Academic literature on the topic 'Continuum fault models'

Understanding electrical systems and fire safety protocols in the operating room is fundamental to patient and staff safety. Modern operating rooms are designed to reduce the risk of electrical hazards. Line isolation transformers were developed in the era of explosive anesthetics to reduce the risk of sparks and macro-shock. Isolated electrical supplies are still used in operating rooms because they allow surgery to continue while the line isolation alarm is activated and the source of the fault is investigated and deactivated. Ground fault circuit breaker interrupters may also be used in operating rooms, but if a fault is detected, they will deactivate the electrical circuit, which may be disruptive to surgical or anesthetic care. Micro-shock occurs when a small amount of current is delivered directly to the myocardium via an indwelling catheter or pacing wire. Operating room fires, though relatively rare, can cause devastating patient injury but are largely preventable.

Ireland has enjoyed continuous democratic government for almost a century, an unusual experience among countries that gained their independence in the twentieth century. But the way this works has changed dramatically over time. Ireland’s colonial past has had an enduring influence over political life, enabling stable institutions of democratic accountability, while also shaping economic underdevelopment and persistent emigration. More recently, membership of the EU has brought about far-reaching transformation across almost all aspects of life. But the paradoxes have only intensified. Now one of the most open economies in the world, Ireland has experienced both rapid growth and a severe crash in the wake of the Great Recession. By some measures, Ireland is among the most affluent countries in the world, yet this is not the lived experience for many of its citizens. Ireland is an unequivocally modern state, yet public life continues to be marked by ideas and values in which tradition and modernity are uneasy bedfellows. It is a small state that has ambitions to carry more weight on the world stage. Ireland continues to be deeply connected to Britain through ties of culture and trade, now matters of deep concern post-Brexit. And the old fault lines between North and South, between Ireland and Britain, which had been at the core of one of Europe’s longest and bloodiest civil conflicts, risk being reopened. These key issues are teased out in this book, making it the most comprehensive volume on Irish politics to date.

This chapter focuses on the unintended consequences of the post-crisis mandate that over-the-counter (OTC) derivatives be cleared through centralized clearinghouses in an effort to reduce counterparty and systemic risk. Although central clearing has been widely implemented, it has reproduced many of the same characteristics of financial markets that contributed to the 2008 crisis: concentrated risk, moral hazard, and a reliance on faulty risk models. What accounts for the recalcitrance of the OTC derivatives market to a regulatory change? The chapter argues that focusing on the technologies and practices used to govern derivatives markets helps explain the absence of more radical regulatory policy shifts in derivatives regulation. Although there has been a significant shift in who regulates OTC markets, much less has changed at the level of the specific practices that govern these markets, and the chapter examines the continued reliance on netting, collateralization, and risk modeling within clearinghouses.

While the critical and popular attention afforded to twenty-first century young adult literature has exponentially increased in recent years, the texts selected for discussion in both classrooms and scholarship has remained static and small. Twilight, The Hunger Games, The Fault in Our Stars, and The Hate U Give dominate conversations among scholars and critics—but they are far from the only texts in need of analysis. Beyond the Blockbusters: Themes and Trends in Contemporary Young Adult Fiction offers a necessary remedy to this limited perspective by bringing together a series of essays about the many subgenres, themes, and character types that have been overlooked and under-discussed until now. The collection tackles a diverse range of subjects—modern updates to the marriage plot; fairy tale retellings in dystopian settings; stories of extrajudicial police killings and racial justice—but is united by a commitment to exploring the large-scale generic and theoretical structures at work in each set of texts. As a collection, Beyond the Blockbusters is an exciting glimpse of a field that continues to grow and change even as it explodes with popularity, and would make an excellent addition to the library of any scholar, instructor, or reader of young adult literature.

