Fault System Research Articles

Tri-training algorithm based nuclear power systems semi-supervised fault diagnosis under multiple restricted data conditions

An improved tri-training semi-supervised classification method was proposed as a solution to the restricted data conditions of supervised classification models relying on labeled data, semi-supervised models not considering imbalanced proportion of faults data and the presence of faults data with similar feature or varying severity in existing data-driven nuclear power system fault diagnosis studies. Based on the structure of three identical sub-classifiers in the original tri-training algorithm, data update paths have been added, the updated data selection has been strengthened, and the initial information and permissions of sub-models have been differentiated. The novel method only requires the dataset to meet the Smoothness Assumption, and through multiple strict data update paths, it improves the utilization of unlabeled training data while reducing Pseudo-labeling process errors. The difference in permissions and initial information between sub-classifiers is utilized to improve the training rigor for fault types with smaller sample sizes, in order to alleviate the problem of imbalanced data. Based on a marine nuclear power system secondary circuit model and a widely recognized nuclear power plant dataset, multiple nuclear power system common restricted fault diagnosis datasets faced in ships and power plants were obtained to verify the novel method's advantages and generalization. The conclusion is that under the same conditions, compared with the original tri-training method and other semi-supervised learning methods, the Accuracy, AUC, Precision Rate and Recall Rate of the novel method have improved by about 21 %, 10 %, 20 %, and 21 %, respectively, and can reduce about 25 % of misjudgments between similar feature faults.

Crustal stress near the Yakutat microplate collision from probabilistic earthquake focal mechanisms

Earthquake source characteristics provide a valuable constraint on crustal stress and regional plate tectonics. Earthquake source studies in Yukon and northern British Columbia have been limited by sparse seismic network coverage. In this work, we leverage recent seismic network improvements to estimate focal mechanisms for small and moderate-magnitude (M>2.0) earthquakes from P-wave first-motion polarity data. We invert these data within a probabilistic framework to rigorously quantify mechanism uncertainties. Subsequently, probabilistic earthquake focal mechanisms are used as input for inversions for the orientation of principal stress axes, and stress shape ratio, for spatial windows throughout the region. We implement a novel, data-driven approach to propagate focal mechanism uncertainty in stress inversion. Our results improve the spatial coverage of existing earthquake focal mechanisms and enable an orogenic-scale study of crustal stress near the Yakutat-North America collision. Overall, the region is characterized by a transpressive stress regime. Maximum horizontal compressive stress orientations exhibit a pattern orthogonal to the Yakutat collision syntaxis. Stress inversion results also reveal an abrupt change in orientation east-to-west, which we interpret as a change in stress regime across the Fairweather-Connector-Totschunda fault system that is likely related to coupling between the subducting Yakutat slab and North America. This work improves our understanding of potential earthquake hazards in southwestern Yukon and the surrounding region.

Enhanced stochastic coding framework for discriminating faults and cyber incidents in hybrid systems

AbstractThis article presents an innovative approach for distinguishing replay attacks from faults in hybrid systems. To develop the idea at first, a novel technique for replay attack detection, which differentiates it from typical system faults, is introduced. This is achieved through a tactical combination of stochastic coding and a window‐based comparison mechanism, setting a new standard in hybrid system security. In the realm of fault detection, robust L2‐L∞ adaptive observers are employed for precise mode detection, allowing for an accurate assessment of the system's operational state. Additionally, robust H∞ Sliding Mode Observers are utilized to identify abnormal behaviours in the system's output, further solidifying the fault detection capabilities. A significant enhancement in the proposed approach is the integration of a Luenberger observer with the Butterworth low‐pass filter. This novel addition not only refines the filtering process but also contributes to the overall reliability and accuracy of the system. The efficiency and versatility of these methods are demonstrated through their application to a four‐tank hybrid system. This practical simulation showcases the adaptability of the approach to real‐world scenarios, highlighting its potential in diverse applications within the field of hybrid systems.

Opening of the North Atlantic Ocean and the rise of Scandinavian mountains

Large-scale topography is usually associated with tectonic plate boundaries, but the Scandinavian mountains (Scandes) are located far from any active tectonic setting, and their origin is unknown. We demonstrate that the Precambrian lithospheric structure of Fennoscandia controlled both the Cenozoic ocean opening and mountain rise in the North Atlantic region. Our new seismic receiver function analysis reveals a block of thick continental crust formed by Proterozoic crustal stacking. We propose that this block created a wide continental shelf bounded by two transform fault systems during continental breakup. This geometry resulted in the formation of lithospheric steps at the continental margin on both sides of the stacked crustal structure, coinciding with the highest Southern and Northern Scandes. We propose that edge-driven convection at these steps caused the mountain rise. This study presents a general model for the formation of high elevation behind passive margins.

