Realistic Fault Research Articles

Dynamic Rupture Simulation Reproduces Spontaneous Multifault Rupture and Arrest During the 2016Mw7.9 Kaikoura Earthquake

AbstractThe 2016 Kaikoura (New Zealand) earthquake is characterized as one of the most complex multifault rupture events ever observed. We perform dynamic rupture simulations to evaluate to what extent relatively simple forward models accounting for realistic fault geometry can explain the characteristics of coseismic observations. Without fine parameter tuning, our model reproduces many observed features including the multifault rupture, overall slip distribution, and the locations of the maximum slip and rupture arrest. In particular, our model shows spontaneous arrest of dynamic rupture at the both ends of the ruptured fault system due to smaller prestress levels expected from a regional tectonic stress field. Both the simulated and the observationally inferred source time functions show similar double peaks with a larger second peak. The results illuminate the importance of the 3‐D fault geometry in understanding the dynamics of complex multifault rupture.

Fault detection in distillation column using NARX neural network

Fault detection in the process industries is one of the most challenging tasks. It requires timely detection of anomalies which are present with noisy measurements of a large number of variable, highly correlated data with complex interactions and fault symptoms. This study proposes the robust fault detection method for the distillation column. Fault detection and diagnosis (FDD) for process monitoring and control has been an effective field of research for two decades. This area has been used widely in sophisticated engineering design applications to ensure the proper functionality and performance diagnosis of advanced and complex technologies. Robust fault detection of the realistic faults in distillation column in dynamic condition has been considered in this study. For early detection of faults, the model is based on nonlinear autoregressive with exogenous input (NARX) network. Tapped delays lines (TDLs) have been used for the input and output sequences. A case study was carried out with three different fault scenarios, i.e., valve sticking at reflux and reboiler, and tray upset. These faults would cause the product degradation. The normal data (no fault) is used for the training of neural network in all three cases. It is shown that the proposed algorithm can be used for the detection of both internal and external faults in the distillation column for dynamic system monitoring and to predict the probability of failure.

A critical review of fault modeling of HVAC systems in buildings

Buildings consumed about 40% of primary energy and 70% of the electricity in the U.S. It is well known that most buildings lose a portion of their desired and designed energy efficiency in the years after they are commissioned or recommissioned. Majority of the Heating, Ventilation, and Air-Conditioning (HVAC) systems have multiple faults residing in the systems causing either energy, thermal comfort, or indoor air quality penalties. There are hundreds of fault detection and diagnostics (FDD) algorithms available, but there is lacking a common framework to assess and validate those FDD algorithms. Fault modeling is one of the key components of such a framework. In general, fault modeling has two purposes: testing and assessment of FDD algorithms, and fault impacts analysis in terms of building energy consumption and occupants’ thermal comfort. It is expected that fault ranking from the fault impact analysis can facilitate building facility managers to make decisions. This paper provides a detailed review of current state-of-the-art for the fault modeling of HVAC systems in buildings, including fault model, fault occurrence probability, and fault simulation platform. Fault simulations considering fault occurrence probability can generate realistic faulty data across a variety of faulty operating conditions, and facilitate testing and assessment of different FDD algorithms. They can also help the fault impact study. Three research gaps are identified through this critical literature review: (1) The number of available fault models of HVAC systems is still limited. A fault model library could be developed to cover all common HVAC faults for both traditional and non-traditional HVAC systems. (2) It is imperative to include the fault occurrence probability in fault simulations for a realistic fault impacts analysis such as fault ranking. (3) Fault simulation platforms need further improvements to better facilitate the fault impact analysis.

Fault detection, classification, and location for active distribution network based on neural network and phase angle analysis

ABSTRACTThe improved radial basis function (RBF) method utilizes an orthogonal regression matrix to produce an artificial neural network structure based on regularized least square. The phase angle and amplitude signal of fault voltage and current are extracted based on frequency domain analysis. The proposed method adopts the fault signal for fault diagnosis synchronously. The IEEE 13-bus active distribution network (ADN) simulation model is set up in Matlab. Test results demonstrate that accuracy of the fault diagnosis can reach 98.07% and the response time of the fault classification method is less than 0.04s. The wavelet neural network (WNN) model is developed to extract the maximum decomposition level and time series behavior. The WNN method can resist noise effects and improve the fault classification accuracy by 4.3%. The effect of fault type and fault resistance on the fault location method is researched. The fault simulation result shows that the proposed method can locate a fault precisely and synchronously. The improved RBF method can diagnose the fault section, classify the fault type and locate a fault accurately in ADN. The research is significant to maintain system stability against realistic fault and network restore.

