Fault Location Research Articles

A Model-Driven Approach to Extract Multi-Source Fault Features of a Screw Pump

Screw pumps’ faulty working conditions affect the stability of oil production. At project sites, different sensors are used simultaneously to collect multi-dimensional signals; the data fault labels and location are not clear, and how to comprehensively use multi-source information in effective fault feature extraction has become an urgent issue. Existing diagnostic methods use a single signal or part of a signal and do not fully utilize the acquired signal, which makes it difficult to achieve the required accuracy of diagnostic results. This paper focuses on the model-driven approach to extract multi-source fault features of screw pumps. Firstly, it constructs a fault data model (FDM) by analyzing the fault mechanism of the screw pump. Secondly, it uses the FDM to select an effective data set. Thirdly, it constructs a multi-dimensional fault feature extraction model (MDFEM) to extract featured signal features and data features, for which we also comprehensively used multi-source signals in effective fault feature extraction, while other traditional methods only use one or two signals. Finally, after feature selection, unsupervised fault diagnosis was achieved by using the k-means method. After experimental verification, the method can comprehensively use multi-source information to construct an effective data set and extract multi-dimensional, effective fault features for screw pump fault diagnosis.

Enhanced and precise traveling wave fault location accuracy on transmission lines utilizing dual time of arrival

Enhanced and precise traveling wave fault location accuracy on transmission lines utilizing dual time of arrival

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm

Active Distribution Network Fault Location Based on Petri Nets and Improved Particle Swarm Optimization Algorithm

High-impedance fault location method using guessed fault resistance

Electromagnetic time reversal fault location method requires the placement of observation points at a single location and is effective for searching fault location. Among the various existing metrics, the transfer function correlation metric emerged as the most robust. However, its application to high-impedance faults also presents challenges. This difficulty can be attributed to the unknown real fault resistance in the direct-time process, where the guessed fault resistance is typically set to 0 during the reversed-time process of the electromagnetic time reversal fault location method. Consequently, this practice yields comparable direct-time and reversed-time transfer functions for low-impedance faults. Conversely, high-impedance faults exhibit significant discrepancies between direct-time and reversed-time transfer functions, leading to errors in fault location. To overcome this challenge, this paper proposes a location method based on specific guessed fault resistance preset during the reversed-time process. First, the direct-time voltage and reversed-time current transfer functions under high-impedance faults were derived in the frequency domain. Subsequently, by comparing transfer function differences when fault resistances in the direct-time and reversed-time processes did not align, a principle for selecting the guessed fault resistance was established. Additionally, the similarity of the two transfer functions under specific guessed fault resistances was investigated. Furthermore, the symmetry of the fault current was leveraged to reflect the correlation between the two transfer functions, and the differential fault current symmetry coefficient metric was introduced. A fault location time-domain algorithm based on guessed fault resistance was proposed, and its effectiveness was demonstrated through both reduced-scale and simulation experiments. The results indicate that the proposed method is not only applicable to complex lines but also offers a straightforward calculation approach, facilitating fault location under high-impedance faults.

A Systematic Exploration of Mutation‐Based Fault Localization Formulae

ABSTRACTFault localization (FL) aims to automatically find the location of bugs in a software program. In the family of FL approaches, spectrum‐based fault localization (SBFL) is the most widely used and has been extensively studied, which computes the suspicious scores of a code element to be buggy via test coverage information. Mutation‐based fault localization (MBFL) further analyses the relationship between mutants and test results. Although MBFL involves more information, it also faces the following limitations. (1) To precisely evaluate suspicious sources, SBFL approaches proposed dozens of formulae based on coverage, while only a few of them have been adopted by MBFL. (2) The current MBFL approaches are based on the assumption that the capability in localizing bugs of each mutant is the same, so they assign the same weight to the mutants that change the test outputs and consider the mutant from the same code element equivalent. In this paper, we intend to enrich the MBFL family by the following two approaches. First, we collect 25 typical SBFL formulae and transform them into MBFL versions. Second, we propose new assumptions that the mutants should have different weights in computing suspicious sources and propose BLMu, a novel MBFL approach that treats mutants differently. We also propose novel metrics for MBFL by considering the high cost of mutation analysis. We perform large‐scale experiments by evaluating all the MBFL approaches against 395 real‐world Java bugs of the Defects4J benchmark. Our evaluation results reveal that BLMu demonstrates a substantial improvement over both MUSE and Metallaxis, the most popular MBFL approaches, at both the statement level and the method level. Specifically, in terms of Top‐1, BLMu improves by 106% at the statement level and 69% at the method level. When compared with other types of FL approaches, MBFL outperforms typical SBFL approaches while still far behind the state‐of‐the‐art learning‐based FL approaches.