AbstractBatch or semi-batch processes have been utilized to produce high-value-added products in the biological, food, semi-conductor industries. Batch process, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes than that of continuous processes, due to the common natures of batch process: non-steady, time-varying, finite duration, and nonlinear behaviors. The lack of exact monitoring model in most batch processes leads that an operator cannot identify the faults when they occurred. Therefore, effective techniques for monitoring batch process exactly are necessary in order to remind the operator to take some corrective actions before the situation becomes more dangerous.

Genetic Algorithms are important techniques to solve many NP-Complete problems related to distributed computing and its application domains. Genetic algorithm-based fault diagnoses in distributed computing systems have been a feasible methodology to solve diagnosis problems recently. Distributed embedded systems consisting of sensors, actuators, processors/microcontrollers, and interconnection networks are one class of distributed computing systems that have long been used, staring from small-scale home appliances to large-scale satellite systems. Some of their applications are in safety-critical systems where occurrence of faults can result in catastrophic situations for which fault diagnosis in such systems are very important. In this chapter, different types of faults, which are likely to occur in distributed embedded systems and a GA-based methodology to solve these problems along with the performance analysis of fault diagnosis algorithm have been presented. Nevertheless, the diagnosis algorithm presented here is well suitable for general purpose distributed computing systems with appropriate modification over system and fault model. In fact, this book chapter will enable the reader not only to study various aspects of fault diagnosis techniques but will also provide insight to build robust systems to allow for continued normal service despite the occurrence of failures.

With the improvement of automation and intelligence degree of unmanned aerial vehicle, its application scenarios and service scope continue to expand. The UAV system is complex and the task environment is changeable, which poses new challenges to its safety and reliability. Fault prediction and Health management (PHM) technology can effectively reduce the risk of mission interruption caused by faults, and improve the quality of UAV mission throughout its life cycle. Firstly, the framework of UAV PHM technology is proposed based on the basic concepts of UAV and PHM technology, and then the research status of UAV fault diagnosis and fault prediction technology is analyzed and summarized. Finally, the challenges of UAV fault diagnosis and prediction technology are discussed. In addition, the development trend of UAV PHM technology is summarized from four aspects: failure mechanism basis, condition monitoring technology, fault model construction and intelligent technology application, aiming to provide certain reference for the research and development of the new generation of UAV PHM technology.

This chapter discusses the main principles of the creation and use of a classifier in order to predict the interpretation of an unknown data sample. Classification offers the possibility to learn and use learned information received from previous occurrences of various normal and fault modes. This process is continuous and can be generalized to cover the diagnostics of all objects that are substantially of the same type. The effective use of a classifier includes initial training with known data samples, anomaly detection, retraining, and fault detection. With these elements an automated, a continuous learning machine diagnostics system can be developed. The main objective of such a system is to automate various time intensive tasks and allow more time for an expert to interpret unknown anomalies. A secondary objective is to utilize the data collected from previous fault modes to predict the re-occurrence of these faults in a substantially similar machine. It is important to understand the behaviour and functioning of a classifier in the development of software solutions for automated diagnostic methods. Several proven methods that can be used, for instance in software development, are disclosed in this chapter.

The production, transmission, and distribution of energy can only be made stable and continuous by detailed analysis of the data. The energy demand needs to be met by a number of optimization algorithms during the distribution of the generated energy. The pricing of the energy supplied to the users and the change for investments according to the demand hours led to the formation of energy exchanges. This use costs varies for active or reactive powers. All of these supply-demand and pricing plans can only be achieved by collecting and analyzing data at each stage. In the study, an electrical power line with real parameters was modeled and fault scenarios were created, and faults were determined by artificial intelligence methods. In this study, both the power flow of electrical power systems and the methods of meeting the demands were investigated with big data, machine learning, and artificial neural network approaches.

The production, transmission, and distribution of energy can only be made stable and continuous by detailed analysis of the data. The energy demand needs to be met by a number of optimization algorithms during the distribution of the generated energy. The pricing of the energy supplied to the users and the change for investments according to the demand hours led to the formation of energy exchanges. This use costs varies for active or reactive powers. All of these supply-demand and pricing plans can only be achieved by collecting and analyzing data at each stage. In the study, an electrical power line with real parameters was modeled and fault scenarios were created, and faults were determined by artificial intelligence methods. In this study, both the power flow of electrical power systems and the methods of meeting the demands were investigated with big data, machine learning, and artificial neural network approaches.