Off-fault plasticity in three-dimensional dynamic rupture simulations using a modal Discontinuous Galerkin method on unstructured meshes: implementation, verification and application

The dynamics and potential size of earthquakes depend crucially on rupture transfers between adjacent fault segments. To accurately describe earthquake source dynamics, numerical models can account for realistic fault geometries and rheologies such as nonlinear inelastic processes off the slip interface. We present implementation, verification and application of off-fault Drucker–Prager plasticity in the open source software SeisSol (www.seissol.org). SeisSol is based on an arbitrary high-order derivative modal Discontinuous Galerkin method using unstructured, tetrahedral meshes specifically suited for complex geometries. Two implementation approaches are detailed, modelling plastic failure either employing subelemental quadrature points or switching to nodal basis coefficients. At fine fault discretizations, the nodal basis approach is up to six times more efficient in terms of computational costs while yielding comparable accuracy. Both methods are verified in community benchmark problems and by 3-D numerical h- and p-refinement studies with heterogeneous initial stresses. We observe no spectral convergence for on-fault quantities with respect to a given reference solution, but rather discuss a limitation to low-order convergence for heterogeneous 3-D dynamic rupture problems. For simulations including plasticity, a high fault resolution may be less crucial than commonly assumed, due to the regularization of peak slip rate and an increase of the minimum cohesive zone width. In large-scale dynamic rupture simulations based on the 1992 Landers earthquake, we observe high rupture complexity including reverse slip, direct branching and dynamic triggering. The spatiotemporal distribution of rupture transfers are altered distinctively by plastic energy absorption, correlated with locations of geometrical fault complexity. Computational cost increases by 7 per cent when accounting for off-fault plasticity in the demonstrating application. Our results imply that the combination of fully 3-D dynamic modelling, complex fault geometries and off-fault plastic yielding is important to realistically capture dynamic rupture transfers in natural fault systems.

Wind Turbine Condition Monitoring Strategy through Multiway PCA and Multivariate Inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [ 1 % , 13 % ] , the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

Fuzzy model-based faults diagnosis of the wind turbine benchmark

Modern Wind turbines are complex systems which can be affected by malfunctions, regarding actuators, sensors, and components. An early faults detection and isolation are then highly required. For this aim, in the present paper, a robust fault detection and isolation (FDI) scheme is developed for a 4.8 MW wind turbine described via Takagi–Sugeno (T–S) multiple models. The FDI method is developed by using Fuzzy sliding mode observer as residual generators. Regarding the evaluation task, a set of pre-defined thresholds designed to indicate the occurrence of faults. Following that, a bank of residual generators is employed appropriately to determine fault type and location. Finally, the wind turbine benchmark is used to evaluate the performances of the proposed method against a set of realistic fault scenarios.

Application of Model Reference Adaptive PI Control to FTCC of a Wind Farm

High reliability and availability are crucial for cost-effective operation of any wind farm. In this regard, effective schemes for fault detection, diagnosis and accommodation need to be developed to improve the reliability and availability of wind turbines and consequently wind farms (groups of wind turbines). To address this issue, this paper employs an adaptive proportional-integral (PI) control approach in a cooperative framework that is oriented to the design and development of a novel fault-tolerant cooperative control (FTCC) scheme in a wind farm. Applied to a wind farm, this scheme handles decreased power generation faults that may be caused by icing or debris build-up on the blades over time. Different simulations on a high-fidelity offshore wind farm benchmark show the effectiveness and satisfactory performance of the proposed scheme in the presence of wind turbulences, measurement noises and realistic fault scenarios.

Numerical and experimental diagnosis of complex rotor system by time-frequency techniques

This paper describes the application of Discrete Wavelet Transform (DWT) to identify various types of nonlinear damage caused by, unbalance, rotor-stator contact and a breathing crack in rotating machinery. Multiple faults have been investigated based on numerical and experimental signal analysis using Fast Fourier Transform (FFT) and DWT. A four degree of freedom fully coupled model of the rotor-stator system that includes the nonlinear damage in the rotor vibrations was established using Energy principles. Existence of high system nonlinearity could not allow exhaustive discrimination of rub and crack by classical FFT. Therefore, the DWT was employed. The results provide detailed feature analysis of the fault signals. Practical vibration measurements through a data acquisition system interfaced with Rotor Kit-4 and crack simulator provided the test data. Experimental Time-Frequency analysis gave more realistic faults responses with variable faults features. Irregularity of orbit, harmonic peaks in the presence of rub and crack were unique and distinguished periodic motion from other types of motion. The presence of a crack shifted the critical speed location and exhibited sub-harmonic components, which were more prominent with rub in vibration response. The detailed decomposition signal by DWT method established inherent feature patterns that effectively discriminated the multiple faults.

