Fault Model Research Articles

Three-dimensional mineral prospectivity mapping Considering structural restoration in the Dayingezhuang gold Deposit, eastern china

In the era of big data and the rapid advancements in machine learning, particularly deep learning, three-dimensional mineral prospectivity mapping (3D MPM) has emerged as a powerful tool for accurately deciphering the complex nonlinear relationships between ore-controlling factors hidden within geoscientific datasets and known mineralization. However, post-mineralization structural deformations can significantly distort the structures and mineralization distribution. This distortion introduces a substantial amount of error into 3D MPM, thereby diminishing its effectiveness in mineral resource exploration. In response to this challenge, our study introduces an innovative 3D MPM approach incorporating structural restoration. This approach has been applied to the Dayingezhuang gold deposit, where the Zhaoping fault, experienced disruption due to the post-mineralization NW-trending Dayingezhuang fault. The existing 3D model of the Zhaoping fault was first constructed using the observation data. A 3D geometric transformation technique was then employed to restore the original structures and mineralization distribution during the mineralization period. Subsequently, morphological analysis and prospectivity modeling were performed using deep learning algorithms including the Bayesian decomposition model for regression and multilayer perceptron model for classification. The results indicated several crucial findings: (1) structural restoration effectively eliminated the spatial distortions (250 m to 500 m) within the Zhaoping fault and rectified the discontinuities in mineralization zones; (2) the post-mineralization ore-controlling features exhibited smaller dips and undulations, with the main mineralization concentrated in areas transitioning from steep to gentle dips (-10°/100 m to 2°/100 m); (3) the post-restoration regression and classification models showed higher accuracy and reliability compared to the pre-restoration models, with an improvement of approximately 27 % and 10 %, respectively. As per our research findings, structural restoration effectively eliminates distortions in both fault and mineralization induced by post-ore structural deformations. Consequently, this method significantly enhances the reliability of 3D MPM. In the context of structural deformations, it underscores the necessity and effectiveness of structural restoration in conducting 3D MPM on a deposit scale.

A Hybrid Bearing Prognostic Method With Fault Diagnosis and Model Fusion

Accurate bearing fault diagnosis and prognosis (FDP) is critical for optimal maintenance schedules, safety and reliability. The existing methods face some problems and challenges in detecting the starting time for prognosis and using a single model to describe fault dynamics. With these motivations, this paper presents a hybrid bearing FDP framework with fault detection and automatic fault model selection. In the proposed approach, convolutional neural network is used to detect fault and select the appropriate fault dynamic model. To improve the performance, power spectrum of vibration signals are fused with operating conditions to built information maps for fault detection and model selection. After a fault is detected, a Bayesian method is triggered to estimate the fault state and predict the remaining useful life. In the prognosis, Dempster-Shafer theory is employed to fuse prediction results from different models if necessary. The proposed approach is verified with bearings under different operating conditions. Experimental results and comparison studies verify the high accuracy and efficiency of the proposed method.

A New Method to Invert for Interseismic Deep Slip Along Closely Spaced Faults Using Surface Velocities and Subsurface Stressing‐Rate Tensors

AbstractInversions of interseismic geodetic surface velocities often cannot uniquely resolve the three‐dimensional slip‐rate distribution along closely spaced faults. Microseismic focal mechanisms reveal stress information at depth and may provide additional constraints for inversions that estimate slip rates. Here, we present a new inverse approach that utilizes both surface velocities and subsurface stressing‐rate tensors to constrain interseismic slip rates and activity of closely spaced faults. We assess the ability of the inverse approach to recover slip rate distributions from stressing‐rate tensors and surface velocities generated by two forward models: (a) a single strike‐slip fault model and (b) a complex southern San Andreas fault system (SAFS) model. The single fault model inversions reveal that a sparse array of regularly spaced stressing‐rate tensors can recover the forward model slip distribution better than surface velocity inversions alone. Because focal mechanism inversions currently provide normalized deviatoric stress tensors, we perform inversions for slip rate using full, deviatoric or normalized deviatoric forward‐model‐generated stressing‐rate tensors to assess the impact of removing stress magnitude from the constraining data. All the inversions, except for those that use normalized deviatoric stressing‐rate tensors, recover the forward model slip‐rate distribution well, even for the SAFS model. Jointly inverting stressing rate and velocity data best recovers the forward model slip‐rate distribution and may improve estimates of interseismic deep slip rates in regions of complex faulting, such as the southern SAFS; however, successful inversions of crustal data will require methods to estimate stressing‐rate magnitudes.

