Fault Model Research Articles

Dynamic Rupture of the 2021 MW 7.4 Maduo Earthquake: An Intra‐Block Event Controlled by Fault Geometry

AbstractThe 2021 MW 7.4 Maduo event occurred within the Bayan Har block in eastern Tibet, which provides an opportunity to investigate the stress conditions and rheology of faults within the block. Here, we perform dynamic rupture simulations based on the finite element method to explore the physical conditions underlying this earthquake and the factors that controlled the rupture process. We construct the model with a nonplanar fault inferred from the Interferometric Synthetic Aperture Radar (InSAR) data and aftershocks sequence relocation. Our dynamic model is controlled by slip‐weakening friction law with initial stress on fault resolved from a uniform regional stress field. The preferred model produces an average slip of ∼2.2 m with a maximum slip of ∼4.0 m. There are three asperities distributed along the strike, which have captured the main features of the Maduo event. The simulation results are consistent with the static GPS coseismic surface displacements, InSAR data, and displacement waveforms recorded by high‐rate GNSS stations. By comparing the results with the planar fault model and rotated stress fields, we find that the fault geometry and regional stress field are the primary factors that control the rupture process of the event. Moreover, we infer that the unfavorable orientation and fault bend lead to minor slips on the branch fault. Furthermore, we investigate the potential mechanisms of supershear rupture on the eastern fault segment.

Novel Generic Fault Model Considering Fundamental and PWM Current Components of PM Machines with Inter-Turn Short-Circuit

Novel Generic Fault Model Considering Fundamental and PWM Current Components of PM Machines with Inter-Turn Short-Circuit

Efficiency Enhancement Using Fault-Tolerant Sliding Mode Control for the PMVG-Based WTS Under Actuator Faults

This study focuses on efficiency enhancement using fault-tolerant sliding mode control (SMC) for permanent magnet vernier generator (PMVG)-based wind turbine system (WTS) under actuator faults. To do this, the dynamical model of the WTS is constructed with the actuator fault model. Next, the optimum tip speed ratio-based SMC method is proposed to calculate the optimum speed reference of the maximum power point tracking controller that achieves the maximum power capture of the WTS. And then, to effectively track the optimum rotor speed under actuator faults, a disturbance attenuation-based sliding surface is proposed with a novel time-varying exponential reaching law. At the same time, the stability conditions of the SMC are also derived using the appropriate Lyapunov function. Finally, the resilient performance and superiority of the proposed control method are demonstrated through simulation for 5-kW PMVG-based and, 4.8-MW benchmark WTSs and its applicability is proven through an experimental prototype for the 5-kW PMVG-based WTS.

Diagnosis of Oil Drilling Permanent Magnet Synchronous Motor Inter-turn Short Circuit Under Physical Fault Model Training Sample

Diagnosis of Oil Drilling Permanent Magnet Synchronous Motor Inter-turn Short Circuit Under Physical Fault Model Training Sample

Review of fault models for reproducing and predicting strong ground motions and permanent displacements—findings in the 2016 Kumamoto earthquake

AbstractFor accurate evaluation of the impact of earthquakes to structures in the vicinity of the earthquake faults, it has been required to develop appropriate calculation methods for short‐ and long‐period ground motions and permanent displacements. The 2016 Kumamoto, Japan, earthquake provided these kinds of records, and several research papers have been published to propose methods for reproducing or predicting them. I reviewed these papers and summarized probable methods for evaluating fault parameters for broadband‐period ground motions in areas very close to the faults.

Dynamic Analysis and Neural-Adaptive Prescribed-Time Control of the FO Memristive Magnetic-Field Electromechanical Transducer.

This article is concerned with dynamic analysis and neural-adaptive prescribed-time control of the magnetic-field electromechanical transducer incorporating a memristor. First, a fractional-order (FO) mathematical model is developed, which comprehensively characterizes fractional properties of various dielectrics and establishes the relationship between magnetic flux and electric charge. The dynamical analysis explores internal evolution and complexity performance concerning a single factor or double factors among the FO, system parameter, and memristor configuration by the Bifurcation diagram, sample entropy, and C0 complexity from multiple perspectives. Subsequently, a neural-adaptive prescribed-time control scheme is proposed to transform detrimental chaotic oscillations into orderly motions, achieve the pregiven tracking precision and accommodating both actuator fault and system uncertainty. The controller design consists of three key steps: 1) a deferred constraint function is imposed on the tracking error starting from anywhere to get assignable tracking precision within a specified time, ensuring collision avoidance; 2) a type-2 fuzzy wavelet neural network (FWNN) is utilized effectively to handle parameter perturbations and system uncertainties; and 3) a second-order FO tracking differentiator (TD) is utilized to address the "explosion of complexity" of traditional backstepping under actuator fault model. It is shown that the proposed scheme is able to ensure the boundness of all signals of the closed-loop system. Finally, extensive simulation experiments are conducted to validate the effectiveness and robustness of the rendered scheme.