Model-Based Fault-Tolerant Pitch Control of an Offshore Wind Turbine

Given the importance of reliability and availability issue in wind turbines, the current paper presents the design and development of a novel active fault-tolerant control scheme for an offshore wind turbine. The proposed scheme tolerates the effects of any possible fault that may happen in pitch actuators of wind turbine blades. A model-based fault detection and diagnosis system provides fault information that are accurate enough for compensation of fault effects in the pitch control loop. The effectiveness of the proposed scheme is finally evaluated through simulations on an advanced offshore wind turbine benchmark model in the presence of wind turbulences, measurement noises, and realistic fault scenarios.

Improved definition of faults in the Groningen field using seismic attributes

AbstractThe Groningen field is the largest onshore gas field in Europe. The gas-bearing section comprises aeolian and fluvial Rotliegend sandstones of Permian age and fluvial sandstones of Carboniferous age. Continuous production since 1963 has led to induced seismicity starting in the early 1990s.Faults at reservoir level play a major role in the seismicity in the Groningen field. Fault slip is expected when shear traction is sufficient to overcome frictional resistance on the fault surface. Clear insights into which faults and fault segments are most susceptible to seismicity could be used to optimise production and minimise the seismic risk. To gain these insights, a detailed and realistic fault model is required as input to both statistical analyses on seismicity and deterministic geomechanical modelling of seismogenic behaviour along fault planes. Geometrical seismic attributes and, subsequently, fault planes were extracted from a reprocessed and depth-imaged 3D seismic volume. This resulted in a detailed visualisation of the faults at reservoir level, with extension into the deeper strata below the reservoir in many cases. They represent fault planes with realistic dimensions and shapes. The fault map based on seismic attributes suggests the presence of faults that have not been included in studies on Groningen seismicity before. The improved fault definition correlates with recent earthquake hypocentres. We conclude that a detailed fault model of the Groningen field can be created using 3D seismic attributes and that detailed 3D fault planes can be extracted from these attributes. The results can be used as input to statistical and geomechanical analyses on seismicity.

Application of FMRAC to fault‐tolerant cooperative control of a wind farm with decreased power generation due to blade erosion/debris buildup

SummaryWind energy has shown a remarkable potential for fulfilling the increasing world's energy demand in a clean and sustainable way. The wind energy industry has installed increasingly sophisticated and larger wind turbines particularly in offshore regions to capture such energy as efficiently and cost effectively as possible. The rapid growth in size and capacity of wind turbines together with harsh climate conditions and limited accessibility in offshore regions all result in higher failure rates and increased maintenance requirements and costs. Such difficulties motivate the use of advanced fault detection and diagnosis and fault‐tolerant control schemes in wind farms to improve their reliability and availability. Given the importance of this issue, this paper uses a fuzzy model reference adaptive control approach in a cooperative framework that is oriented to the design and development of a novel fault‐tolerant cooperative control scheme in a wind farm. This scheme handles decreased power generation faults in a wind farm caused by turbine blade erosion and debris buildup on the blades over time. The effectiveness and performance of the proposed scheme is demonstrated by a series of simulations on an advanced large offshore wind farm benchmark model in the presence of wind turbulence, measurement noise, load variations, and realistic fault scenarios.

Challenges and solutions for MV and LV protection in grids with large amount of distributed generation – a final report from the German research project ProFuDis

The ongoing installation of distributed generation (DG) in low- (LV) and medium-voltage (MV) grids might be prospectively limited by restrictions of today's protection systems due to the DG's impact onto the system's fault behaviour. Investigations show that today's DG shares do not yet lead to protection malfunctions. A reasonable future increase of the share of DG and the increasing complexity of the grids will partially pose protection-challenges in the near future though. Therefore, detailed protection planning becomes increasingly important in the future, using enhanced calculation, modelling and evaluation approaches, as developed within the project Protection for Future Distribution Systems (ProFuDiS). While the future protection-challenges in German MV grids can widely be solved using altered parametrisation and application of the present digital protection devices, the usage of classical NH fuses for LV grid protection will be possible for most, but not all grids any more. While sufficient, but rather complex digital protection approaches are already available, the development of innovative, more cost-efficient solutions for LV protection should be carried on. Additionally more precise regulations for the fault behaviour of DG should be stated to allow effective protection planning and reliable and realistic fault simulation.

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics.

The importance of structural model availability on seismic interpretation

Interpretation of faults in seismic images is central to the creation of geological models of the subsurface. The use of prior knowledge acquired through learning allows interpreters to move from singular observations to reasoned interpretations based on the conceptual models available to them. The amount and variety of fault examples available in textbooks, articles and training exercises is therefore likely to be a determinant factor in the interpreters' ability to interpret realistic fault geometries in seismic data. We analysed the differences in fault type and geometry interpreted in seismic data by students before and after completing a masters module in structural geology, and compared them to the characteristics of faults represented in the module and textbooks. We propose that the observed over-representation of normal-planar faults in early teaching materials influences the interpretation of data, making this fault type and geometry dominant in the pre-module interpretations. However, when the students were exposed to a greater range in fault models in the module, the range of fault type and geometry increased. This work explores the role of model availability in interpretation and advocates for the use of realistic fault models in training materials.

