Fault Domain Research Articles

Fault domain separation networks: A cross-domain diagnostic method for variable operating conditions in the elevator guidance system

High-speed elevators play a critical role in vertical transportation. However, they may exhibit different working conditions under varying operating conditions, posing challenges for fault diagnosis. We propose a model named fault domain separation networks (FDSN) to tackle the cross-domain diagnostic problem of guidance system by leveraging transfer learning techniques. FDSN comprises three distinct modules that extract invariant fault features and variable operating condition features, and use orthogonal loss function for feature decoupling, achieving effective feature allocation, and mitigating the impact of variable operating conditions on fault diagnosis. To evaluate and demonstrate the efficacy of FDSN, a high-speed elevator platform is constructed, and fault vibration data under various working conditions are simulated to form a comprehensive dataset. The proposed FDSN achieved an average diagnostic accuracy of 92.7% in cross-domain diagnostic tasks, outperforming other comparative methods and demonstrating the remarkable superiority.

Intelligent fault diagnosis for unbalanced battery data using adversarial domain expansion and enhanced stochastic configuration networks

An accurate and efficient fault diagnosis method for battery systems is crucial to ensuring the safety of battery packs. Addressing the issue of insufficient actual fault data in battery operations, this paper proposes an intelligent fault diagnosis method based on feature-enhanced stochastic configuration networks and adversarial domain expansion of imbalanced battery fault data (AFDEM-FESCN). Firstly, we designed an adversarial fault domain data expansion method (AFDEM). By learning the distribution of fault data through adversarial training, the distribution of sample domains is balanced, thereby reducing model bias. Subsequently, we adjusted the distribution of SCN iterative parameters and added a linear feature layer. This enhances the feature extraction capability of the network through distribution overlay, enabling fault diagnosis. Finally, the effectiveness and feasibility of the proposed method were validated through a practical battery system fault diagnosis case, achieving a diagnostic accuracy of 92.1%. Experimental results demonstrate that the AFDEM-FESCN method exhibits good accuracy in battery system fault diagnosis, providing an effective solution to the challenge of imbalanced data in intelligent fault diagnosis.

Dependability Assessment of a Dual-Axis Solar Tracking Prototype Using a Maintenance-Oriented Metric System

This study presents a numerical method for evaluating the maintainability of a dual-axis solar tracking system that can be deployed in residential areas for improved energy production. The purpose of this research manuscript is threefold. It targets the following objectives: (i) First, we present the construction of a self-sufficient dual-axis solar tracking system based on a low-power electronic schematic that requires only one motor driver to control the azimuth and elevation angles of the photovoltaic (PV) panel. The automated system’s main electronic equipment comprises 1 × Arduino Mega2560 microcontroller unit (MCU), 1 × TB6560 stepper driver module, 2 × stepper motors, 2 × relay modules, 1 × solar charge controller, 1 × accumulator, and 1 × voltage convertor. Additional hardware components such as photoresistors, mechanical limit switches, rotary encoders, voltage, and current sensors are also included to complete the automation cycle of the solar tracking system. (ii) Second, the Arduino Mega 2560 prototyping board is replaced by a custom-made and low-cost application-specific printed circuit board (ASPCB) based on the AVR controller. The MCU’s possible fault domain is then further defined by examining the risks of the poor manufacturing process, which can lead to stuck-at-0 (Sa0) and stuck-at-1 (Sa1) defects. Besides these issues, other challenges such as component modularity, installation accessibility, and hardware failures can affect the automated system’s serviceability. (iii) Third, we propose a novel set of maintenance-oriented metrics that combine the previously identified variables to provide a maintainability index (MI), which serves as a valuable tool for evaluating, optimizing, and maintaining complex systems such as solar tracking devices. The experimental data show that the computed MI improves the system’s maintainability and enhances repair operations, increasing uptime.

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.

Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments

Rotating machinery operates continuously for long periods of time under varying conditions in actual industrial environments. The number of fault samples increases with equipment operating time, whereas differences in data distribution are inevitable because of varying operating conditions. As a result, fault diagnosis of rotating machinery poses challenges related to the fault domain increments. In addition, the issue of catastrophic forgetting, where previously learned knowledge significantly affects the model's performance, remains a critical concern. Deep adaptive sparse residual network (DASRN), a new lifelong learning-based method, is proposed to overcome these challenges. The DASRN model is constructed based on an improved deep residual network. The model uses a task-aware dynamic masking strategy to adjust the retention and utilization of blank weights in the network, thereby balancing the model's memory and learning capabilities, mitigating catastrophic forgetting. Furthermore, throughout the entire training process of the model, a dynamic data removal strategy is applied to eliminate easily distinguishable samples. This approach adaptively allocates higher weights to more challenging samples, enhancing the model's learning efficiency while consolidating the sparsity within the model. Finally, the effectiveness of the proposed DASRN is validated on bearing datasets from the Chinese CRH380A high-speed train gearbox and integrated wheelset transmission test rig. Results demonstrate that DASRN provides an effective solution for catastrophic forgetting and domain incremental scenarios in rotating machinery fault diagnosis tasks.