Development of a methodology for diagnosing faults in bearings operating under variable operating conditions based on self-supervised learning

Predictive maintenance plays a crucial role in ensuring the efficiency and availability of industrial assets. Bearings, essential components in rotating machinery, are subject to diverse and complex operating conditions, necessitating advanced fault diagnosis methods. Traditional diagnostic approaches often rely on supervised learning, which requires extensive labeled datasets, a process that is both costly and impractical under varying conditions. This work proposes a novel methodology for diagnosing bearing faults using self-supervised learning, which leverages unlabeled data to generate useful representations for fault detection. The proposed approach aims to develop an end-to-end system that processes raw vibration signals to accurately diagnose the current state of bearings, including fault detection, localization, and severity assessment. The methodology is validated using experimental data from a test rig simulating various fault conditions and will be further tested on real industrial machinery. This research contributes to the development of more efficient and generalizable diagnostic tools for rotating machinery, particularly under variable operational conditions.

Only you can check my data: certificateless and designed-verifier auditing of multi-cloud storage with faults localization

Only you can check my data: certificateless and designed-verifier auditing of multi-cloud storage with faults localization

Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies

Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.

Underground XLPE coaxial cable length calculation and fault detection based on broadband impedance spectroscopy

Abstract Accurately determining the tested cable's total length is important in cable fault detection and localization. Therefore, an iterative method of relative propagation coefficients based on broadband impedance spectroscopy (BIS) is proposed to solve the actual length of the cable and a phase difference integral transform method for fault detection. First, the overall detection process framework is designed. Then, the cable distribution parameter model and the characteristics of the input impedance spectroscopy are analyzed. The calculation methods for determining the cable length and propagation coefficients are explained, followed by a demonstration of the fault localization process. Finally, the model LCR1000A impedance analyzer is used to measure cable length and actual faults in cables with lengths of 35 m, 100 m, and 500 m. The final fault location error is less than 0.67%, proving that the method can calculate the length of cables and various fault point locations.

Study of conveyor belt deviation detection based on improved YOLOv8 algorithm

Conveyor belt deviation is a commmon and severe type of fault in belt conveyor systems, often resulting in significant economic losses and potential environment pollution. Traditional detection methods have obvious limitations in fault localization precision and analysis accuracy, unable to meet the demands of efficient and real-time fault detection in complex industrial scenarios. To address these issues, this paper proposes an improved detection algorithm based on YOLOv8, aiming to achieve efficient and accurate detection during the operation of the belt. Firstly, an Enhanced Squeeze-and-Excitation (ESE) module is incorporated into C2f to boost feature extraction for rollers and belts. Secondly, the construction of the BiFPN_DoubleAttention module in the neck network enhances bidirectional feature fusion and attention mechanism, thereby improving multi-scale object localization accuracy under complex environments. Then, a Multi-Head Self-Attention (MHSA) mechanism is introduced in the head network, better capturing positional features of small roller targets and belt areas in various environments, thus enhancing detection performance. Finally, extensive experiments are conducted on a self-built dataset, achieving an accuracy of 98.1%, mAP0.5 of 99.0%, and a detection speed of 46 frames per second (FPS). This method effectively handles variations and disturbances in the conveyor belt transportation environment, meeting real-time diagnostic needs for belt misalignment in the industry.

Switch monitoring algorithm for 220 kV terminal substation startup process based on multi time scale graph model

The switch monitoring in the startup process of 220 kV terminal substation is a dynamic and changeable process, which has obvious characteristics of diversified scales in time, so as to solve the problem of switch monitoring under multivariable and multi-scale interference. The switch monitoring framework for the startup process of 220KV terminal substation is built. The process layer uses the graphic configuration software and combines the internal and external models of the substation to build the objective function for the generation of the substation graphic model. The particle swarm optimization algorithm is used to solve it to generate the optimal substation graphic model. Through the online monitoring device, the signals such as sensors, normally open and normally closed contacts are collected in real time, and the status of the switchgear is obtained through processing, the reduced half trapezoidal cloud model multivariable multi-scale sample entropy similarity tolerance criterion is used for softening treatment, and the multivariable multi-scale cloud sample entropy is determined to achieve the extraction of multiple time scale switch fault feature vectors, which are used as the input of the SE-DSCNN fault diagnosis model. Combined with the substation diagram, the switch fault identification during the startup process of the substation is realized, and the fault switch position is located. The experimental results show that the algorithm can accurately generate the substation model, which includes all the equipment in the substation, accurately describes the connection relationship and operation status of the equipment, and has high accuracy, integrity and aesthetics; The algorithm can effectively extract and analyze the MMCES entropy characteristics of different types of switch faults; The algorithm can realize the switch monitoring during the startup of substation C, and determine the fault switch and fault location.

Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model

The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.

Multidisciplinary Framework for Vibration-Based Gear Fault Diagnosis Using Experiments, Modeling, and Machine Learning

Vibration-based gear diagnosis is crucial for ensuring the reliability of rotating machinery, making the monitoring of gear health essential for preventing costly downtime and optimizing performance. This study proposes a multidisciplinary framework to enhance gear diagnosis, that aligns with the new era of digital twins by integrating experiments, dynamic modeling, physical preprocessing, and machine learning. Within this framework, we focus on three core procedures: domain adaptation to reduce discrepancies between measured data and synthetic data generated by dynamic models; physical preprocessing, grounded in in-depth investigations dictating signal processing and feature engineering techniques; and learning algorithms, encompassing the process of training AI-based models. We demonstrate this framework through a comprehensive case study of localized tooth fault diagnosis, using controlled-degradation tests and realistic simulations. First, we detect faults using unsupervised learning algorithms; then, we use zero-shot-learning for classifying between localized and distributed faults; finally, we adopt a one-shot-learning strategy for severity estimation. Above all, this hybrid framework bridges the gaps between physical-based and AI-based approaches by combining physical knowledge and advanced algorithmics with machine learning. This contributes to the PHM field by offering valuable insights into integrating different aspects of research, thereby enhancing performance in gear diagnosis tasks.

Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration

INTRODUCTION: Fault detection in transmission lines is critical for keeping the grid stable and reliable. This research offers a new methodology, the Wavelet Transform-Enhanced Random Forest Fault Classification System with STATCOM Integration (WERFCS-SI), to solve the shortcomings of existing fault detection approaches. OBJECTIVES: The integration of STATCOM-compensated transmission lines improves fault detection capabilities. The Wavelet Transform finds faults by analysing approximation and detail coefficients, allowing for multiresolution analysis and exact fault localisation. METHODS: Feature selection approaches, such as information gain, are used to discover and keep relevant features, increasing classification accuracy. RESULTS: Due to its ability to process complex, high-dimensional data and identify minute feature connections, Random Forest (RF) is utilised for classification tasks. The proposed approach improves RF model performance while maintaining precision. CONCLUSION: The integrated technique simplifies fault categorisation, increasing accuracy and efficiency by detecting problems in the transmission line system.

Study on the accurate detection method of full‐polarimetric ground‐penetrating radar faults in mines based on modified Yamaguchi decomposition

AbstractObtaining information on tectonic tendencies is a prerequisite for intelligent and accurate mining in mines. In the special mine environment, the co‐polarized ground‐penetrating radar can only identify the spatial location of faults, and it is difficult to analyse the inclination information of fault structures. This paper proposes a mine full‐polarimetric ground‐penetrating radar fault tendency detection method based on this. First, based on the stacking characteristics of the coal depositional, this paper analyses the propagation law of the pulse electromagnetic wave in the coal seam and puts forward the assumption of the overlapping echo reflection of the fault structure. The reasonableness of the fault reflection assumption is verified through a numerical simulation study. Second, based on the cutting relationship of the fault to the coal seam, we divided the reflection structure of the fault structure into plane scattering and dihedral angle scattering. We realized the mingled echoes’ decomposition using the improved Yamaguchi decomposition technique. To analyse the applicability of the modified Yamaguchi and Freeman decomposition methods in the identification of fault inclination, we use the upright fault simulation data for the discussion, and we find that the improved Yamaguchi decomposition method is more advantageous in the identification of fault inclination in the mine. The decomposition results based on the simulation data of fault models with different dip angles found that when the dip angle of the fault is less than 90°, the scattering of the fault structure is dominated by planar scattering and dihedral angle scattering; when the dip angle of the fault is greater than or equal to 90°, the scattering of the fault structure is dominated by planar scattering, and the scattering power of the dihedral angle model is zero. By analysing the effect of fault strike on the decomposition results, it is found that the fault strike angle has little effect on the identification of fault tendency. Finally, the application potential of this paper's method is tested by constructing complex numerical models and probing experiments. Therefore, the method proposed in this paper can solve the fault tendency identification under a thick coal seam.

An adaptive distance protection scheme considering fault resistance, injected current, and structural changes in the power system

AbstractFaults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines (TLs). Distance relays (DRs), which protect TLs, detect faults, estimate their location, and send the required commands. However, these relays may experience mis‐detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance (FR), and injected currents from remote line terminals. To address this challenge, an innovative adaptive protection scheme that considers FR, changes in network topology, and injected current from the opposite end of the line is proposed. By estimating the equivalent circuit impedances (ECIs) of the network connected to the terminal of the TL, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39‐bus standard network) show that the proposed scheme accurately estimates fault location (FL) and FR with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones ( and ).

