Fault Patterns Research Articles

Block Rotations in NW Iran in Response to the Arabia‐Eurasia Collision Constrained by Paleomagnetism

AbstractNorthwest Iran is a seismically active region dominated by NW‐SE trending strike‐slip faults, such as the North Tabriz and Qosha Dagh faults, and smaller NNE‐SSW striking faults. The Bozgush Mountains are shaped by these faults and divided into two domains that show a difference in strike. To quantify rotational tectonic deformation in NW Iran, we performed a paleomagnetic study along three transects of the Bozgush and Qosha Dagh Mountains with 127 sites. Our large new paleomagnetic data set shows that the Bozgush Mountains did not rotate as a single rigid block. In the western domain of the Bozgush Mountains, we find evidence for clockwise vertical axis rotations of ∼40°, while the eastern domain has rotated up to ∼80° clockwise. Declinations of the western Bozgush domain fit well with observed declinations in the Qosha Dagh Mountains. Fault patterns show that the eastern domain of the Bozgush Mountains is divided by a set of NNE‐SSW striking sinistral strike‐slip faults, which created domino‐style blocks that accommodated the additional 40° of rotation. We estimate that these extra rotations have resulted in around 4 km of N‐S shortening and more than 1.5 km of differential uplift.

Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement

This study introduces WirelessGridBoost, an innovative framework designed to revolutionize real-time fault detection in wireless electrical grids by harnessing the power of the LightGBM machine learning algorithm. Traditional fault detection systems in electrical grids often face challenges such as latency and scalability due to the intricate nature of grid operations and limitations in communication infrastructure. To overcome these challenges, WirelessGridBoost integrates LightGBM, a highly efficient gradient boosting decision tree algorithm, with wireless technology to facilitate advanced fault detection capabilities. Trained on historical sensor data, the LightGBM model demonstrates exceptional proficiency in discerning complex fault patterns inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, WirelessGridBoost enables prompt identification of anomalies in real-time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of WirelessGridBoost, achieving a fault detection accuracy of 96.80% and reducing latency by 38% compared to conventional methods. This research presents a promising avenue for enhancing fault detection efficiency in wireless electrical grids through the innovative WirelessGridBoost framework.

Enhancing fault-clearing algorithm for renewable-energy-based distribution systems using artificial neural networks

Abstract Integrating small and large-scale photovoltaic (PV) solar systems into electrical distribution systems has become mandatory due to increased electricity bills and the concern for limiting greenhouse gases. However, the reliable and efficient operation of PV-based distribution systems can be confronted by the intermittence and high variability of solar sources and their consequential faults. In this regard, this article suggests a moderated fault-clearing strategy based on the incremental conductance–maximum power point tracking (IC–MPPT) technique and artificial neural networks (ANNs) to enhance fault detection, localization, and restoration processes in PV-based distribution systems. The proposed strategy leverages IC–MPPT to ensure optimal power generation from the PV solar system, even in the presence of faults. By tracking the maximum power point, the algorithm maintains the performance of the system and mitigates against the impact of faults on the output power. Furthermore, an ANN is employed to improve fault detection and localization accuracy. The developed ANN-based moderated fault-clearing strategy is trained using historical data and fault scenarios, enabling it to recognize fault patterns and make informed decisions through extensive simulations and comparisons with traditional fault-clearing methods. To accomplish this study, benchmarks in PV-based distribution systems are constructed and employed using the MATLAB®/Simulink® software package. Moreover, to validate the efficacy of the developed ANN-based moderated fault-clearing strategy, a real case study of a 1-MW PV-based distribution system in an industrial field located in Giza governorate, Egypt, is tested and investigated. The obtained results demonstrate the effectiveness of the IC–MPPT and ANN-based moderated fault-clearing strategy in achieving faster fault detection, precise fault localization, and efficient restoration in PV solar-based distribution systems while preserving maximum power extraction under small and large system disturbances. Furthermore, IC–MPPT based on an ANN achieves an average power of 98.556 kW and 299.632 kWh energy availability, whereas the IC–MPPT based on a proportional–integral controller achieves 95.7996 kW and 283.4036 kWh, and the classic perturb-and-observe MPPT algorithm achieves 92.2657 kW and 276.8014 kWh.