Caledonian reactivation and reworking of Timanian thrust systems and implications for latest Mesoproterozoic to mid-Paleozoic tectonics and magmatism in northern Baltica

Background The Trollfjorden–Komagelva Fault Zone is the southernmost thrust fault of the Timanian Orogen and extends for thousands of kilometers from northwestern Russia to northern Norway. Though there is little about its location onshore northeastern Norway, where it is mapped as a major fault system dominantly comprised of NNE-dipping thrust faults, its continuation to the west below Caledonian nappes and offshore post-Caledonian sedimentary basins remains a matter of debate. Methods The present study provides a more definitive answer about the continuation of Trollfjorden–Komagelva Fault Zone west of the Varanger Peninsula by using seismic reflection, bathymetric, topographic, and magnetic data onshore Finnmark and offshore on the Finnmark Platform. Results The present study demonstrates that the Sørøya–Ingøya shear zone represents a portion of the Trollfjorden–Komagelva Fault Zone that was folded into a NE–SW orientation and reactivated as a top-southeast thrust during the Caledonian Orogeny, while other portions of the Trollfjorden–Komagelva Fault Zone (e.g., on the Varanger Peninsula) were reactivated as strike-slip faults. The study also documents the presence of another major, NNE-dipping Timanian shear zone with a similar geometry to the Trollfjorden–Komagelva Fault Zone north of the Varanger Peninsula. Conclusions The Trollfjorden–Komagelva Fault Zone may continue offshore as a NE–SW-striking folded structure. This has the following implications: (1) the Seiland Igneous Province likely formed in a backarc setting, (2) metasedimentary rocks of the Kalak Nappe Complex deposited along the Baltican margin of the Iapetus Ocean, possibly in a late–post-Grenvillian collapse basin, (3) the Iapetus Ocean was much narrower than the several thousands of kilometers width commonly proposed, and (4) early Neoproterozoic magmatism in northern Norway is possibly related to the initial breakup of Rodinia.

Precision Seismicity of the San Andreas Fault System in Central California: Insights into Active Tectonics and Earthquake Physics

Since the late 1960s, seismicity of the San Andreas Fault system in central California has been under intense observation by U. S. Geological Survey’s Northern California Seismic Network (NCSN). Designed to provide accurate focal depth control and a low magnitude of catalog completeness, the network maps fault, both large and small across the full breadth of this 400-kilometer-long portion of the plate boundary, including many that are known only from their seismicity. Beginning in 1984, with the advent of routine digital recording, new opportunities were created to significantly enhance the spatial resolution of seismicity. This led to the co-evolution of method that take full advantage of digital waveforms and new insights into the nature of seismicity and faults. Collectively referred to as precision seismicity, discoveries included repeating earthquakes and highly organized and stable distributions of seismicity on major faults. Long term observation of seismicity before and after the six largest earthquakes over the last half-century underscore the outstanding challenges for earthquake predictability and theories of earthquake nucleation.

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.

EMA fault diagnosis method based on multi-information fusion of GRU and improved attention mechanism

Aiming at the problems of time series feature loss and fault information loss in traditional electromechanical servo system (EMA) fault diagnosis methods based on machine learning and deep learning, a multi-information fusion EMA fault diagnosis method based on gated recurrent unit (GRU) and improved attention mechanism is proposed. Firstly, the collected different sensor signals are divided into different channels, and the time series features of each channel signal are extracted by GRU. Then, the self-attention mechanism is introduced to further distinguish the important relationship between different time points of the signal. The multi-channel attention mechanism is further introduced to adaptively fuse the features of different channels, and finally the fault diagnosis is realized through the classifier. The experimental results based on the test bench data set show that: compared with the single sensor model, the diagnosis rate is improved 10%; compared with the model without the introduction of the attention mechanism, the diagnosis rate is improved 5.2%; compared with the classic machine learning, deep learning and the improved algorithm based on deep learning in the past two years, the diagnosis rate of the diagnosis model designed in this paper is 98.5%above, and the diagnosis effect is the best.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Deep learning-based proactive fault detection method for enhanced quadrotor safety