Noise-induced chaos in an electronic Burridge-Knopoff model analog

Electronic analog circuits are valuable platforms to experimentally address physical systems whose implementation would otherwise be unwieldy. Besides validating numerical predictions, electronic analogs of nonlinear systems are also ideal tools to study the effect of noise injection in realistic conditions and are often more scalable than numerical approaches. In this work, we address the effect of noise injection in an electronic analog of the Burridge-Knopoff mechanical model of earthquake faults. Noise is injected into the friction term of the mechanical system: interestingly, the presence of a sufficient level of noise can trigger the onset of chaos in otherwise periodic regimes.

Self Consistent Electrical and Thermal-Hydraulic Model of Faults in Superconducting Magnets: EU DEMO TF Short Circuit During a Fast Discharge

Self Consistent Electrical and Thermal-Hydraulic Model of Faults in Superconducting Magnets: EU DEMO TF Short Circuit During a Fast Discharge

Data Preprocessing for Utilizing Simulation Models for ML-based Diagnosis

With increasing complexity in cyber-physical systems (CPS), fault detection with root cause analysis during application is essential. Thus, several approaches and methods have been introduced in the past. In this paper, we contribute to fault localization in the context of CPS. We present a data preprocessing method that enables the real-time diagnosis of a system’s behavior by classifying the present conditions. The suggested data preprocessing pipeline utilizes simulation models comprising fault models to compute information used for root cause analysis. The applied diagnosis methods, trained on the preprocessed data, enable a fault behavior analysis during operation by analyzing the system observations. This paper presents the complete processing pipeline, comprising the CPS analysis with relevant behavior information extraction and a classification algorithm. In addition, we demonstrate the results obtained from a use case considering a simplified DC e-motor model. Based on the use case, different machine learning (ML) algorithms, such as nearest neighbor, multi-layer perception, decision tree, and random forest, are evaluated on performance and diagnosis accuracy.

Simulation of CMOS technology microcircuit elements with eco-friendly practices in agro-industrial production

The paper explores contemporary design technologies for CMOS LSIs and examines challenges encountered during the modelling of LSIs at various hierarchical levels within the design process. The analysis underscores the pressing need for developing software tools for fault modelling while minimizing computational expenses. In addition to addressing the technical aspects, this study sheds light on the influence of ecology and agro-industrial practices on the microcircuit modelling process. Emphasis is placed on resource efficiency and mitigating adverse environmental impacts, particularly in the context of agro-industrial production. The paper discusses the requirements for creating and utilizing SF block libraries, aiming at establishing databases facilitating swift identification, environmental monitoring, and evaluation of the ecological footprint resulting from agro-industrial activities. Moreover, the paper presents principles for constructing VLSI CAD centres that navigate the delicate balance between advanced technologies and environmental sustainability. These principles provide a foundation for developing a unified hardware and software complex tailored for designing radio- electronic equipment while taking into consideration the specific needs and challenges of the agricultural sector.

Construction of the geological model of the “Knurów” deposit and its application to the exploitation design

The “Knurów” deposit is characterized by intense and rich fold and fault tectonics. The model of the “Knurów” deposit includes 11 seams belonging to the Orzesze, Ruda Śląska and Siodłowe layers. Each of these seams is recognized by boreholes, but also properly documented by mining, which greatly facilitates the modelling of its surface. The article addresses selected issues related to the construction of a three-dimensional model of the deposit. The method of modelling the surface of the terrain and the erosion surface of the Carboniferous is presented, which are used to calculate the location of subcrops of modelled seams and to predict the deformation of the terrain under the influence of mining. The general principles of fault modelling are presented, and so are ways of designing mining surfaces in longwalls passing through a fault. In the analysed deposit there are also seam splits. The article discusses the principles adopted for the development of the deposit model and its visualization, as well as the use of a dedicated algorithm for calculating the location of mining surfaces in such zones.

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.