A Bayesian Source Model for the 2022 Mw6.6 Luding Earthquake, Sichuan Province, China, Constrained by GPS and InSAR Observations

Until the Mw 6.6 Luding earthquake ruptured the Moxi section of the Xianshuihe fault (XSHF) on 5 September 2022, the region had not experienced an Mw >6 earthquake since instrumental records began. We used Global Positioning System (GPS) and Sentinel-1 interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation and constrain the location and geometry of the seismogenic fault using a Bayesian method We then present a distributed slip model of the 2022 Mw6.6 Luding earthquake, a left-lateral strike-slip earthquake that occurred on the Moxi section of the Xianshuihe fault in the southwest Sichuan basin, China. Two tracks (T26 and T135) of the InSAR data captured a part of the coseismic surface deformation with the line-of-sight displacements range from ∼−0.16 m to ~0.14 m in the ascending track and from ~−0.12 m to ~0.10 m in the descending track. The inverted best-fitting fault model shows a pure sinistral strike-slip motion on a west-dipping fault plane with a strike of 164.3°. We adopt a variational Bayesian approach and account for the uncertainties in the fault geometry to retrieve the distributed slip model. The inverted result shows that the maximum slip of ~1.82 m occurred at a depth of 5.3 km, with the major slip concentrated within depths ranging from 0.9–11 km. The InSAR-determined moment is 1.3 × 1019 Nm, with a shear modulus of 30 GPa, equivalent to Mw 6.7. The published coseismic slip models of the 2022 Luding earthquake show apparent differences despite the use of similar geodetic or seismic observations. These variations underscore the uncertainty associated with routinely performed source inversions and their interpretations for the underlying fault model.

A Comprehensive Fault-System Inversion Approach: Methods and Application to NSHM23

ABSTRACT We present updated inversion-based fault-system solutions for the 2023 update to the National Seismic Hazard Model (NSHM23), standardizing earthquake rate model calculations on crustal faults across the western United States. We build upon the inversion methodology used in the Third Uniform California Earthquake Rupture Forecast (UCERF3) to solve for time-independent rates of earthquakes in an interconnected fault system. The updated model explicitly maps out a wide range of fault recurrence and segmentation behavior (epistemic uncertainty), more completely exploring the solution space of viable models beyond those of UCERF3. We also improve the simulated annealing implementation, greatly increasing computational efficiency (and thus inversion convergence), and introduce an adaptive constraint weight calculation algorithm that helps to mediate between competing constraints. Hazard calculations show that ingredient changes (especially fault and deformation models) are the primary driver of hazard changes between NSHM23 and UCERF3. Updates to the inversion methodology are also consequential near faults in which the slip rate in UCERF3 was poorly fit or was satisfied primarily using large multifault ruptures that are now restricted by explicit b-value and segmentation constraints.

Including stress relaxation in point-process model for seismic occurrence

SUMMARY Physics-based and statistic-based models for describing seismic occurrence are two sides of the same coin. In this paper, we compare the temporal organization of events obtained in a spring-block model for the seismic fault with the one predicted by probabilistic models for seismic occurrence. Thanks to the optimization of the parameters, by means of a Maximum Likelihood Estimation, it is possible to identify the statistical model which fits better the physical one. The results show that the best statistical model must take into account the non-trivial interplay between temporal clustering, related to aftershock occurrence, and the stress discharge following the occurrence of high magnitude main shocks. The two mechanisms contribute in different ways according to the minimum magnitude considered in the data fitting catalogue.