New insights into the structure of the Yinggehai Basin and its tectonic implications, South China Sea: Evidence from scaled physical models

AbstractThe Yinggehai Basin is situated at the junction of Indochina and the northern South China Sea (SCS). The origin of the Yinggehai Basin is generally believed to be controlled by the rotation of the Indochina block along the Red River shear zone (RRSZ), which was formed by the collision of India with Asia during the Oligocene. However, the Eocene structural mechanisms of this basin remain debatable. Some studies suggest that the Eocene reactivation of the palaeo‐suture zone (which serves as a precursor to the RRSZ) has influenced the region. In contrast, others propose that the NNW–SSE extension of the northern SCS caused by the subduction of the palaeo‐SCS towards Borneo in the Eocene has played a significant role. To address these controversies, our study takes into account these two crucial factors using physical analogue modelling. The experimental results, including slow sinistral strike‐slip along the palaeo‐suture zone and the adjacent NNW–SSE extension, successfully explain the observed fault pattern during the Eocene period. It is noteworthy that the former primarily controlled the Eocene structure in the northern region of the Yinggehai Basin, whereas the latter played a pivotal role in shaping the ENE–WSW Eocene structures on the eastern slope of the basin. The westward propagating faults of the Qiongdongnan basin are cut off by the Yinggehai Basin structures at later large‐scale rotation stage. The experiment indicates that the basin evolution exhibits diachronous characteristics, with subsidence in the south occurring later than in the north. Our modelling results provide valuable insights into the key controlling factors that shaped the evolution of the basin during each stage. Furthermore, our findings offer evidence of the interaction between two significant tectonic processes: Indochina extrusion and the opening of the SCS.

Deep Fusion of Intrinsic Vibration Information and Grassmann Manifold-based Similarity for Fault Identification of Reciprocating Compressor

This paper introduces a new method based on deep belief networks (DBN) to integrate intrinsic vibration information and assess the similarity of subspaces established on the Grassmann manifold for intelligent fault diagnosis of a reciprocating compressor (RC). Initially, raw vibration signals undergo empirical mode decomposition (EMD) to break them down into multiple intrinsic mode functions (IMFs). This operation can reveal inherent vibration patterns of fault and other components hidden in the original signals. Subsequently, features are refined from all the IMFs and concatenated into a high-dimensional representative vector, offering localized and comprehensive insights into RC operation. Through DBN, the fault-sensitive information are further refined from the features to enhance their performance in fault identification. Finally, similarities among subspaces on the Grassmann manifold are computed to match fault types. The efficacy of the method is validated using field data. Comparative analysis with traditional approaches for feature dimension reduction, feature extraction, and Euclidean distance-based fault identification underscores the effectiveness and superiority of the proposed method in RC fault diagnosis. Conflict of Interest Statement The authors declare no conflicts of interest.

FSN: Feature Shift Network for Load-Domain (LD) Domain Generalization

Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it is hard for the trained model to achieve desirable performance on the testing set. In this paper, we introduce a novel domain generalization, Load-Domain (LD) domain generalization, which is based on the analysis of the Case Western Reserve University (CWRU) bearing dataset and takes advantage of the physical information of this dataset. For this scenario, we propose a feature shift model called Feature Shift Network (FSN). FSN is trained for feature shift on adjacent source domains and finally shifts target domain features into adjacent source domain feature space to achieve the purpose of domain generalization. Furthermore, through the hybrid classification method, the generalization performance of the model on unseen target domains is effectively improved. The results on the CWRU bearing dataset demonstrate that FSN is better than the existing models in the LD domain generalization. Furthermore, we have another test on the rotated MNIST, which also shows FSN can achieve the best performance.

Fault identification of centrifugal pump using WGAN-GP method with unbalanced datasets based on kinematics simulation and experimental case

Abstract In practical engineering applications, the accuracy and stability of fault identification for centrifugal pump will be significantly reduced due to unbalanced distribution between normal and fault datasets, i.e., the number of normal working samples is far more than the fault samples. To alleviate this bottleneck issue, this paper explores the fault identification of centrifugal pump based on Wasserstein generative adversarial network with gradient penalty (WGAN-GP) through combining kinematics simulation and experimental case. Specifically, ideal unbalanced vibration datasets from failure patterns such as damaged impeller of centrifugal pump are simulated and collected by prototype ADAMS software, then the unbalanced vibration signals are transformed into 2D grey-scale images. Furtherly, the generated grey-scale image datasets are feed into the original grey-scale image dataset as new datasets for training when the Nash equilibrium of the WGAN-GP model is reached. Eventually, the fault patterns of centrifugal pump are identified using confusion matrix graph. Meanwhile, another public dataset of centrifugal pump is employed for verifying the accuracy of the WGAN-GP model. Results indicate that fault identification accuracies with 95.07% and 98.0% of both kinematics simulation and experimental case are obtained, respectively, and the issues of unbalanced distribution and insufficient dataset can be overcome effectively.

MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis

AbstractAs industrial production escalates in scale and complexity, the rapid localization and diagnosis of equipment failures have become a core technical challenge. In response to the demand for intelligent fault diagnosis in large‐scale industrial equipment, this study presents “MultiCogniGraph”—a multi‐hop reasoning diagnostic method that integrates multimodal data fusion, knowledge graphs, and graph convolutional networks (GCN). This method leverages internet of things (IoT) sensor data, small‐sample imagery, and expert knowledge to comprehensively characterize the equipment state and accurately detect subtle distinctions in fault patterns. Utilizing a knowledge graph to synthesize data from multiple sources and deep reasoning with GCN, “MultiCogniGraph” achieves swift and effective fault localization and diagnosis. The integration of these techniques not only enhances the efficiency and accuracy of fault diagnosis but also its interpretability, marking a new direction in the field of intelligent fault diagnostics.

Patterns of Causative Faults of Normal Earthquakes in the Fluid‐Rich Outer Rise of Northeastern Japan, Constrained With 3D Teleseismic Waveform Modeling

AbstractAccurate earthquake source parameters are crucial for understanding plate tectonics, yet, it is difficult to determine these parameters precisely for offshore events, especially for outer‐rise earthquakes, as the limited availability of direct P or S wave data sets from land‐based seismic networks and the unsuitability of simplified 1D methods for the complex 3D structures of subducting systems. To overcome these challenges, we employ an efficient hybrid numerical simulation method to model these 3D structural effects on teleseismic P/SH and P‐coda waves and determine the reliable centroid locations and focal mechanisms of outer‐rise normal‐faulting earthquakes in northeastern Japan. Two M6+ events with reliable locations from ocean bottom seismic observations are utilized to calibrate the 3D velocity structure. Our findings indicate that 3D synthetic waveforms are sensitive to both event location, thanks to bathymetry and water reverberation effects, and the shallow portion of the lithospheric structure. With our preferred velocity model, which has Vs ∼16% lower than the global average, event locations are determined with uncertainties of <5 km for horizontal position and <1 km for depth. The refined event locations in a good match between one of the nodal strikes and the high‐resolution bathymetry, enabling the determination of the causative fault plane. Our results reveal that trench‐ward dipping normal faults are more active, with three parallel to the trench as expected, while five are associated with the abyssal hills. The significant velocity reduction in the uppermost lithosphere suggests abundant water migrating through active normal faults, enhancing both mineral alteration and pore density.

Differences in hydrocarbon accumulation and controlling factors of slope belt in graben basin: A case study of Pinghu Slope Belt in the Xihu sag of the east China Sea Shelf basin (ECSSB)

The Pinghu slope belt in the Xihu sag of the East China Sea Shelf Basin (ECSSB) is a crucial hydrocarbon production area in eastern China. However, due to the complex geological conditions, publications have lacked comprehensive research on the spatial-temporal coupling relationships of primary factors that impact hydrocarbon accumulation in the Pinghu slope belt. Furthermore, the hydrocarbon distribution patterns and the controlling factors across different study areas within the same slope belt are not yet fully understood. This study extensively utilized three-dimensional seismic data, well logging data, geochemical analysis, fluorescence analysis, and oil testing and production data to address these issues. Following a “stratification and differentiation” approach, the study identified seven distinct hydrocarbon migration and accumulation units (HMAU) in the Pinghu slope area based on the structural morphology characteristics, hydrocarbon source-reservoir-cap rock patterns, hydrocarbon migration pathways, and hydrocarbon supply range. Detailed analysis was conducted to examine the hydrocarbon distribution patterns and controlling factors within each migration and accumulation unit across different structural units, including high, medium, and low structural components. All data sources support a “southern‒northern sub-area division, eastern‒western sub-belt division, and variations in hydrocarbon accumulation” pattern in the Pinghu slope belt. The degree of hydrocarbon accumulation is controlled by the factors of structural morphology, hydrocarbon generation potential of source rocks, the spatial position of source slopes, fault sealing capacity, and sand body distribution. Furthermore, different coupling patterns of faults and sand bodies play a pivotal role in governing hydrocarbon enrichment systems across various migration and accumulation units. These observations indicate that three hydrocarbon accumulation patterns have been established within the slope belt, including near-source to far-source gentle slope with multiple hydrocarbon kitchens in the XP1−XP4 zones, near-source to middle-source gentle slope with dual-hydrocarbon kitchens in the XP5 zone, and near-source steep slope with a single hydrocarbon kitchen in the XP6−XP7 zones. These findings contribute to enhancing the theoretical system of hydrocarbon accumulation in the slope belt.

