformer within the power grid are not affected very much by the incipient turn‐to‐turn short‐circuit fault. Therefore, the incipient turn‐to‐turn short‐circuit faults are often undetected, resulting in massive accidents. Incipient turn‐to‐turn short‐circuit faults lead to localized overheating in the transformer, which changes the transformer's oil‐tank surface temperature (OTST). A thermal simulation model (TSM) is presented. Based on the TSM, OTST data with different load rates and fault parameters are collected. The steady‐state and transient characteristics of OTST are analysed by extracting the OTST feature vector and the temperature difference of specific regions. The results show that the growth of the heat production value of faulty coils causes a rise in the OTST; higher faulty coils' locations lead to wider OTST differences; the temperature difference's area S can follow the incipient short‐circuit fault in the first 20 min after it occurs, which is faster than the top oil temperature. This study gives insight into the thermal behaviour of OTST, assisting in fault detection and location at the incipient fault stage.

Research on electromagnetic torque and fault parameter identification of pumped storage machine under power generation state based on optimized network parameter method

Because of the complex and frequent multi-operation conditions of pumped storage machine and the difficulty of its numerical analysis, an optimized network parameter method (ONPM) is given to identify the second harmonic electromagnetic torque of large salient-pole generators. Different from the former research which only considers the positive and negative sequence components, the second harmonic electromagnetic torque of pumped storage machine under power generation state is identified in neutral point grounded system, which are displayed by the lumped parameters. Then, in order to test the validity of the theoretical analysis among power generation salient-pole generators, the finite element model of 300 MVA pumped storage machine under power generation is established. Through the comparison and analysis of the finite element result data with the actual experimental data in the steady state of no-load test and short-circuit test, the correctness of the finite element model is completely verified. Finally, in the case of small current asymmetry and large current asymmetry, the second harmonic electromagnetic torques obtained by ONPM and finite element method (FEM) are found respectively, and their relative errors are elaborately displayed. The results display that ONPM can accurately identify the electromagnetic torque parameters which indicate the specific operation and fault state of pumped storage machine under power generation state. This fault parameter identification has practical significance for monitoring and improving the operation state and stability of large salient-pole generator.

The 2013 Mw 6.2 Shonbeh (Kaki) earthquake (Zagros-Iran): seismo-tectonic implications for the Kazerun shear transition zone from high-resolution aftershock and InSAR data analysis

SUMMARY The aim of this study is to investigate the aftershock sequence data recorded by a dense temporary seismological network deployed in the epicentral area of the 2013 April 9 Shonbeh (Kaki) earthquake, located in the south of the Simply Folded Belt of the Zagros (Iran). For a comprehensive understanding, coseismic displacements of the Shonbeh earthquake have been investigated using Interferometric Synthetic Aperture Radar (InSAR) data. The epicentral distribution of high-resolution relocated aftershocks shows NW–SE and N–S trending of seismicity. The aftershocks are confined between ∼3 and ∼14 km depth, which implies that the rupture occurred mostly within the sedimentary cover beside the fault parameters retrieved from InSAR modelling. Projection of precisely located aftershocks on NE-oriented section and InSAR ground displacement data are consistent with both NW-trending NE- and SW-dipping fault segments. We observe a NNW–SSE right-lateral strike-slip motions that accommodate oblique convergence and differential motion between the North and Central Zagros. The spatial pattern and focal mechanisms of aftershocks are consistent with a distributed deformation between NW–SE trending reverse and N–S trending right-lateral strike-slip fault segments in the south of the Kazerun transition zone that accommodates a wide shear zone.

The latest activity of the slope fault zone (Pearl River Mouth) in northern South China Sea and implications for earthquake hazard assessments

In the northern region of the South China Sea, located at the intersection of the continental shelf and slope, there have been recorded four earthquakes with magnitudes M ≥ 6.0. This pattern of seismic activity suggests the presence of a previously unidentified active submarine fault. Notably, no active faults have been documented in this area prior to our investigation, with earlier studies predominantly focusing on the structural morphology and sedimentary basin evolution associated with the South China Sea's spreading phenomenon. Through the examination of seismic profiles and historical earthquake records, we have identified a newly discovered fault, termed the Slope Fault Zone (SFZ). The SFZ exhibits diverse structural components, including a listric normal fault in the western sector and a strike-slip fault zone in the eastern sector. Analysis of shallow seismic profiles and borehole data has revealed that the SFZ intersects with strata from the late Pleistocene epoch, confirming its classification as an active fault. The derived fault parameters indicate that the western segment of the SFZ has the potential to generate earthquakes of considerable magnitude, ranging from M 6.7 to 7.2. Furthermore, given the significant occurrence of submarine landslides along the southern boundary of the SFZ, there exists a risk that such seismic events could trigger slope failures and subsequent tsunamis. In summary, our research has unveiled the presence of an active fault capable of precipitating submarine earthquakes, landslides, and tsunamis.

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (Mw≤5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the Mw=3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.