Research on neural network-based fault diagnosis and prediction method for power communication equipment

Abstract In this paper, facing the digital development of the power grid and the status quo of massive power communication equipment access and targeting the demand for highly intelligent operation and maintenance management of the power grid, combined with neural network technology, we propose an intelligent diagnosis model of power communication equipment faults. Adopting BERT as the vector embedding layer to obtain the vector sequence of fault text, designing a fault entity recognition model for power communication equipment based on BERT-BiGRU-CRF, and completing the construction of the relationship set of fault text. The proposed knowledge graph-based power communication equipment fault intelligent diagnosis model combined with the WBLA-based power communication equipment fault severity level recognition algorithm to obtain different severity fault information, from which a TFIDF-COS-based power communication equipment fault intelligent diagnosis algorithm is designed to realize intelligent diagnosis of power communication equipment faults. After testing, the TFIDF-COS algorithm can get the best optimization effect when the number of hidden layers of the selected algorithm is 1, and the initial learning rate is 0.05, and its accuracy rate can be kept above 98%. Compared with the traditional fault diagnosis system, in terms of the order of magnitude 100M, 500M, 1G, and 5G, the running time is reduced by 322s, 1874s, 4617s, and 7467s, and the accuracy rate is increased by 2.33%, 2.6%, 32.02%, and 61.4%, respectively. Therefore, this paper realizes the accurate positioning of power communication equipment faults and provides technical support for intelligent operation and maintenance of the power grid.

Study on Evolution Characteristics of Stress Field of Stepped Fault

With the development of mining to the deep, the ground stress increases correspondingly, and the geological conditions become more and more complicated. Mine dynamic disasters mainly occur near faults. Numerous studies have been conducted on single faults, but few studies have been done on stepped faults formed by double faults under complex geological conditions. Based on this, with the 3110-working face of Zhong Tai Mining in He bi as the engineering background, Single fault model a and different step fault models b, c, d, e and f were established by Flac3D numerical simulation software. The characteristics of stress field evolution of single fault and step fault under the influence of deep mining are studied. The results show that the stress distribution in the upper wall of the fault forms a stress concentration zone, and in the lower wall forms a stress weakening zone. Under the influence of excavation disturbance, the vertical fault distance and the horizontal distance between faults will affect the stress evolution characteristics of the stepped fault.

Autonomous selection of the fault classification models for diagnosing microservice applications

Microservices architecture is a new approach for deploying applications and services in the cloud, gaining popularity for constructing large-scale systems that are highly resilient, robust, and adaptable to dynamic customer needs. Given that the number of services in a microservice application can be in the hundreds or thousands, the complex dependencies between them can cause faults to propagate and affect multiple services, ultimately leading to degraded system performance. To run microservices reliably and with high uptime, it is essential to quickly troubleshoot when a fault occurs. Simultaneously, there is a need for unsupervised fault diagnosis methods, as existing supervised methods are usually time and labor-intensive. As a solution to these challenges, this paper proposes the Autonomous Selection of Fault Classification models (ASFC) for Diagnosing Microservice Applications. The method can automatically select optimal models from candidate unsupervised detection models for different faults and cascade them to perform fault diagnosis. Meanwhile, our method can determine the root cause of the fault by conducting a root cause localization of faulty services. Experiments on two widely used benchmark microservices demonstrate that ASFC outperforms the baseline methods. As shown in the experimental results, our method achieves an average macro-F1 of 82.9% and 88.6% on two benchmark microservices, respectively, with an average improvement of 34.4% and 24.7% over baseline methods. Furthermore, the method achieves high accuracy in the localization of the root causes, with an average Avg@5 of 0.857 and 0.525 on two benchmark microservices.

An adaptive voltage control compensator for converters in DC microgrids under fault conditions

This paper proposes a voltage compensator for converters in multi-converter DC microgrids (MG), which enhances the DC-MG behavior under fault conditions. Among the four existing categories in the classification of the faults, this paper focuses on those faults that affect voltage generation of converter-based distributed generation units (DGUs). Such faults conceptually occur from the terminal voltage reference to actual voltage generated by DGUs. To compensate the adverse effects of the faults, an adaptive scheme is proposed, which considers the most general case of the faults by using the time-varying multiplicative and additive fault models. The proposed scheme benefits from an integrated structure, which does not require separate fault detection, isolation, and identification blocks. As an advantage of the proposed approach, it is designed independent of the primary and secondary controllers of DGUs, and hence its performance is not affected by using different primary and secondary controllers. The effectiveness of the proposed scheme is verified under various fault conditions and uncertainty in the system parameters. Using the proposed scheme, the results show that the performance of system under the fault condition is similar to that of the normal operation.