Reliability Assessment of Interconnection Networks Based on Link Fault Patterns

Reliability Assessment of Interconnection Networks Based on Link Fault Patterns

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

Structural framework of the Caswell Sub-basin, North West Shelf, Australia

The Caswell Sub-basin, situated within the Browse Basin in the North West Shelf constitutes one of Australia’s primary hydrocarbon producing regions, with notable gas-condensate producing fields including Ichthys and Prelude. Jurassic syn-rift sandstones are extensively distributed across the basin and serve as one of the major reservoirs. However, reservoir sequences are typically intensely faulted and exhibit heterogeneity in thickness and lithofacies, with some areas experiencing localised erosion on uplifted fault blocks (e.g. Northern Caswell Sub-basin). Hence, understanding the nature of the 3D fault patterns and their growth history is crucial for evaluating the reservoir characteristics for field development and additional exploration activities. This study, therefore, aims to evaluate the structural framework and the tectonic evolution of the Caswell Sub-basin. Detailed structural interpretation of Paleozoic and younger sequences was conducted using multiple 3D seismic datasets extending over Ichthys, Prelude, Lasseter, and Crown fields. Our study focuses mainly on Mesozoic faulting patterns and kinematics evaluated from interpreted structural maps, thickness changes of each stratigraphic interval and fault throw profiles of major bounding faults. The extensional phase during the Early–Middle Jurassic triggered the development of NE–SW trending faults and the deepening of the Brewster Graben. While, from the Late Jurassic to the Early Cretaceous, the development of E–W trending faults in the north of the Crown and Lasseter fields indicate a shift in the regional stress regime. We highlight the importance of evaluating the structural linkage from basement to cover sequences to achieve a comprehensive understanding of reservoirs and associated petroleum systems.

Research on roller monitoring technology based on distributed fiber optic sensing system

As one of the key components of the belt conveyor, the roller bears the task of supporting and rolling the conveyor belt, and monitoring its condition is very important. The traditional monitoring of the conveyor roller group adopts worker inspection, which has strong subjectivity. Monitoring using sensors necessitates the use of numerous sensors, which can pose wiring challenges. The use of inspection robots for monitoring results can be discontinuous, and their performance may be limited. This study proposes a fault diagnosis method for rollers based on a distributed fiber optic sensing system. By improving the traditional Isolation Forest (IForest), a framework called Incremental Majority Voting Isolation Forest (IMV-IForest) is proposed. By analyzing the optical signal, we extracted the variation patterns of roller faults over time and space, and analyzed the abnormal score distribution between fault data and normal data. Using the dataset collected on-site, we compared and analyzed IMV-IForest with the traditional IForest and the Extended Isolation Forest (E-iForest). The results indicate that the variation of the fault of the faulty roller with time and space can be used for early prediction of roller faults; determine an anomaly score threshold of 0.6; improved IForest have faster computation time and higher accuracy. Finally, to verify the effectiveness of the proposed scheme, a 3-month experiment was conducted on a 600 m long belt conveyor in a certain mine, and on-site monitoring results were obtained. By comparing with manual detection results, it was shown that the proposed method has high recognition rate for faulty idlers, with an accuracy rate of 97.92%, and can effectively diagnose faulty idlers.