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g. gear fault or outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

The continuation of the Mull Dyke Swarm into the Southern North Sea

We present new geophysical observations of the Mull Dyke Swarm in the Southern North Sea. Two- and three-dimensional seismic reflection and aeromagnetic data were used to map the dykes. The three main dyke groups recognized onshore (Cleveland, Blyth and Hawick–Acklington) are found to extend to varying distances into the North Sea, crossing a number of major crustal-scale fault zones and domain boundaries, with almost no reorientation. The Blyth Dyke Group extends furthest, for a distance of 672 km from the source on Mull. The seismic data show extensive development of pit chain craters above the upper tips of these dykes, which can be approximately dated to the late Paleocene from the ages of crater fills. Volumetric estimates are made of the intrusive volumes associated with each group, ranging from 90 to 202 km 3 . These three main axes of intrusion probably formed in different intrusive events within a c. 1 myr period, from 59 to 58 Ma, during magnetic chron C26R.

DdERT: Research on Named Entity Recognition for Mine Hoist Using a Chinese BERT Model

This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine hoist fault text. Therefore, this study proposes a named entity recognition method based on domain dictionary embedding. The method first uses the fault domain knowledge of the mine hoist to construct a domain-specialized dictionary and generate a word vector of characteristic words. Secondly, the BERT pre-trained language model is used to obtain dynamic word vectors, and a dictionary adapter is loaded to obtain contextual domain lexical features to improve recognition accuracy. Finally, the conditional random field (CRF) is the model classifier to output the annotation sequence with the highest score. The experimental results show that this model achieves better than several baseline models and effectively improves the accuracy of fault named entity identification for mine hoists. The innovation of this study is the combination of domain dictionary embedding and a BERT pre-trained language model, which improves the accuracy and robustness of named entity recognition. Therefore, the results of this study have essential research significance for improving the accuracy of fault named entity identification of mine hoists and the construction of fault knowledge maps.

CausalViT: Domain generalization for chemical engineering process fault detection and diagnosis

Fault detection and diagnosis (FDD) is a promising technology for safe operation, quality control, and profitability improvement in chemical process systems. In practice, chemical process systems usually switch among operating conditions in response to market demands, fatigue, malfunctions of components and environments. However, FDD models often deteriorate when transferred from one operating condition to another. Current widely used FDD models are trained and tested in identical domains (or operating conditions) without emphasis on the capability of domain generalization. Furthermore, they ignore the causality that remains invariant among domains but excessively rely on statistical correlation that varies from domain to domain. This paper presents an FDD model named CausalViT. Causality and a gradient reversal operation are designed to enhance domain generalization. In CausalViT, a Causal Layer is constructed to discover the causal mechanism in features to purify superfluous domain-specific statistical correlation. Then, a domain classifier cooperating with gradient reversal layers is developed to further enhance the domain invariance. To evaluate the CausalViT, comparisons have been made between it and other advanced FDD models in six domains/modes in Tennessee Eastman Process. CausalViT shows superiority from the perspectives of both fault detection ratio and domain generalization, obtaining an average 16% improvement on domain generalization. Dimension reduction visualization shows the significance of domain generalization and gives a deeper insight into CausalViT. Furthermore, the convergence of the model and the effectiveness of components in the model have been analyzed and demonstrated.

A New Cross-Domain Bearing Fault Diagnosis Framework Based on Transferable Features and Manifold Embedded Discriminative Distribution Adaption Under Class Imbalance

Cross-domain fault diagnosis based on transfer learning has been popularly developed to overcome inconsistent data distribution-caused degradation of diagnostic performance. However, the existing methods are typically suffering from a class imbalance of domains and lacking sufficient fault data because it is difficult to obtain the real industrial data of machinery under fault conditions. To address these problems, this work proposes a new cross-domain bearing diagnosis framework based on transferable features and manifold embedded discriminative distribution adaption. First, it applies the maximal overlap discrete wavelet packet transform to process the vibration data and extract different statistics-based features. Then, to enhance the domain adaptation performance, it designs a transferability evaluation based on the adjusted rand index and maximum mean discrepancy to quantify the fault discriminability and domain invariance of the features. After that, it proposes a novel manifold embedded discriminative joint distribution adaptation method to perform cross-domain feature discriminative joint distribution alignment in a Grassmann manifold subspace. Finally, it utilizes a random forest classifier to train the cross-domain fault diagnosis model. To verify the performances of the proposed methods, extensive experiments have been conducted on two real rolling bearing datasets. The results demonstrate that the proposed methods can achieve the desirable diagnosis results and significantly outperform comparative classical transfer learning-based models when there is the class imbalance between source and target domains.