Research on fault diagnosis strategy of air-conditioning system based on signal demodulation and BPNN-PCA

An efficient fault diagnosis strategy is crucial for the operation of air conditioning systems. In this paper, a novel fault diagnosis strategy based on the signal demodulation method and deep learning model is proposed for a small sample set's fault diagnosis. The potential application of the novel method in air conditioning systems has been discussed through three fault experiments and model evaluation indicators. The signal demodulation method based on principal component analysis (DPCA) is applied in the field of image enhancement innovatively. Compared with the time-domain sample set, the DPCA sample set has stronger features and higher discrimination. The correct rate of the model using the DPCA sample set has been improved by 10.44 %, the loss rate has been reduced by 34.68 %, and the running time has been reduced by 57.13 %. The Back Propagation Neural Network- Principal Component Analysis (BPNN-PCA) model is applied to air conditioning fault diagnosis. Compared with the traditional BPNN and the CART model, the BPNN-PCA model has better fault diagnosis performance and computational efficiency. Through the test and the model performance evaluation indicators, the model optimization strategy has been proposed, and its effectiveness has been verified. This lays the foundation for the optimization and improvement of the model.

Seasonally hydrological load and background seismicity in the intraplate collision zone, the Longmen Shan, China

SUMMARY Hydrological modulation of background seismicity provides a way to understand the seismogenic process and interaction mechanism between the hydrosphere and lithosphere. To elucidate the hydrological modulation of background seismicity in the Longmen Shan, the monthly spatio-temporal distribution of terrestrial water storage (TWS) variations is inverted from the seasonal term of continuous GNSS (Global Navigation Satellite System) data and compared with monthly precipitation. In the Longmen Shan, monthly TWS variations are in phase with precipitation variations in the northern area but lag precipitation variation by 2 months in the southern area. The annual amplitude of the inverted TWS is about 0.2 m, which is consistent with the amplitude of precipitation. To investigate the stress perturbations in the crustal interior induced by the hydrological load, monthly Coulomb failure stress (CFS) variation caused by TWS and precipitation load is calculated on a simplified fault model of the Longmen Shan, respectively. The maximum CFS variations of TWS and precipitation are about 2.4 kPa. To obtain the background seismicity, different earthquake declustering methods are employed on a two-decade earthquake catalogue (M ≥ 2.5). The declustered results are subjected to null hypothesis tests, including assessing whether the earthquake distribution follows a homogeneous Poisson distribution in time or exhibits a temporally homogeneous, spatially heterogeneous Poisson distribution in space and time. The only declustered catalogue derived by the epidemic type aftershock sequence model pass all the tests. Furthermore, the monthly CFS variation and its rate caused by TWS and precipitation are compared with the monthly background seismic events. Notably, a correlation is observed between CFS variation rate due to precipitation and background seismicity. This correlation suggests the presence of long-term hydrological modulation of seasonal background seismicity in the Longmen Shan.

Model-based safety analysis with time resolution (MBSA-TR) method for complex aerothermal–mechanical systems of aero-engines

Current aero-engine safety assessments mostly rely on experience-based safety analysis methods such as Fault Tree Analysis (FTA) or Failure Modes and Effects Analysis (FMEA). The progressively significant dynamic characteristics of aero-engines during their transition between states render these methods incapable of providing accurate quantitative safety guidance, which is vital for complex aerothermal–mechanical systems. To solve this problem, a Model-Based Safety Analysis with Time Resolution (MBSA-TR) method was established, which included system modeling, fault modeling, and Safety Critical Parameter (SCP) identification. An integrated system model combining the main gas path and air system was built based on dynamics principles, and it was capable of reflecting the dynamic characteristics of aero-engines. The fault model included several time-resolved sub-modules and interacted bidirectionally with the system model at each time step to realize time-sequential fault propagation. SCPs were defined as parametric analysis objectives, and they were identified using an FTA-like identification framework. Finally, a case study of a typical turbofan engine with low-pressure compressor blade fractures as the primary fault was analyzed. The results showed that some hazard risks are incapable of detection without time resolution. Thus, time resolution is indispensable for the safety evaluation of complex aerothermal–mechanical systems of aero-engines.

RLARA: A TSV-Aware Reinforcement Learning Assisted Fault-Tolerant Routing Algorithm for 3D Network-on-Chip