The early detection of faults in advanced technological systems is imperative for ensuring operational reliability and safety. While there is a growing interest in using artificial intelligence for fault detection, current methodologies often exhibit limitations in utilizing comprehensive system information and sensor data. Hidden faults within collected data further highlight the need for advanced analysis techniques. This study introduces a novel deep learning-based framework designed to predict faults and extract insights from complex system datasets. The model, consisting of LSTM-autoencoder and BiLSTM classification components, effectively reduces feature dimensions, thereby enhancing fault detection accuracy. The autoencoder’s latent layer identifies prominent features across various dimensions, while BiLSTM classification conducts bidirectional analysis using these features from both healthy and faulty states, facilitating early fault detection. Experimental results demonstrate the model’s efficacy, achieving an accuracy of 79.48% in predicting incipient faults 30 seconds before a serious malfunction occurs. This underscores the significant potential of the proposed framework in enhancing operational safety and reliability in complex systems. Moreover, the study emphasizes the importance of leveraging comprehensive data and advanced analysis techniques for early fault detection.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

Fault diagnosis is critical in maintaining the operational stability and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems, which are crucial for ensuring indoor environmental quality and energy efficiency in buildings. However, traditional fault diagnosis methodologies face substantial challenges in accurately capturing the dynamic interactions within these systems and effectively utilizing unlabeled data. To overcome these limitations, an innovative fault diagnosis approach utilizing the Adaptive Fusion Graph Convolution Network (AFGCN) is proposed in this paper. This method significantly enhances the model's learning and inference abilities, particularly in scenarios with limited labeled data, by adaptively integrating the associative graph features of both unlabeled and labeled data. Furthermore, to augment the transparency and trustworthiness of the diagnostic outcomes, this paper introduces an interpretability analysis module. This module is designed to quantify the contribution of each sensor node in the fault diagnosis process. Experimental evaluations using the ASHRAE RP-1043, actual building operating chillers datasets, and LBNL FDD Data Sets_SDAHU indicate that this method offers substantial performance improvements in diagnosing faults in HVAC systems.

Output feedback fault-tolerant Q-learning for discrete-time linear systems with actuator faults

This paper presents an innovative output feedback fault-tolerant Q-learning algorithm that can be implemented online without relying on explicit system models or fault details. In the face of actuator faults, finding optimal Fault-Tolerant Control (FTC) solutions that can stabilize the faulty system poses significant challenges. The proposed approach is implemented online without the need for system dynamics and actuator fault information. Furthermore, it operates without relying on full state measurements, utilizing only the input-output data of the faulty system. An innovative representation of the output feedback Fault-Tolerant Q-function (FTQF) is established using input-output data. Subsequently, a model-free optimal output feedback FTC policy is obtained from the developed FTQF. Then, a fault-tolerant Q-learning algorithm is formulated to iteratively acquire the optimal FTC policy in real-time, eliminating the necessity for system dynamics and actuator fault information. The proposed algorithm exhibits immunity to excitation noise bias, even without considering a discounting factor. Furthermore, the proposed Q-learning approach is proven to be effective in stabilizing faulty closed-loop system. Finally, the proposed algorithm's efficiency is validated through numerical simulations of F-16 autopilot aircraft dynamics.

A Geometric View of Seismic Wavefields: Implications for Imaging Dense Clusters of Events

Summary Imaging dense clusters of seismicity is crucial to many problems in seismology: to delineate complex systems of faults, provide constraints on the causes of volcanic and cryogenic swarms, and to shed light on possible means to prevent damaging induced seismicity in mining, geothermal and oil and gas extraction activities. Current imaging methods rely upon high-resolution relative location techniques, commonly requiring arrival-time picks for seismic phases. This paper examines an alternative approach, based upon concepts drawn from differential geometry, that images directly from waveform data. It relies upon the common assumption of spatial continuity of seismic wavefield observations, which implies that a differentiable map exists between the source region to be imaged and waveform observations considered as elements of a vector space. The map creates an image of event clusters on a Riemannian manifold embedded in that vector space. The image can be visualized by projecting the observations into a tangent space of the manifold and is a distorted rendering of cluster geometry. However, the distortion can be predicted and removed if a model for wavefield propagation is available. This visualization approach is applicable to clusters of uniform events with highly similar waveforms, such as are commonly acquired with correlation detectors or other pattern matching techniques. To assess its performance, it is applied to the closely-related reciprocal problem of imaging the (known) geometry of an array from observations by the array of several regional events. Differences between the original problem and its reciprocal analog are noted and controlled for in the analysis. Chief among the differences is the necessity for aligning the waveforms in the original problem, which, to maintain consistency with the original problem, is solved in the reciprocal problem by a generalization of the VanDecar-Crosson algorithm. The VanDecar-Crosson algorithm exhibits a bias, shown through an analysis of the situation when the observed wavefields are adequately modeled as plane waves. In that circumstance, the bias can be predicted and removed. In a test using a portion of a large-N array, this imaging approach is shown to successfully reconstruct the array geometry. The method is applicable directly to infinitesimal array apertures, but is extended to a larger aperture by partitioning the image into local, effectively infinitesimal overlapping subsets. These are inverted, then assembled into a global picture of the array geometry using constraints provided by the overlapped regions. Although demonstrated in a reciprocal array context, the method appears viable for imaging clusters of events with highly similar source mechanisms and time histories.