Method for Locating Secondary Circuit Faults in Substations Based on Graph Neural Networks

To improve the accuracy and portability of fault location in secondary circuits of smart substations, a fault location method for secondary circuits based on graph neural networks is proposed. First, a fault analysis of the secondary circuits in smart substations is conducted, and a graph database model of the secondary circuits is constructed, revealing the connection relationships between the secondary devices. Then, the fault location problem in secondary circuits is defined as a graph classification problem, and a fault graph generation model is proposed based on a representation method for fault information in secondary circuits. Finally, a fault location model for secondary circuits based on graph neural networks is constructed, and a performance optimization method based on binary cross-entropy is proposed to achieve accurate fault location in substation secondary circuits. Using the secondary circuits of a 220 kV smart substation as a reference, different case studies of secondary circuit faults are generated by altering the network topology, connection relationships, and network configurations through the fault graph generation model. Experiments comparing the proposed location method with other models show that this method has higher location accuracy and robustness.

An ultra-fast MMC-HVDC fault location algorithm based on transient voltage features and regression neural network

An ultra-fast fault location algorithm based on the single-ended transient voltage features and regression neural network (RNN) is proposed, which utilizes 2.5 ms postfualt data window and is suitable for modular multilevel converter-based high-voltage DC (MMC-HVDC) grids equipped with quick-action protections and hybrid DC circuit breakers (HDCCBs). Firstly, the analyses based on the lumped RLC equivalent circuit demonstrate that the delay time, the first negative peak time and its value all have exact relationships with the fault location. Nevertheless, considering the actual parameters and topology of the MMC-HVDC grid, three features can only be approximately extracted. Thus, RNN is utilized to estimate fault locations. 2.02 × 104 distinct fault cases validate the algorithm’s high accuracy across all fault locations and transition resistances up to 1005 Ω. It can well tolerate reasonable deviations of line parameter and current limiting reactor value, as well as 40-dB white noise. Besides, it also has remarkable adaptability, and can be suitable for different systems.

A Two-stage coordinated restoration scheme of hybrid AC/DC distribution grid considering cold load pickup and resilience enhancement

The ever-increasingly severe weather events have elevated the quest for resilience in distribution grids. Cold load pickup (CLPU), a common occurrence in buildings with thermostatically controlled loads (TCLs), generates a significant peak power demand when loads restart. With widespread TCLs distribution, the restoration speed and power level could be impacted by the conventional grid restoration scheme due to limited distribution generator (DG) capability and power supply paths. In this context, this paper proposes a two-stage coordinated restoration scheme based on the novel hybrid AC/DC distribution grid, encompassing the grid configuration level, information interaction level, and designed restoration flow. The typical delayed exponential model is used to characterize CLPU properties during extended outages. In the 1st stage, the contained coordinated restoration strategy decides the optimal load restoration sequence with CLPU concerned. Then, the grid loss optimization is carried out in stage 2 to generate the proper power reference for DGs and voltage source converters (VSCs) of hybrid grids. In case studies, four types of heterogeneous buildings with varied CLPU characteristics are deployed in the analyzed grid. It is verified that the proposed scheme could make effective aggregation and dispatching for multiple DGs, achieving an additional 11.3 hours of total load support, a 16.5% increase of DG utilization and an 11.7% enhancement of the resilience index compared to the conventional restoration scheme. Furthermore, this scheme demonstrates adaptability for resilience improvement under varied temperatures and fault locations.

Can Large Strains Be Accommodated by Small Faults: “Brittle Flow of Rocks” Revised

AbstractBrittle deformation in the upper crust is thought to occur primarily via faulting. The fault length‐frequency distribution determines how much deformation is accommodated by numerous small faults versus a few large ones. To evaluate the amount of deformation due to small faults, we analyze the fault length distribution using high‐quality fault maps spanning a wide range of spatial scales from a laboratory sample to an outcrop to a tectonic domain. We find that the cumulative fault length distribution is well approximated by a power law with a negative exponent close to 2. This is in agreement with the earthquake magnitude‐frequency distribution (the Gutenberg‐Richter law with b‐value of 1), at least for faults smaller than the thickness of the seismogenic zone. It follows that faulting is a self‐similar process, and a substantial fraction of tectonic strain can be accommodated by faults that don't cut through the entire seismogenic zone, consistent with inferences of “hidden strain” from natural and laboratory observations. A continued accumulation of tectonic strain may eventually result in a transition from distributed fault networks to localized mature faults.