Fault Location Research Articles

Uniqueness of suspiciousness scores: towards boosting evolutionary fault localization

Context. Software is subject to the presence of faults, which impacts its quality as well as production and maintenance costs. Evolutionary fault localization uses data from the test activity (test spectra) as a source of information about defects, and its automation aims to obtain better accuracy and lower software repair cost. Motivation. Our analysis identified that the test spectra commonly used in research exhibit a high ratio of sample repetition, which impairs the training and evolution of Genetic Programming (GP) evolved heuristics. Problem. We investigate whether evolutionary training based on the uniqueness of suspiciousness scores can increase the ability to find software faults (defects), even in repeat-sample scenarios. Specifically, we examine whether the GP-evolved heuristic, which is based on the distinguishability of program elements in terms of faults, is really competitive. Methodology. The investigation formalized hypotheses, introduced three training strategies to guide the research and carried out an experimental evaluation, aiming to reach conclusions regarding the assessment of research questions and hypotheses. Analysis. The results have shown the competitiveness of all the proposed training strategies through evaluation metrics commonly used in the research field. Conclusion. Statistical analyses confirmed that the uniqueness of suspiciousness scores guides the generation of superior heuristics for fault localization.

Influence of annealing on microstructures and mechanical properties of laser powder bed fusion and wire arc directed energy deposition additively manufactured 316L

The annealing response of 316L stainless steel manufactured with both laser powder bed fusion (PBF-LB) and wire-arc directed energy deposition (DED-Arc) was investigated in the context of microstructural evolution and mechanical properties. Because additive manufacturing (AM) comprises several technologies that differ in heat source, feedstock, and scan strategy, among other build variables, the final products can experience a range of thermal histories and defect (dislocation) populations. These unique thermal histories can subsequently influence as-built properties and annealing response of AM materials, most notably those manufactured with different AM deposition processes. The PBF-LB process (with higher cooling rates) yielded finer austenite microstructures having more lattice strain, higher dislocation densities, and higher yield strength values than the DED-Arc 316L process (with lower cooling rates). Electron backscattered diffraction (EBSD) data of the kernel average misorientation (KAM), boundary density, and geometrically necessary dislocation (GND) density support the differences in lattice strain. As a result, the PBF-LB 316L exhibited more recovery and recrystallization after annealing at elevated temperatures above 873 K based on changes to yield strength, work hardening behavior, and microstructure evolution. The DED-Arc 316L exhibited a δ-ferrite/austenite microstructure with microsegregation that influenced deformation mechanisms active after annealing. Tensile data, deformed microstructures and misorientation histograms from EBSD showed a decreasing amount of deformation twinning when comparing the as-built condition to annealed conditions (up to 1473 K/1h) of PBF-LB 316L. The opposite trend was noted in DED-Arc 316L. The behavior was interpreted to be due to the differences in chemical segregation during solidification and the effects of heat treatment on chemical homogenization and local stacking fault energy.

Time-Domain Fault Detection and Location Scheme for Flexible DC Distribution Networks

Accurately detecting and locating the fault point of the DC line is significant for eliminating the fault and restoring the power supply of the flexible DC distribution network as soon as possible. Firstly, a direction pilot protection scheme for a complex DC distribution network is proposed based on the integral of the current superposition to identify the fault direction. Then, an online time-domain fault location method based on the least square (LS) method to solve the overdetermined equations is proposed by analyzing the fault loop circuit after the DC line fault. The proposed location scheme utilizes fault data at both ends of the line to eliminate the theoretical impact of fault resistance, and the calculation of the current difference method is discussed to reduce the location error of whether the fault current-limiting reactor (CLR) exists. Finally, various simulations by PSCAD/EMTDC V4.5 demonstrate that the proposed scheme has high protection reliability and location results after different fault positions and resistances. The proposed scheme has low requirements for the sampling rate, fault data length, and implementation costs, which can meet practical application requirements.