This paper proposes a predictive redundancy method for management, which is used for intelligent prediction management, which can effectively improve the real-time online reliability of agents. This method changes the multi-redundant post-processing scheme of traditional system and adopts single-redundant real-time pre-switching. Through a large number of experiments, the feasibility of the system is verified, and the system can be built as a redundant fault-tolerant system. The redundant fault-tolerant system has good generalization and can be implemented in the scene. A redundant module that is about to fail is used, including: the proposed redundant module that continues to decline in health, a redundant module that rapidly decreases in health at a certain time, and a redundant module that is always in a sub-healthy state. After the health assessment, you can enter the early warning model and observe whether the early warning model can issue a health warning, which can verify the effectiveness of the early warning model.

For the increasingly prominent problems of wind turbine maintenance, using edge cloud collaboration technology to construct wind farm equipment operation and maintenance framework is proposed, digital twin is used for fault prediction and diagnosis. Framework consists of data source layer, edge computing node layer, public or private cloud. Data source layer solves acquisition and transmission of wind turbine operation and maintenance data, edge computing node layer is responsible for on-site data cloud computing, storage and data transmission to cloud computing layer, receiving cloud computing results, device driving and control. The cloud computing layer completes the big data calculation and storage from wind farm, except that, based on real-time data records, continuous simulation and optimization, correct failure prediction mode, expert database and its prediction software, and edge node interaction and shared intelligence. The research explains that wind turbine uses digital twin to do fault prediction and diagnosis model, condition assessment, feature analysis and diagnosis, life prediction, combining with the probabilistic digital twin model to make the maintenance plan and decision-making method.

This paper presents a comparative analysis of the time, frequency and time-frequency domain based features of the vibration and current signals for identifying various faults in induction motors (IMs) using support vector machine (SVM). Four mechanical faults (bearing fault, unbalanced rotor, bowed rotor and misaligned rotor), and three electrical faults (broken rotor bars, stator winding fault with two severity levels and phase unbalance with two severity levels) are considered in the present study. The proposed fault diagnosis consists of three steps. In the first step, the vibration in three orthogonal directions and the current in three phases are acquired from the healthy and faulty motors using a machine fault simulator (MFS). In second step, useful statistical features are extracted from the time, frequency and time-frequency domain (continuous wavelet transform (CWT)) of the signal. For the effective fault diagnosis, SVM parameters are optimally selected based on the grid-search method along with 5-fold cross-validation, and the effective fault features are selected based on the wrapper model. Finally, the fault diagnosis of IM is performed using optimal SVM parameters and effective features as input to the SVM. The classification performance of all methodologies developed in three domains is compared for various operating conditions of IMs. The test results showed that the developed methodology could isolate ten IM fault conditions successfully based on features from all three domains at all IM operating conditions; however, time-frequency features give the best results.

This paper presents a hybrid framework for fault diagnosis of complex systems that are modeled by hybrid automata. A bank of residual generators is constructed based on the continuous models of the system. Each residual generator is modeled by a discrete-event system (DES). Next, the DES models of the residual generators and the DES model of the hybrid plant are combined to build an “extended DES” model. A hybrid diagnoser is constructed based on the extended DES model. The hybrid diagnoser effectively combines the readings of discrete sensors and the information supplied by the residual generators (which is based on continuous sensors) to determine the health status of the hybrid plant. The hybrid diagnosis approach is employed to investigate faults in the fuel supply system and the nozzle actuator of a single-spool turbojet engine with an afterburner.

Abstract Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.