A three-dimensional Network-on-Chip (3D NoC) equips modern multicore processors with good scalability, a small area, and high performance using vertical through-silicon vias (TSV). However, the failure rate of TSV, which is higher than that of horizontal links, causes unpredictable topology variations and requires adaptive routing algorithms to select the available paths dynamically. Most works have aimed at the congestion control for TSV partially 3D NoCs to bypass the TSV reliability issue, while others have focused on the fault tolerance in TSV fully connected 3D NoCs and ignored the performance degradation. In order to adequately improve reliability and performance in TSV fully connected 3D NoC architectures, we propose a TSV-aware Reinforcement Learning Assisted Routing Algorithm (RLARA) for fault-tolerant 3D NoCs. The proposed method can take advantage of both the high throughput of fully connected TSVs and the cost-effective fault tolerance of partially connected TSVs using periodically updated TSV-aware Q table of reinforcement learning. RLARA makes the distributed routing decision with the lowest TSV utilization to avoid the overheating of the TSVs and mitigate the reliability problem. Furthermore, the K-means clustering algorithm is further adopted to compress the routing table of RLARA by exploiting the routing information similarity. To alleviate the inherent deadlock issue of adaptive routing algorithms, the link Q-value from reinforcement learning is combined with the router status based in buffer utilization to predict the congestion and enable RLARA to perform best even under a high traffic load. The experimental results of the ablation study on simulator Garnet 2.0 verify the effectiveness of our proposed RLARA under different fault models, which can perform better than the latest 3D NoC routing algorithms, with up to a 9.04% lower average delay and 8.58% higher successful delivered rate.

Implications of multi-stage deformation on the differential preservation of Lower Paleozoic shale gas in tectonically complex regions: New structural and kinematic constraints from the Upper Yangtze Platform, South China

The strength of tectonic deformation in tectonically complex regions, particularly multi-stage superimposed tectonic regions, can have a significant impact on shale gas preservation conditions. However, differential tectonic deformation has still not been quantitatively constrained, and its impact on the preservation conditions of shale gas is thus poorly understood in the northern Guizhou Province, southwest China. This study investigates the differential preservation conditions of marine shale gas in four eroded synclines (including Shixi, Daozhen, Zhongguan, and Fuyan Synclines) of the northern Guizhou Province. Interpretation of structures in field outcrops, geometry and kinematic analysis, and typical gas reservoir anatomy are combined to quantitatively explore the effect of superimposed deformation on the preservation conditions of shale gas. According to a set of new structural and kinematic constraints, the Riedel shear model for strike-slip faults is firstly conducted in the eroded synclines in the northern Guizhou Province, which innovatively established a linkage between detailed structural analysis and evaluation of shale-gas preservation conditions. We demonstrate that superimposing of deformations plays a role in differential accumulation in eroded synclines. The results indicate that (1) fault strike in the Shixi Syncline is more concentrated than Zhongguan and Fuyan Synclines, whereas fault strikes in the Zhongguan and Fuyan Synclines are more scattered. There is only one direction of maximum principal stress (σ1) in the Shixi and Daozhen Synclines. There are two maximum principal stress directions (σ1) in the Zhongguan and Fuyan Synclines. The Zhongguan and the Fuyan Synclines have tensional environments, whereas the Shixi and the Daozhen Syncline have compressional settings; (2) the formation dip angle, distance to a class II fracture, average detachment layer thickness, intensity of superimposed modifications, and interlimb angle may be positively correlated with gas content; (3) the progressive transformation pattern is established. These findings lay the foundation for the future research on the differential preservation mechanism of eroded synclines and may be useful for enhancing research on worldwide strike-slip faults controlling hydrocarbon accumulation.

Electrical Resistivity Tomography for Urban Underground Space by COMSOL Multiphysics

The development and utilization of urban underground space are of great significance to urban sustainable development. Geophysical methods have the characteristics of non-destructive detection, and electrical resistivity tomography has an obvious response to underground resistivity structure and can be used in urban underground space exploration. In this paper, COMSOL Multiphysics is used to realize the finite-element simulation of geoelectric models, and electrical resistivity tomography is used to explore resistivity anomalies of karst and fault models in urban underground environments. The results show that electrical resistivity tomography can identify karst and fault distribution effectively, and COMSOL Multiphysics promises to be a powerful tool for exploring urban underground space.

Electrical Power Generator Faults Analysis Using Fault Tree and Bayesian Network

Abstract This paper presents a model to predict Electrical Power Generator (EPG) faults. The fault tree (FT) model is developed and used to help maintenance engineers in fault analysis procedure of this rotating machine. By identifying the main, intermediate and basic events it’s possible to construct the FT with logical reasoning. The top dreaded event is defined. By using a Bayesian network (BN) as a complementary tool, fault prediction of the EPG becomes possible and easy. By using the developed BN, the probability of occurrence of the top event (EPG failure) is calculated. Also, by this approach, we can process complex information that causes system faults in an easy and simple way. The essential elements to do this analysis are the reliable and good exploitation of the information previously stored in the system. The use of the BN in combination with the FT gives the possibility of qualitative and quantitative analysis, diagnosis, and prediction of faults from the same Bayesian model. The flexibility of the proposed BN model in this paper allows better and precise decision making. Also, priorities regarding maintenance job are defined and resources are a priori prepared.