Fault Characteristics and Reservoir Potential of Mesozoic Basins in the Southern East China Sea

The East China Sea Basin (ECSB) is an integral part of the Western Pacific tectonic system. Its development is linked to the Kula–Pacific Plate and the formation and expansion of the Philippine Sea Basin. Recent advancements in exploration technologies and theory have been applied to Mesozoic basins in the East China Sea. Researchers have posited that the southern basin has great oil and gas exploration potential. However, the characteristics and evolution of fault structures and their influence on hydrocarbon accumulation remain unclear. Here, in-depth geometric and kinematic analyses of Mesozoic fault structures in the southern ECSB were conducted using the latest interpretations of 2D seismic data and structural analysis theory. The findings revealed that the fault system was well developed and predominantly exhibited multiphase extensional and extensional-torsional features. Based on their lateral distribution and morphology, faults were categorized into three structural styles and seven combinations. According to their developmental timing, periods of active faulting were attributed to the Yanshan and Himalayan epochs. Multiphase fault activities strongly controlled the formation of traps and thus hydrocarbon accumulation, while earlier NE-trending faults controlled the formation of structural belts and hydrocarbon source areas.

Research on condition assessment of nuclear power systems based on fault severity and fault harmfulness

Obtaining real time quantitative condition assessment results is a focus in the field of nuclear power system PHM. The existing researches on the nuclear power system condition assessment based on the Multi-Criteria Decision-Making framework do not consider the faults perniciousness differences. This leads to the assessment results relying on the system status deviation degrees(severity), and the weighting and aggregation processes cannot effectively reflect the differences in the consequences and risks of different faults, which represent the faults harmfulness. A quantitative assessment method based on fault severity and fault harmfulness is proposed to address this issue. The novel method quantifies the fault severity based on the similarity principle while diagnosing system faults, and then analyzes the devices mainly affected by various faults to quantifies the harmfulness. Combining the system status deviation degrees(severity), the devices and parameters importance, and the differences in faults harmfulness, the novel method aggregates comprehensive system condition assessment results. Based on a widely recognized nuclear power system faults dataset, the novel method was compared with other methods. The conclusion is that the novel method considers the differences in the faults harmfulness, resulting in more reasonable assessment results and avoiding insufficient or excessive warnings.

Seismicity and fractal analysis in Aswan region, southern Egypt

Seismic activity in Aswan is influenced by the complex interactions of tectonic plates, the accumulation of stress, and the presence of geological fault systems. It revealed that epicenters are well distributed along four fault segments in a conjugate pattern, indicating a prominent E-W compressional stress. This research aims to explore the characteristics of seismicity and seismotectonics, with a focus on assessing their implications for risk reduction and disaster management in this densely populated region. A data review from the Egyptian National Seismological Network (ENSN) identified 464 earthquakes occurred between 2000 and 2021, with local magnitudes ranging from 0.3 to 4.4, and depths up to 25 km. The calculated Gutenberg-Richter b-value is approximately 0.87 ± 0.05, indicating a gradual stress accumulation. The current analysis shows a more consistent level of moderate seismic activity, unlike previous studies in Aswan region that reported a wide range of b-values from 0.554 to 1.07. This suggests that while earlier research captured a wider range of seismic behaviors, recent data indicates a stabilization in earthquake frequency and intensity. Additionally, the fractal dimension (Dc) calculated at 1.57 ± 0.04 shows an intermediate level of complexity and reflecting a clustering pattern of earthquakes. The variations in the b-value with different magnitudes and depths signify the involvement of active smaller faults, responsible for earthquakes up to magnitude 2.2, which then transition to fractured zones inducing earthquakes up to magnitude 2.5. This transition is followed by a decline in seismic activity, indicating regions that are potentially more likely to experience larger earthquakes. Moreover, stress disparities at various depths contribute to smaller earthquakes within the 5–10 km depth range. Return period analysis suggests that the earthquakes of magnitude 3.7 or higher are expected to occur approximately once every decade in Aswan. These findings are of utmost importance for earthquake risk reduction, hazard assessment, and the sustainable development of Aswan area.