Abstract One of the core features of the smart grid deemed essential for smooth grid operation is the detection and diagnosis of system failures. For a utility transmission grid system, these failures could manifest in the form of short circuit faults and open circuit faults. Due to the advent of the digital age, the traditional grid has also undergone a massive transition to digital equipment and modern sensors which are capable of generating large volumes of data. The challenge is to preprocess this data such that it can be utilized for the detection of transients and grid failures. This paper presents the incorporation of artificial intelligence techniques such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) to detect and comprehensively classify the most common fault transients within a reasonable range of accuracy. For gauging the effectiveness of the proposed scheme, a thorough evaluation study is conducted on a modified IEEE-39 bus system. Bus voltage and line current measurements are taken for a range of fault scenarios which result in high-frequency transient signals. These signals are analyzed using continuous wavelet transform (CWT). The measured signals are afterward preprocessed using Discrete Wavelet Transform (DWT) employing Daubechies four (Db4) mother wavelet in order to decompose the high-frequency components of the faulty signals. DWT results in a range of high and low-frequency detail and approximate coefficients, from which a range of statistical features are extracted and used as inputs for training and testing the classification algorithms. The results demonstrate that the trained models can be successfully employed to detect and classify faults on the transmission system with acceptable accuracy.

The safety and reliability margin of offshore floating wind turbines need to be higher than that of onshore wind turbines due to larger environmental loads and higher operational and maintenance costs for offshore wind turbines compared to onshore wind turbines. However rotor cyclic loads coupled with 6 DOFs motions of the substructure, amplifies the fatigue damage in offshore floating wind turbines. In general a lower fatigue design factor is used for offshore wind turbines compared to that of the stationary oil and gas platforms. This is because the consequence of a failure in offshore wind turbines in general is lower than that of the offshore oil and gas platforms. In offshore floating wind turbines a sub-system fault in the electrical system and blade pitch angle controller also induces additional fatigue loading on the wind turbine structure. In this paper effect of selected controller system faults on the fatigue damage of an offshore floating wind turbine is investigated, in a case which fault is not detected by a fault detection system due to a failure in the fault detection system or operator decided to continue operation under fault condition. Two fault cases in the blade pitch angle controller of the NREL 5MW offshore floating wind turbine are modeled and simulated. These faults include: bias error in the blade pitch angle rotary encoder and valve blockage or line disconnection in the blade pitch angle actuator. The short-term fatigue damage due to these faults on the composite blade root, steel low-speed shaft, tower bottom and hub are calculated and compared with the fatigue damage under normal operational conditions considering same environmental conditions for both cases. This comparison shows that how risky is to work under the fault conditions which could be useful for wind turbine operators. The servo-hydro-aeroelastic code HAWC2 is used to simulate the time domain responses of the spar-type offshore floating wind turbine under normal and faulty operational conditions. The rain-flow cycle counting method is used to calculate the load cycles under normal operational and fault conditions. The short term fatigue damage to the composite blade root and steel structures are calculated for 6-hour reference period. The bi-linear Goodman diagram and a linear SN curve are used to estimate the fatigue damage to the composite blade root and the steel structures respectively. Moreover the fatigue damage for different mean wind speeds, sea states and fault amplitudes are calculated to figure out the region of wind speeds operation with the highest risk of damage.

The paper investigates the behavior of buried offshore pipelines overlying active reverse faults using the finite element (FE) tool Abaqus. In the FE analyses, the pipeline is modeled with 3D shell elements and the soil with continuum elements. Equivalent boundary conditions are imposed at both pipeline ends to account for the elastic response in the far-field away from the fault. Nonlinear materials, nonlinear interactions, and nonlinear geometries are adopted.The Mohr-coulomb constitutive model with strain-softening is used to model the soil behavior and true stress-strain properties are incorporated to model the response of the pipeline steel material. The effects of soil properties, pipeline diameter, diameter to thickness ratio, and fault displacement are investigated. The results focused on analyzing the pipeline deformations, the axial strains, and the buckling behavior of the pipeline with increasing vertical bedrock displacement. Critical compressive strains are calculated and compared with the DNV code provisions.