Frictional power dissipation in a seismic ancient fault

The frictional power per unit area Q˙ (product of frictional traction τ and slip rate u˙ in MW m−2) dissipated during earthquakes triggers fault dynamic weakening mechanisms that control rupture nucleation, propagation and arrest. Although of great relevance in earthquake mechanics, Q˙ cannot, with rare exceptions, be determined by geophysical methods. Here we exploit theoretical, experimental and geological constraints to estimate Q˙ dissipated on a fault patch exhumed from 7-9 km depth. According to theoretical models, in polymineralic, silicate rocks the amplitude (< 1 mm) of the grain-scale roughness of the boundary between frictional melt (pseudotachylyte) and host rock decreases with increasing Q˙. The dependence of grain-scale roughness with Q˙ is due to differential melt front migration in the host rock minerals. This dependence is confirmed by friction experiments reproducing seismic slip where pseudotachylytes were produced by shearing tonalite at Q˙ ranging from 5 to 25 MW m−2. In natural pseudotachylytes across tonalites, the grain-scale roughness broadly decreases from extensional to compressional fault domains where lower and higher Q˙ are expected, respectively. Analysis of the natural dataset calibrated by experiments yields Q˙ values in the range of 4-60 MW m−2 (16 MW m−2 average value). These values, estimated in small fault patches, are at the lower end of broad estimates of Q˙ (3-300 MW m−2) obtained from frictional tractions (30-300 MPa) and fault slip rates (0.1-1 m/s) assumed as typical of upper crustal earthquakes.

Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults.

An Intelligent Fault Diagnosis Scheme for Rotating Machinery Based on Supervised Domain Adaptation With Manifold Embedding

In rotating machinery fault diagnosis, domain adaptation (DA) transfer learning-based framework has been attracting great attentions to tackle the problem of inconsistent feature distribution and insufficient labeled fault feature data. However, most of the existing approaches mainly focus on either the cross-domain distribution alignment or manifold subspace learning, which faces two critical limitations: 1) it is hard to overcome the feature distortions when aligning the distribution in the original feature space and 2) subspace learning is insufficient to decrease the distribution divergence. To address the above limitations, this work proposes an intelligent fault diagnosis scheme based on supervised DA with manifold embedding and key features selection. It first applies maximal overlap discrete wavelet packet transform (MODWPT) to process the vibration signals and performs the statistical feature extraction. In order to ensure that the selected key features are conductive to domain adaption, the fault discriminative ability and domain invariance of the features are investigated based on the domain differences and Laplacian score. Then, it presents a new supervised domain adaption with manifold embedding for the distribution alignment in manifold subspace by taking the class information and neighboring relationships into account. Finally, an intra classifier is learned to predict the unlabeled target domain. The proposed fault diagnosis scheme is evaluated using a set of practical data sets of motor and bearing. The extensive experimental results demonstrate that it significantly outperforms the comparative models and achieves much more effective fault diagnosis under different working conditions.

New 3D fault model for eastern Flanders (Belgium) providing insights on the major deformation phases in the region since the late Paleozoic

A new 3D fault model was created for the Upper Paleozoic to Cenozoic subsurface in eastern Flanders (Belgium). The model was based on the integration of 2D seismic interpretations, borehole and gravimetric data, as well as coal mining and topographic maps. Four major tectonic phases, taking place during the latest Paleozoic, Jurassic, Late Cretaceous and Cenozoic, shaped the fault network. The latest Paleozoic phase was transpressional and expressed by local folding of the Carboniferous strata underneath the base Permian unconformity. The largest anticlines were asymmetric, with amplitudes of up to 500 m and SSW-NNE oriented axal planes. The subsequent Jurassic phase caused normal faulting across the area, resulting in the development of the northeast-dipping, western border fault systems of the Campine Basin and Roer Valley Graben. Fault strikes were predominantly WNW-ESE to NNW-SSE. Both fault directions were most likely inherited from early Carboniferous and older tectonic phases. Jurassic fault reactivation explains the activity along fault populations with different orientations as well as the linkage of these faults into long (>10 km) systems, despite displaying generally relatively small vertical throws (<500 m). Soft-linked boundaries (accommodation zones) between Jurassic fault domains are located on top of the largest latest Paleozoic transpressional anticlines.The Late Cretaceous major tectonic phase was compressional and involved inversion of the Roer Valley Graben with subsequent subsidence and carbonate deposition in the flanking Campine Block.During the Cenozoic extensional phase, most normal faults that developed were rooted in Jurassic faults and therefore displayed similar WNW-ESE to NNW-SSE strikes. Cenozoic extension was, however, weaker than Jurassic extension; Cenozoic fault numbers, lengths and vertical throws were smaller than during the Jurassic phase. Also during the Cenozoic phase, faulting was more localized onto the pre-existing Roer Valley Graben border fault system compared to the more diffuse Jurassic faulting. The Cenozoic faults were generally planar, whereas some of its Jurassic roots near and in the border fault system of the Roer Valley Graben were listric.