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis–A comparative study

Even though various frameworks exist for reasoning under uncertainty, a realistic fault diagnosis task does not fit into any of them in a straightforward way. For each framework, only part of the available data and knowledge is in the desired format. Moreover, additional criteria, like clarity of inference and computational efficiency, require trade-offs to be made. Finally, fault diagnosis is usually just a subpart of a larger process, e.g. condition-based maintenance. Consequently, the final goal of fault diagnosis is not (just) decision making, and the outcome of the diagnosis process should be a suitable input for the subsequent reasoning process. In this paper, we analyze how a knowledge-based diagnosis task is influenced by uncertainty, investigate which additional objectives are of relevance, and compare how these characteristics and objectives are handled in two well-known frameworks, namely the Bayesian and the Dempster-Shafer reasoning framework. In contrast to previous works, which take the reasoning method as the starting point, we start from the application, knowledge-based fault diagnosis, and examine the effectiveness of different reasoning methods for this specific application. It is concluded that the suitability of each reasoning method highly depends on the problem under consideration and on the requirements of the user. The best framework can only be assigned given that the problem (including uncertainty characteristics) and the user requirements are completely known.

Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches

Given the importance of reliability and availability in wind farms, this paper focuses on the development of fault diagnosis and fault-tolerant control schemes in a cooperative framework (referred to as active fault-tolerant cooperative control) at the wind farm level against the decreasing power generation caused by turbine blade erosion and debris build-up on the blades over time. In more details, the paper presents a novel integrated fault detection and diagnosis and fault-tolerant control approach oriented to the design and development of two active fault-tolerant cooperative control schemes for an offshore wind farm. Each of the schemes employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the wind farm. The effectiveness and performance of the proposed schemes are evaluated and compared using different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios.

An RTOS-based Fault Injection Simulator for Embedded Processors

Evaluating embedded systems vulnerability to faults injection attacks has gained importance in recent years due to the rising threats they bring to chips security. The task is particularly important for micro-controllers since they have lower resistance to fault attacks compared to hardware-based cryptosystems. This paper reviews recent embedded fault injection simulators from literature and presents an embedded high-level fault injection mechanism based on a Real-Time Operating System (RTOS). The approach aims to be architecture-independent and portable to 32-bit micro-controllers and embedded processors. The proposed mechanism, primarily targets realistic fault attack scenarios on memory locations, is adapted to timed and event-based fault injection. A Differential Fault Attack (DFA) was mounted on a popular ARM-based micro-controller running FreeRTOS to illustrate the proposed mechanism. The aim is also to bridge the embedded fault injection simulation mechanism efficiently to a computer-based cryptanalysis and to highlight the importance of physically protecting the memory and integrating data-specific countermeasures.

Model-based Active Fault-tolerant Cooperative Control in an Offshore Wind Farm

Abstract Given the importance of reliability and availability in wind farms, this paper focuses on the development of an integrated fault diagnosis and fault-tolerant control scheme in a cooperative framework at wind farm level against the decreased power generation caused by turbine blade erosion and debris build-up on the blades over time. More precisely, the paper presents a novel integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) approach oriented to the design and development of an active fault-tolerant cooperative control (AFTCC)scheme for an offshore wind farm. The scheme employs a fault detection and diagnosis system to provide accurate and timely diagnosis information to be used in an appropriate automatic signal correction algorithm for accommodation of faults in the farm. The effectiveness and performance of the proposed scheme are evaluated and analyzed by different simulations on a high-fidelity offshore wind farm benchmark model in the presence of wind turbulences, measurement noises and realistic fault scenarios.

Hysteresis‐Based Design of Dynamic Reference Trajectories to Avoid Saturation in Controlled Wind Turbines

AbstractThe main objective of this paper is to design a dynamic reference trajectory based on hysteresis to avoid saturation in controlled wind turbines. Basically, the torque controller and pitch controller set‐points are hysteretically manipulated to avoid saturation and drive the system with smooth dynamic changes. Simulation results obtained from a 5MW wind turbine benchmark model show that our proposed strategy has a clear added value with respect to the baseline controller (a well‐known and accepted industrial wind turbine controller). Moreover, the proposed strategy has been tested in healthy conditions but also in the presence of a realistic fault where the baseline controller caused saturation to finally conduct to instability.