Abstract In recent years, there has been a growing interest in tidal current energy as it is a potential source for green electricity generation and the most predictable form of ocean renewable energy. Due to the harsh marine environment, the Tidal Current Turbine (TCT) system has to be designed to be robust and to work reliably with high availability to minimize the need for intervention. Thus, fault tolerant control strategies are needed to enable the system to continue operating under some fault conditions, this will reduce the power generation cost and also increase the system robustness. This paper introduces some of the different fault conditions that may occur in TCT systems such as sensor faults, especially tidal current sensors. Potential solutions for these faults are then introduced. The paper then presents a standalone TCT generation system model with perturb and observe (P&O) control; this control aims to solve the tidal current speed sensor fault problem, ensuring that the system operates near the maximum power point (MPP) without the tidal current speed sensor. The control system is simulated using MATLAB/Simulink, for a TCT, utilizing a permanent synchronous generator (PMSG) and a boost converter.

Modern aircraft engines are equipped with sophisticated sensing instruments to enable proactive condition monitoring and effective health management capability. Development of intelligent systems that efficiently process sensor and operational data both onboard and off-board, to provide maintenance personnel with timely decision support, is the key to minimize flight service disruption and reduce engine ownership cost. The goal of this research is to develop a practical approach and strategy to leverage various available information sources and modeling techniques to streamline the engine health management process and maximize system accuracy and efficiency. This paper demonstrates a flexible fusion architecture that encapsulates the key elements of the engine monitoring and diagnostic process, i.e., sensor trend analysis module for anomaly detection, feature selection and fault isolation module for root cause identification, a decision module for diagnostic model fusion and action determination, and finally, a feedback module for knowledge validation and continuous learning. At the core of this engine health management system is a diagnostic fusion model designed around a common fault hierarchy which captures both a priori probabilities and interactions among various engine faults isolated by different classification models. The fusion model will resolve conflicting assessments from individual diagnostic models and provide a more accurate and comprehensive engine state estimate.

In deep waters, pipelines are usually installed exposed on the seabed, as burial is generally not required to ensure on-bottom stability. These exposed pipelines are nevertheless susceptible to seismic geohazards like slope instability at scarp crossings, soil liquefaction and fault movements which may result in failure events, although larger diameter pipelines are generally known to have good tolerances to ground deformation phenomena, provided the seismic magnitudes are not too onerous. Regardless of the pipeline size, these seismic geohazard issues are usually addressed during the design stage by routing the pipeline to avoid such hazardous conditions, where possible. However, extreme environmental conditions like hurricanes or tropical cyclones, which are typically experienced in the Gulf of Mexico and Asia-Pacific regions, are also factors which can cause exposed pipelines to be susceptible to large pipeline displacements and damage. Secondary stabilisation in the form of rock dump is sometimes employed to reduce the hydrodynamic loads from high turbidity currents acting on the pipeline. However, rock dumping (or burying the displaced pipeline) on a fault line could again pose a threat to its integrity following a seismic faulting event. The traditional method of assessment of a buried pipeline subjected to seismic faulting is initially carried out using analytical methods. Due to the limitations of these techniques for large deformation soil movement associated with fault displacement, non-linear finite element (FE) methods are widely used to assess the pipeline integrity. The FE analysis typically idealises the pipeline using discrete structural beam-type elements and the pipeline-soil interaction as discrete non-linear springs, based on the concept of subgrade reactions proposed by Winkler. Recent research from offshore pipeline design activities in the arctic environment for ice gouge events have however suggested that the use of the discrete Winkler element model leads to over-conservative results in comparison to the coupled continuum model. The principal reason for the conservatism is related to the poor modeling of realistic surrounding soil behaviour for large deformation events. This paper discusses the application of continuum FE methods to model the fully coupled seabed-buried pipeline interaction events subject to ground movements at active seismic faults. Using the continuum approach, a more realistic mechanical response of the pipeline is established and can be further utilised to confirm that calculated strains are within allowable limits.