Intelligent fault diagnosis of rolling bearings under varying operating conditions based on domain-adversarial neural network and attention mechanism

As a domain adaptation method, the domain-adversarial neural network (DANN) can utilize the adversarial learning of the feature extractor and domain discriminator to extract the domain-invariant features, thus realizing fault identification of rolling bearings. In the cross-domain diagnosis of rolling bearing faults, how to obtain fault-related discriminative domain-invariant features from the noisy signals is a key to improving the diagnostic result. In response to this, this paper proposes an intelligent diagnosis model based on the DANN and attention mechanism to identify rolling bearing faults. In order to relieve the influence of noisy data on feature extraction and improve the quality of the learned features, the ensemble empirical mode decomposition (EEMD) is first adopted to denoise the raw sample data to weaken the influence of noise on feature extraction. Secondly, a feature extractor composed of three feature extraction modules in series is designed, and each feature extraction module is composed of a convolution layer, an attention mechanism module and a pooling layer. The feature extractor with attention mechanism enables the model to learn and retain key features related to the faults during training process. Meanwhile, the global average pooling layer is used to replace some fully connected layers in the fault classifier and domain discriminator to reduce model parameters and avoid model overfitting. Finally, the analysis using two sets of rolling bearing experimental about the performance of the presented method show that the proposed method has the potential to become a promising tool for the fault diagnosis of rolling bearings.

Weighted quantile discrepancy-based deep domain adaptation network for intelligent fault diagnosis

Numerous intelligent fault diagnosis models do not perform well owing to the lack of labeled data with fault information and domain shifts under different working conditions. In this study, a weighted quantile discrepancy (WQD) metric is proposed and integrated into a deep adversarial learning framework to develop a deep weighted quantile domain adaptation network (DWQDAN) to tackle the aforementioned fault diagnosis challenges. The WQD metric first introduces quantile theory to consider the influence of different quantiles on domain adaptation concerning fault diagnosis. It can integrate the complete distribution information to obtain more accurate distribution matching characteristics with less computational complexity compared with classical metrics. By incorporating the dynamic WQD and adversarial learning, the DWQDAN can effectively learn domain-invariant features to perform different domain adaptation tasks. Its utility is illustrated using empirical applications with the public CWRU and Paderborn bearing datasets. The results show that DWQDAN performs better than some state-of-the-art methods. Adjustable parameters, including the number of quantiles and corresponding weights, provide an effective method for practical fault diagnosis.

Research on a Multisource Domain Improved Fault Diagnosis Method of the Rotor System

In this paper, rotor systems of the rotating machinery such as steam turbines, centrifugal compressors and flue gas turbines are selected as the research objects. At present, most of the rotor system fault diagnosis methods based on artificial intelligence algorithms are in the laboratory research stage, and there is still a gap from the actual industrial application. Therefore, the multi-source domain improved fault diagnosis (MSDIFD) method for satisfying engineering applications is proposed in this paper. Firstly, typical labeled data are selected to construct a multi-source domain training feature space. Then, commonality fault features are extracted and screened by the improved adaptive variational mode decomposition (IAVMD), and the feature reconstruction signal is automatically output. Next, the reinforced semi-supervised transfer component analysis (RSSTCA) method based on enhanced kernel function is employed to narrow the disparity between feature vectors of cross domain data. Finally, typical failure case data and real-time monitoring data are used as the training data and test data of the model, respectively, and an ensemble fault recognition classifier is constructed to achieve failure mode identification of the rotor system. Using 40 groups of typical fault engineering cases under different equipment and different operating conditions, the proposed rotor fault identification method has been verified and compared with five published fault identification methods. The results indicate that the proposed method possesses more excellent fault diagnosis accuracy and domain generalization performance, and the MSDIFD method has good application and promotion value for solving cross equipment, cross working condition, and cross domain diagnostic tasks.