Managing Induced Seismicity Risks From Enhanced Geothermal Systems: A Good Practice Guideline

AbstractGeothermal energy is a green source of power that could play an important role in climate‐conscious energy portfolios; enhanced geothermal systems (EGS) have the potential to scale up exploitation of thermal resources. During hydraulic fracturing, fluids injected under high‐pressure cause the rock mass to fail, stimulating fractures that improve fluid connectivity. However, this increase of pore fluid pressure can also reactivate pre‐existing fault systems, potentially inducing earthquakes of significant size. Induced earthquakes are a significant concern for EGS operations. In some cases, ground shaking nuisance, building damages, or injuries have spurred the early termination of projects (e.g., Basel, Pohang). On the other hand, EGS operations at Soultz‐sous‐Forêts (France), Helsinki (Finland), Blue Mountain (Nevada, USA), and Utah FORGE (USA) have adequately managed induced earthquake risks. The success of an EGS operation depends on economical reservoir enhancements, while maintaining acceptable seismic risk levels. This requires state‐of‐the‐art seismic risk management. This article reviews domains of seismology, earthquake engineering, risk management, and communication. We then synthesize “good practice” recommendations for evaluating, mitigating, and communicating the risk of induced seismicity. We advocate for a modular approach. Recommendations are provided for key technical aspects including (a) a seismic risk management framework, (b) seismic risk pre‐screening, (c) comprehensive seismic hazard and risk evaluation, (d) traffic light protocol designs, (e) seismic monitoring implementation, and (f) step‐by‐step communication plans. Our recommendations adhere to regulatory best practices, to ensure their general applicability. Our guidelines provide a template for effective earthquake risk management and future research directions.

Faulty section location method for high impedance grounding fault in resonant grounding system based on discrete Fréchet distance

When a High Impedance Fault (HIF) occurs in a resonant grounding system, the differentiation of the current flowing upstream and downstream of the fault point is inconspicuous due to the weak electrical signal. This paper proposes a faulty section location approach based on Discrete Fréchet Distance (DFD) to address this issue. Initially, taking the zero-sequence current as the characteristic quantity, the instantaneous value of the zero-sequence current at each sampling point and its previous sampling moments are accumulated to amplify the fault signal, and furthermore to obtain the cumulative waveform of the zero-sequence current at each monitoring point. The DFD values between the cumulative waveforms of zero-sequence currents flowing through each section are also calculated. For a branchless section, if the Coefficient of Variation (CV) of the DFD in each section does not exceed the threshold value, it is identified as an end-of-line fault, otherwise, the section with the maximum DFD is identified as a faulty section. For a section with branches, the branching coefficient is calculated to determine whether it is a faulty section or not. Finally, MATLAB/Simulink simulation results and field-recorded data demonstrate the validity and robustness of the approach under various fault conditions, despite the connection of Distributed Generators (DGs).

Editorial for IJPHM Special Issue on PHM Asia Pacific Conference 2023

Prognostics and system health management (PHM) remain crucial for maintaining various industrial systems' reliability, safety, and efficiency. However, there remain gaps and challenges that need to be addressed. The special issue compiles 12 extended papers from the 2023 PHM Asia Pacific Conference, showcasing extensive research and a wide range of applications. It showcases research findings on autonomous system monitoring, fault detection, anomaly detection, and predictive maintenance. A common theme across the papers is the balance between computational efficiency, explainability, and real-world applicability. While the research presented here is highly innovative, it also highlights the need for PHM systems that are not only robust and scalable but also transparent and easily interpretable for end-users.

Neotectonics of the Magallanes-Fagnano fault system in Fuegian Patagonia based on high-resolution seismic profiles and geomorphic markers

Neotectonics of the Magallanes-Fagnano fault system in Fuegian Patagonia based on high-resolution seismic profiles and geomorphic markers