The Application and Effect of AI Fault Interpretation Technology in the Laoyemiao Area

The complex faults, especially mid-deep faults, in the Laoyemiao area of the Nanpu Sag, the Bohai Bay Basin, are unclearly understood for their characteristics, constraining the structural and geological delineation of the area. The hydrocarbon enrichment in the Laoyemiao area is closely related to the faults, and thus the precise identification of mid-deep faults is of great significance for understanding the structural system and reservoir distribution in the area. In the past twenty years, artificial intelligence (AI) scholars developed new technologies and methods to solve engineering problems. Typically, the AI seismic data interpretation technology plays a critical role in improving the accuracy and efficiency of fault interpretation. In order to define the structural characteristics of the Laoyemiao area, the "2W1H" seismic data were processed by fault-constrained structure-oriented filtering, and then interpreted using the EasyTrack module of GeoEast independently developed by BGP. It is found that the imaging quality and accuracy of mid-deep faults are improved effectively. On this basis, the SN-trending strike-slip fault systems were discovered, and the structural pattern and evolution law of mid-deep faults in the Laoyemiao area were re-understood. The results are of great significance for the structural identification, reservoir evaluation and selection of exploration targets in this area.

Semi-Permanent Stuck-At Fault injection attacks on Elephant and GIFT lightweight ciphers

Fault attacks pose a potent threat to modern cryptographic implementations, particularly those used in physically approachable embedded devices in IoT environments. Information security in such resource-constrained devices is ensured using lightweight ciphers, where combinational circuit implementations of SBox are preferable over look-up tables (LUT) as they are more efficient regarding area, power, and memory requirements. Most existing fault analysis techniques focus on fault injection in memory cells and registers. Recently, a novel fault model and analysis technique, namely Semi-Permanent Stuck-At (SPSA) fault analysis, has been proposed to evaluate the security of ciphers with combinational circuit implementation of Substitution layer elements, SBox. In this work, we propose optimized techniques to recover the key in a minimum number of ciphertexts in such implementations of lightweight ciphers. Based on the proposed techniques, a key recovery attack on the NIST lightweight cryptography (NIST-LWC) standardization process finalist, Elephant AEAD, has been proposed. The proposed key recovery attack is validated on two versions of Elephant cipher. The proposed fault analysis approach recovered the secret key within 85 − 240 ciphertexts, calculated over 1000 attack instances. To the best of our knowledge, this is the first work on fault analysis attacks on the Elephant scheme. Furthermore, an optimized combinational circuit implementation of Spongent SBox (SBox used in Elephant cipher) is proposed, having a smaller gate count than the optimized implementation reported in the literature. The proposed fault analysis techniques are validated on primary and optimized versions of Spongent SBox through Verilog simulations. Further, we pinpoint SPSA hotspots in the lightweight GIFT cipher SBox architecture. We observe that GIFT SBox exhibits resilience towards the proposed SPSA fault analysis technique under the single fault adversarial model. However, eight SPSA fault patterns reduce the nonlinearity of the SBox to zero, rendering it vulnerable to linear cryptanalysis. Conclusively, SPSA faults may adversely affect the cryptographic properties of an SBox, thereby leading to trivial key recovery. The GIFT cipher is used as an example to focus on two aspects: i) its SBox construction is resilient to the proposed SPSA analysis and therefore characterizing such constructions for SPSA resilience and, ii) an SBox even though resilient to the proposed SPSA analysis, may exhibit vulnerabilities towards other classical analysis techniques when subjected to SPSA faults. Our work reports new vulnerabilities in fault analysis in the combinational circuit implementations of cryptographic protocols.

Fault Networks in Triaxial Tectonic Settings: Analog Modeling of Distributed Continental Extension With Lateral Shortening

AbstractTriaxial deformation is a general feature of continental tectonics, but its controls and the systematics of associated fault networks remain poorly understood. We present triaxial analog experiments mimicking crustal thinning resulting from distributed longitudinal extension and lateral shortening. Contemporary longitudinal extension and lateral shortening are related by the principal horizontal strain ratio (PHSR). We investigate the effect of crustal geometry, rheology and strain rate on deformation localization, faulting regime and pattern, and PHSR in brittle and brittle‐viscous crustal‐scale models. We find that in brittle models the fault networks reflect the basal boundary condition and fault‐density scales inversely with brittle layer thickness. In brittle‐viscous models, as strain rate (ė) decreases, (a) Three fault patterns emerge: conjugate sets of strike‐slip faults (ė > 3 × 10−4 s−1, PHSR > 0.31), sets of parallel oblique normal faults (ė = 0.3–3 × 10−4 s−1, PHSR = 0.15–0.25), horst‐and‐graben system (ė < 0.3 × 10−4 s−1, PHSR < 0.1). (b) The strain localization increases systematically and gradually. We interpret the strain rate dependent of faulting regimes to be controlled by vertical coupling between the model upper mantle and model upper crust resulting in spontaneous permutation of principal stress axes. Rate‐dependency of strain localization can be related to mechanical coupling between the upper and lower crust. We identify the following parameters controlling triaxial tectonic deformation: upper crustal thickness and friction coefficient, lower crustal thickness and viscosity, as well as strain rate. We test our models and predictions against natural prototypes (Tibet, Anatolia, Apennines, and Basin and Range Province) thus providing new perspectives on triaxial deformation.