High‐speed algorithm for fault detection and location in DC microgrids based on a novel time–frequency analysis

AbstractProtecting DC microgrids (DCMGs) from faults is critical due to the rapid current changes that occur in milliseconds. However, ensuring fast and accurate protection in DCMGs is more challenging than in AC systems. This study proposes a novel protection algorithm using traveling waves (TWs) for fault detection and localization. The high‐order synchrosqueezing transform (FSSTH) is applied to precisely identify TWs at the relay location. FSSTH offers a sharp time–frequency representation, enhancing the accuracy and speed of fault detection. This method can accurately detect transient phenomena like TWs in DCMGs, even with noise and variable fault resistance. By using the spectral envelope with FSSTH, ridges in time–frequency representations are extracted, improving fault diagnosis. The approach differentiates external from internal faults and recognizes fault direction by assessing TW polarity. Testing on two different DCMGs showed this algorithm's high efficiency and accuracy, with fault location errors ranging from 1 to 50 meters in low‐voltage and 13 to 64 meters in medium‐voltage DCMGs, even under challenging conditions like high resistance (up to 500 Ω) and low signal‐to‐noise ratio (5 dB). These results demonstrate the method's superior accuracy and robustness compared to existing techniques.

Relocation of earthquake clusters show seismogenic transverse structures in the Inner Northern Apennines (Italy)

The Inner Northern Apennines (Italy) are a region with a dominant N-S to NNW-SSE fault system, but dissected and offset by several E-W to NE-SW trending structures and lineaments. The knowledge about the nature of these transverse structures, their origin, activity and role in current tectonic motions is limited and debated. To better establish the location, subsurface shape, and kinematics of faults related to the Livorno-Empoli lineament, one of the major transverse structures in the Northern Apennines, we analysed the seismicity in western Tuscany. In the Viareggio Basin we identified and relocated two distinct earthquake clusters as well as calculated 12 new focal mechanisms. The results show that the clusters consisted of several swarms from the years 2006, 2015, 2016 and 2021. The events had a depth between 2 and 15 km and were located along a NE-SW oriented, SE dipping fault system dissecting the Viareggio Basin. Focal mechanisms show oblique normal slip. We interpret the fault system to form a connection between the Viareggio Basin and the Lucca Basin to the east as well as continuing offshore. The results show that the transversal faults of the Inner Northern Apennines are seismogenic, with the length, position and onshore to offshore nature of the fault suggesting reactivation of pre-existing structures.

Improving protection reliability of series‐compensated transmission lines by a fault detection method through an ML‐based model

AbstractThis article addresses the distance protection challenges associated with the series‐compensated transmission lines and the impact of fault resistance by employing a machine‐learning model. In the proposed model, stacked layers of bidirectional long short‐term memory (Bi‐LSTM) cells are fed by voltage and current signals to distinguish between different fault scenarios. This method takes advantage of only local bus measurements to prevent information leakage in communication channels. Moreover, to make the proposed method harmonics‐robust and improve the correlation interpretation between the features for the Bi‐LSTM model, the 3‐phase raw measurement signals are passed through a discrete Fourier transform (DFT) which extracts their fundamental frequency component magnitudes and angles. Then, an extensive amount of fault scenarios including different compensation levels, fault resistances, and fault locations in normal and power‐swing operational conditions are simulated to train the model. Finally, to validate the performance of the proposed protection method in the series‐compensated transmission lines, distinctive studies are also carried out based on electromagnetic transient simulations. The obtained results confirm the remarkable performance of the proposed method in discriminating fault types, faulty phases, internal or external faults, and normal or power‐swing conditions of the power system.

Data-Driven Dynamic Security Partition Assessment of Power Systems Based on Symmetric Electrical Distance Matrix and Chebyshev Distance

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application.

New approach for accurate discrimination and location of power transformers with different internal winding faults.

Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attached to the transformer. Different types of internal winding faults are intensely considered: partial discharge, inter-disk faults, series and shunt short circuit faults and axial displacement. Depending on the transformer measured output voltage, input voltage and the input current, the construction of a locus diagram (ΔV-Iin) serves as an indicator for any physical modification to the winding. Using five suggested features extracted from the developed locus, an artificial neural network (ANN) technique is applied to accurately distinguish any deviation from the transformer healthy condition. The exact location of each fault inside the windings of power transformer is then determined. The obtained results validate the usefulness of the proposed scheme for different internal faults. The superiority of the proposed scheme is extensively examined by comparing its results with some published schemes.

A fault detection and location algorithm for the LVDC interconnection network in rural area

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

Nonlinear dynamic modeling and vibration analysis for early fault evolution of rolling bearings

In rotating machinery, the condition of rolling bearings is paramount, directly influencing operational integrity. However, the literature on the fault evolution of rolling bearings in their nascent stages is notably limited. Addressing this gap, our study establishes an innovative nonlinear dynamic model for early fault evolution of rolling bearings based on collision impact. Firstly, considering the fault evolution characteristics, the influence of the rolling element and fault structure, the dynamic model of early fault evolution between the rolling element and the local fault is established. Secondly, according to the Hertzian contact deformation theory, a nonlinear dynamic model of rolling bearings expressed as mass-spring is established. Thirdly, the energy contribution method is used to integrate the fault evolution model and the nonlinear dynamic model of the rolling bearing. A nonlinear dynamic model of early fault evolution of the rolling bearing is proposed by using the Lagrangian equation. Comparing the simulation results of the nonlinear dynamic with the experimental results, it can be seen that the numerical model can effectively predict the evolution process and vibration characteristics of the fault evolution of rolling bearings in the early stage.