Abstract The Lower Cretaceous Thamama Group is one of the proven and productive hydrocarbons bearing intervals within Abu Dhabi area. The geological setting of Thamama Group is complex due to the nature of its structural evolution in a strike-slip regime with high-angle fault and subtle vertical displacement. Additionally, the stratigraphy of Thamama Group consists of multiple stacks of Carbonate reservoirs which are separated by various Anhydrite layers. Some of the carbonate layer cannot be interpreted properly due to data resolution and quality of the seismic reflector. Due to the geological complexities, using the existing 3D Seismic Data to interpret faults and horizons within due time frame for critical decision making can be extremely challenging. Vertical displacement of the fault often cannot be easily recognized from seismic section and auto-tracking horizon interpretation doesn't produce a good result due to lack of reflector continuity. In the specific area of this study, the main geological challenge was to interpret those N45W subtle faults developed at the central region of the seismic volume. To address this challenge, we first applied one of the existing machine-learning models proposed by Jiang and Norlund (2020) to pre-train a multi-channel convolutional neural network model with a set of synthetic seismic volumes (over 200 different cases), that resembles multiple geological scenarios with similar structural characteristics to those observed in the study area. We utilize a new point-based method that leverages a network analysis technique to automatically extract fault surfaces from fault imaging volumes. For horizon interpretation we use a new Assisted Horizon Interpretation workflow we are developing. This approach is good for identifying and tracking larger, more continuous surfaces throughout a seismic volume. This results in many output horizons that fill the entire seismic cube, we refer to this as “Dense Horizon” extraction. This portion of the project uses a deterministic approach for identifying & tracking patches throughout the entire seismic volume & then uses several automated and manual tools to join those patches to generate complete surfaces. The results achieved with this study helped to improve the quality of faults and horizon generation in complex geological area with improved efficiency and accuracy.

The Western Canada Sedimentary Basin (WCSB) is a mature oil and gas basin with an extraordinary endowment of publicly accessible data. It contains structural elements of varying age, expressed as folding, faulting, and fracturing, which provide a record of tectonic activity during basin evolution. Knowledge of the structural architecture of the basin is crucial to understand its tectonic evolution; it also provides essential input for a range of geoscientific studies, including hydrogeology, geomechanics, and seismic risk analysis. This study focuses on an area defined by the subsurface extent of the Triassic Montney Formation, a region of the WCSB straddling the border between Alberta and British Columbia, and covering an area of approximately 130,000 km2. In terms of regional structural elements, this area is roughly bisected by the east-west trending Dawson Creek Graben Complex (DCGC), which initially formed in the Late Carboniferous, and is bordered to the southwest by the Late Cretaceous - Paleocene Rocky Mountain thrust and fold belt (TFB). The structural geology of this region has been extensively studied, but structural elements compiled from previous studies exhibit inconsistencies arising from distinct subregions of investigation in previous studies, differences in the interpreted locations of faults, and inconsistent terminology. Moreover, in cases where faults are mapped based on unpublished proprietary data, many existing interpretations suffer from a lack of reproducibility. In this study, publicly accessible data - formation tops derived from well logs, LITHOPROBE seismic profiles and regional potential-field grids, are used to delineate regional structural elements. Where seismic profiles cross key structural features, these features are generally expressed as multi-stranded or en echelon faults and structurally-linked folds, rather than discrete faults. Furthermore, even in areas of relatively tight well control, individual fault structures cannot be discerned in a robust manner, because the spatial sampling is insufficient to resolve fault strands. We have therefore adopted a structural-corridor approach, where structural corridors are defined as laterally continuous trends, identified using geological trend surface analysis supported by geophysical data, that contain co-genetic faults and folds. Such structural trends have been documented in laboratory models of basement-involved faults and some types of structural corridors have been described as flower structures. The distinction between discrete faults and structural corridors is particularly important for induced seismicity risk analysis, as the hazard posed by a single large structure differs from the hazard presented by a corridor of smaller pre-existing faults. We have implemented a workflow that uses trend surface analysis based on formation tops, with extensive quality control, combined with validation using available geophysical data. Seven formations are considered, from the Late Cretaceous Basal Fish Scale Zone (BFSZ) to the Wabamun Group. This approach helped to resolve the problem of limited spatial extent of available seismic data and provided a broader spatial coverage, enabling the investigation of structural trends throughout the entirety of the Montney play. In total, we identified 34 major structural corridors and number of smaller-scale structures, for which a GIS shapefile is included as a digital supplement to facilitate use of these features in other studies. Our study also outlines two buried regional foreland lobes of the Rocky Mountain TFB, both north and south of the DCGC.