Hydraulic breakthrough of clay smears due to technical and natural actions

We outlined earlier in this journal by means of finite element simulations how patterns of normal faults arise by a synsedimentary tectonic extension, and how clay smears evolve therein. In the present paper, we show how hydraulic breakthroughs of clay smears can arise so that water, gas and mud rise in faults. Mechanical properties of sand and clay are introduced first for the stable range and then for rupture and internal erosion. Our numerical simulations for the evolution of normal faults and clay smears are discussed in light of critical phenomena. Water assembled in an open-cast mine about 20 years ago as the critical hydraulic gradient in a clay smear dropped to the actual one due to the rapid excavation-induced deformation. The latter led to a critical point under an excavation the slope of which was parallel to a nearby normal fault. Clay smears can also break by earthquakes so that the critical hydraulic gradient drops to the actual one caused by methane with an excess pressure. This can lead to hydraulic breakthroughs and cold eruptions at outcrops of faults.

Revealing Subtle Active Tectonic Deformation: Integrating Lidar, Photogrammetry, Field Mapping, and Geophysical Surveys to Assess the Late Quaternary Activity of the Sava Fault (Southern Alps, Slovenia)

We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe. It is one of the longest faults in the Southern Alps. Using high-resolution digital elevation models from lidar and photogrammetric surveys, we were able to overcome the challenges of assessing fault activity in a region with intense surface processes, dense vegetation, and relatively low fault slip rates. By integrating remote sensing analysis, geomorphological mapping, structural geological investigations, and near-surface geophysics (electrical resistivity tomography and ground penetrating radar), we were able to find subtle geomorphological indicators, detect near-surface deformation, and show distributed surface deformation and a complex fault pattern. Using optically stimulated luminescence dating, we tentatively estimated a slip rate of 1.8 ± 0.4 mm/a for the last 27 ka, which exceeds previous estimates and suggests temporal variability in fault behavior. Our study highlights the importance of modern high-resolution remote sensing techniques and interdisciplinary approaches in detecting tectonic deformation in relatively low-strain rate environments with intense surface processes. We show that slip rates can vary significantly depending on the studied time window. This is a critical piece of information since slip rates are a key input parameter for seismic hazard studies.

Incorporating the Time-synchronous Averaging Method into Vibration Analysis Methodologies for the Detection and Localization of Bearing Defects

Objective: The objectives of this paper are to highlight the significance of vibration analysis, especially in predictive maintenance for rotating machinery, and to emphasize the importance of detecting bearing defects that may result in machinery failure. Methods: The proposed methodology combines the use of time-synchronous averaging (TSA) with existing vibration analysis techniques. TSA involves aligning vibration data with specific events or phases in the machinery's operation, such as shaft rotation. By synchronizing the data in this way, the methodology aims to reduce noise and enhance the signal related to bearing defects, making them more distinguishable. Additionally, the methodology incorporates well-established vibration analysis techniques. These techniques may include frequency analysis, amplitude modulation analysis, waveform analysis, and others commonly used in the field of condition monitoring and predictive maintenance. Results: The results of the analysis begin with waveform analysis, which involves examining the shape and pattern of vibration signals captured from the pinion. This analysis provides valuable insights into the dynamic behavior of the pinion gear, including any variations or abnormalities in its motion. Moreover, the use of synchronized waveforms is crucial in this analysis. By aligning the vibration data with specific events or phases in the gear mesh cycle, such as tooth engagement, the analysis can pinpoint moments when potential faults or wear in the machinery may occur. This synchronization allows for a more precise assessment of the vibration signals, enabling the detection of irregularities that may indicate underlying issues with the pinion or other components of the machinery. Conclusion: A pivotal aspect of the methodology involves envelope spectra analysis, significantly enhancing diagnostic capabilities. This analysis identifies fault patterns that might not be readily apparent in conventional vibration analysis. The incorporation of envelope spectra proves instrumental in proactive maintenance, enabling early detection of potential issues. This, in turn, contributes to the overall reliability and optimization of machinery performance.