Bayesian optimized deep Q-network for diagnosing mine ventilation systems windage alteration fault targeting imbalanced data

Fault diagnosis of mine ventilation system is of great significance for mine safety production. Traditional machine learning algorithms have been widely applied in the field of mine ventilation systems windage alteration faults (WAFs) diagnosis, but these algorithms have poor intelligence and weak generalization ability. Therefore, this research constructs a WAFs diagnosis model (WAFs-BODQN) based on Bayesian optimized (BO) deep Q-network (DQN). Meanwhile, in order to solve the problem of poor performance of intelligent models when the dataset is imbalanced, a reinforcement learning reward function (KMeans-SDF) was designed, which combines the KMeans++ algorithm and spatial distance function (SDF). The WAFs-BODQN model with KMeans-SDF reward function has fault location diagnosis accuracies of 98.75 %, 97.50 %, and 95.00 % for mild, moderate, and extreme imbalanced datasets UB1, UB2, and UB3, respectively. This effectively avoids the "eccentricity" phenomenon of the intelligent model on majority class and achieves accurate prediction of fault locations. The accuracy of fault location diagnosis using the WAFs-BODQN model in simulation experiments and on-site empirical applications is higher than 95 %, demonstrating the intelligence, generalization, and feasibility of WAFs-BODQN model in the field of WAFs diagnosis in mine ventilation systems. By comparing the results with traditional machine learning and deep learning fault diagnosis models, it has been proven that the WAFs-BODQN model has advantages in intelligence and generalization ability. It is expected to establish a universal architecture for diagnosing WAFs, providing theoretical guidance and technical support for achieving intelligent mine ventilation.

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.

Fault Location Method of Distribution Network Based on VGAE-GraphSAGE

The distribution network is the main component of the power system, which undertakes the important function of power transmission and distribution. Fast and accurate distribution network fault location is one of the important means to ensure the safe and stable operation of the power system and is helpful in guiding troubleshooting, shortening the power outage time, reducing the workload of manual inspection, and reducing the social and economic losses caused by faults. Due to the development of the new distribution network, the comprehensive influence of many factors has put forward new challenges to the traditional distribution network fault location methods. In this paper, a distribution network fault location method based on the graph neural network is proposed. Firstly, the distribution network is treated as non-Euclidean graph data; secondly, variational graph auto-encoders (VGAE) are used to mine the underlying information of nodes and improve the overall anti-noise performance of the fault location method. Then the GraphSAGE model is used to aggregate the neighbor information of nodes, fully consider the influence of the surrounding lines on the target lines, and improve the output of the model to locate the distribution network line where the fault occurred. The experimental example analysis based on OpenDSS simulation software (version 9.8) proves that the proposed method has high accuracy and anti-interference, and the accuracy reached 97.81%. Moreover, the positioning result is still good in the new intelligent distribution network scenario with distributed power access, with an accuracy of 95.07% in the hybrid power generation scenario.

Co- and postseismic subaquatic evidence for prehistoric fault activity near Coyhaique, Aysén Region, Chile

Abstract. Chilean Patagonia is confronted with several geohazards due to its tectonic setting, i.e., the presence of a subduction zone and numerous fault zones, e.g., the Liquiñe-Ofqui Fault Zone (LOFZ). This region has therefore been the subject of numerous paleoseismological studies. However, this study reveals that the seismic hazard is not limited to these large tectonic structures by identifying past fault activity near Coyhaique in Aysén Region. Mass-wasting deposits in Lago Pollux, a lake located ca. 15 km SW of this region's capital, were identified through analysis of reflection-seismic data and were linked to a simultaneous event recorded in nearby Lago Castor. Furthermore, a coeval ∼50-year-long catchment response was identified in Aysén Fjord based on the multiproxy analysis of a portion of a sediment core. Assuming that this widely recognized event was triggered by an earthquake, ground-motion modeling was applied to derive the most likely magnitude and source fault. The model showed that an earthquake rupture along a local fault, in the vicinity of Lago Pollux and Lago Castor, with a magnitude of 5.6–6.8, is the most likely scenario.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