Enhanced hierarchical symbolic dynamic entropy and maximum mean and covariance discrepancy-based transfer joint matching with Welsh loss for intelligent cross-domain bearing health monitoring

Domain adaptation (DA) as a critical and valuable tool is devoted to minimizing the distribution discrepancy across domains, which has been successfully utilized in intelligent bearing health monitoring. In particular, transfer joint matching (TJM) is a promising transfer learning strategy, especially when the domains differ considerably. In the TJM, the maximum mean discrepancy (MMD) is usually employed to assess the discrepancy of distributions in reproducing kernel Hilbert space (RKHS). However, the MMD has been proved to lose some useful statistical information in the RKHS. Hence, an improved TJM approach, called maximum mean and covariance discrepancy-based transfer joint matching with Welsh loss (MMCD-WTJM), is proposed in this article. In MMCD-WTJM, maximum mean and covariance discrepancy (MMCD) instead of MMD is incorporated into the TJM to match more statistical information in RKHS. Meanwhile, the Welsh loss function is incorporated into the TJM to enhance the stability of the model. Besides, we develop enhanced hierarchical symbolic dynamic entropy (EHSDE) to extract a more useful feature representation. Eventually, a novel hybrid cross-domain bearing fault identification based on EHSDE and MMCD-WTJM is developed. The analysis results of eight transfer bearing fault identification tasks demonstrate that the developed approach has an excellent capacity in intelligent bearing health monitoring under varying operation conditions and different machines. Compared with the existing entropy-based fault feature extraction approaches and domain adaptation-based transfer fault diagnosis methods, the presented bearing health monitoring scheme has remarkable strengths in recognition accuracy.

Assessing test suites of extended finite state machines against model‐ and code‐based faults

SummaryTests can be derived from extended finite state machine (EFSM) specifications considering the coverage of single‐transfer faults, all transitions using a transition tour, all‐uses, edge‐pair, and prime path with side trip. We provide novel empirical assessments of the effectiveness of these test suites. The first assessment determines for each pair of test suites if there is a difference between the pair in covering EFSM faults of six EFSM specifications. If the difference is found significant, we determine which test suite outperforms the other. The second assessment is similar to the first; yet, it is carried out against code faults of 12 Java implementations of the specifications. Besides, two assessments are provided to determine whether test suites have better coverage of certain classes of EFSM (or code) faults than others. The evaluation uses proper data transformation of mutation scores and p‐value adjustments for controlling Type I error due to multiple tests. Furthermore, we show that subsuming mutants have an impact on mutation scores of both EFSM and code faults; and accordingly, we use a score that removes them in order not to invalidate the obtained results. The assessments show that all‐uses tests were outperformed by all other tests; transition tours outperformed both edge‐pair and prime path with side trips; and single‐transfer fault tests outperformed all other test suites. Similar results are obtained over the considered EFSM and code fault domains, and there were no significant differences between the test suites coverage of different classes of EFSM and code faults.

Structure and kinematics of active faulting in the Hope-Kelly and Alpine Fault intersection zone, South Island, New Zealand

Using lidar, photogrammetry and field studies, we investigate the structure and kinematics of the Hope, Kelly, and Alpine faults where they converge and intersect along the Australian-Pacific plate boundary in New Zealand. Approximately 50% of the reverse-dextral slip rate of the Alpine fault in the west transfers to the dextral central Hope Fault in the east via a horsetail splay zone of distributed oblique dextral-normal shear forming the Hope-Kelly fault system. The westernmost portion of the Hope fault is misoriented for dextral slip and is likely an extensional fault that accommodates the Alpine fault slip rate change across the intersection zone. As it transitions to an extensional fault, most of the strike-slip of the Hope fault is transferred southward onto Kelly fault strands via incipient, linking fault domains of coeval NE-SW and EW-striking dextral faults. The transition from the narrow zone defining the central Hope Fault to dispersed horsetail splays of the Hope-Kelly fault system is located immediately above the eastward down-dip projection of the Alpine Fault at the base of the seismogenic zone. This suggests that structural interactions among these faults within seismogenic depths control surface rupture geometries and kinematics. Coeval intermingling of surface rupture traces transecting latest Pleistocene to Holocene landforms indicates ongoing structural interactions in active faulting at the intersection of the Hope-Kelly fault splays with the Alpine fault. The transition from dextral to dextral normal kinematics on the horsetail splay accommodates differential orogenic growth through geometrically and kinematically efficient faulting that allows extension of the Alpine fault hanging wall parallel to strike, enabling the slip rate change between the central and northern Alpine fault sections.