Moving sound source localization in 3D space based on time delay and energy ratio technique

Moving sound source localization has numerous applications in interdisciplinary realms such as in teleconferencing, in defense, in robotics, and fault localization in vehicles. Localization of moving source is more challenging than localizing stationary sources because the motion disturbs the sound field and manipulates the frequency and amplitude of the acoustic pressure at recording location, which hampers the accuracy of source localization. Existing moving source localization techniques have numerous restrictions such as source trajectory should be known beforehand as a straight line perpendicular to the array geometry, source should be in same plane or parallel to the microphone array plane, and source velocity should be known. Moreover, when the source elevation is high, these techniques give high error. This study proposes the time delay and energy ratio based real-time moving sound source localization technique that is free from above-mentioned restrictions and can track the source's 3D coordinates accurately. Six microphones are used to localize a moving source by processing the time delay and energy ratios between sensor pairs. The root mean square error found for low source speed and high source speed conditions are 0.73 m and 1.8 m respectively.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Fault location method for new distribution networks based on waveform matching of time–frequency travelling waves

AbstractSome existing fault location methods for distribution networks rely too much on the local wave head information of the time or frequency domain signals, making it difficult to adapt to the increasingly complex structure and operating conditions of the distribution network after the new energy access. For improvement, the travelling wave (TW) time and frequency ranges that can effectively avoid waveform distortion caused by new energy access are analysed. The one‐to‐one matching relationship between the TW waveforms in these ranges and the fault positions is revealed. A fault TW time–frequency matrix with specific time and frequency windows is constructed, and a new distribution network fault location method is proposed based on the matching technique of waveform features, which realises the accurate fault location by exploring the proportionality between the cumulative trend of the matrix energy amplitude deviation and the fault point position. Simulation test results show that the proposed method is not affected by the complex structure of distribution networks such as new energy access and overhead‐cable line mixing on fault location and flexibly transforms the fault location problem into a time–frequency TW waveform matching problem, which improves the accuracy and robustness of the fault location for new distribution networks to a degree.

Causal reasoning in Software Quality Assurance: A systematic review

Context:Software Quality Assurance (SQA) is a fundamental part of software engineering to ensure stakeholders that software products work as expected after release in operation. Machine Learning (ML) has proven to be able to boost SQA activities and contribute to the development of quality software systems. In this context, Causal Reasoning is gaining increasing interest as a methodology to go beyond a purely data-driven approach by exploiting the use of causality for more effective SQA strategies. Objective:Provide a broad and detailed overview of the use of causal reasoning for SQA activities, in order to support researchers to access this research field, identifying room for application, main challenges and research opportunities. Methods:A systematic review of the scientific literature on causal reasoning for SQA. The study has found, classified, and analyzed 86 articles, according to established guidelines for software engineering secondary studies. Results:Results highlight the primary areas within SQA where causal reasoning has been applied, the predominant methodologies used, and the level of maturity of the proposed solutions. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain, but other tasks like testing are rapidly gaining popularity. Both causal inference and causal discovery are exploited, with the Pearl’s graphical formulation of causality being preferred, likely due to its intuitiveness. Tools to favour their application are appearing at a fast pace – most of them after 2021. Conclusions:The findings show that causal reasoning is a valuable means for SQA tasks with respect to multiple quality attributes, especially during V&V, evolution and maintenance to ensure reliability, while it is not yet fully exploited for phases like requirements engineering and design. We give a picture of the current landscape, pointing out exciting possibilities for future research.

Inducing alternating magnetic fields for real-time non-contact fault localization within electric energy storage component arrays.

With the wide application of electric energy storage component arrays, such as battery cell arrays, capacitor arrays, and inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot realize rapid, precise, and nondestructive localization of the faulty component within these large-scale arrays, especially for a component with an early stage short-circuit fault. To address this challenge, this paper proposes a magnetic field based method and realizes precise fault localization by inducing an alternating magnetic field from the target array, unlike previous research where a static magnetic field was induced. Through establishing a physical model of the short-circuit component as well as the whole array, a spatial filtering algorithm based on beamforming techniques is utilized to process the measured magnetic field data in real time. Both the simulation and experimental results demonstrate the capability of the proposed method in enhancing the security of electric energy storage component arrays. Within an imaging area of 80 × 80 mm2, the proposed method can accurately locate the faulty component out of a nine-component array, with an error of only 0.72mm for capacitors and 0.91mm for battery cells.