The role of crustal lineaments in the development of the western margin of Laurentia, Selwyn basin and associated sediment-hosted Zn-Pb deposits (clastic-dominated, Mississippi-Valley-type) in Yukon and NWT, are reassessed through a new 3-D inversion strategy applied to new compilations of gravity and magnetic data. Regionally continuous, broadly NE-trending crustal lineaments including the Liard line, Fort Norman structure, and Leith Ridge fault, were interpreted as having had long-standing influence on craton, margin, and sedimentary basin development. However, multiple tectonic overprints including terrane accretion, thrust faulting, and plutonism obscure the region's history. The Liard line, related to a transfer fault that bounds the Macdonald Platform promontory, is refined from the integration of the new geophysical models with published geological data. The geophysical models support the continuity of the Fort Norman structure below the Selwyn basin, but the presence of Leith Ridge fault is not supported in this area. The ENE-trending Mackenzie River lineament, traced from the Misty Creek Embayment to Great Bear Lake, is interpreted to mark the southern edge of a cratonic promontory. The North American craton is bounded by a NW-trending lineament interpreted as a crustal manifestation of lithospheric thinning of the Laurentian margin, as echoed by a change in the depth of the lithosphere-asthenosphere boundary. The structure is straddled by Mississippi Valley-type Zn-Pb occurrences, following their palinspastic restoration, and also defines the eastern limit of mid-Late Cretaceous granitic intrusions. Another NW-trending lineament, interpreted to be associated with a shallowing of lower crustal rocks, is coincident with clastic-dominated Zn-Pb occurrences.

Introduction u cours des deux derniers millénaires, il y a eu plusieurs façons de conserver, transmettre et même créer la connaissance ; la tradition orale, l’écrit manuscrit, l’écrit imprimé et l’écrit numérisé. La tradition orale et le manuscrit ont dominé pendant plus de 1400 ans, et ce, jusqu’à l’apparition du livre imprimé en 1451, résultant de l’invention mécanique de Gutenberg. Il faudra attendre un peu plus de 550 ans, avant que l’invention du support électronique déloge à son tour le livre imprimé, prenant une ampleur sans précédent grâce à la révolution numérique contemporaine, résultat du maillage des technologies de l’informatique, de la robotique et de la science des données. Les premières universités qui sont nées en Occident, au Moyen Âge, ont développé cette tradition orale de la connaissance tout en multipliant l’usage du manuscrit créant ainsi de véritables communautés de maîtres et d’étudiants ; la venue de l’imprimerie permettra la multiplication des universités où l’oral et l’écrit continueront de jouer un rôle déterminant dans la création et la transmission des connaissances même si le « support » a évolué du manuscrit à l’imprimé puis vers le numérique. Au cours de toutes ces années, le modèle de l’université s’est raffiné et perfectionné sur une trajectoire somme toute assez linéaire en élargissant son rôle dans l’éducation à celui-ci de la recherche et de l’innovation, en multipliant les disciplines offertes et les clientèles desservies. L’université de chaque ville universitaire est devenue une institution florissante et indispensable à son rayonnement international, à un point tel que l’on mesure souvent sa contribution par la taille de sa clientèle étudiante, l’empreinte de ses campus, la grandeur de ses bibliothèques spécialisées ; c’est toutefois la renommée de ses chercheurs qui consacre la réputation de chaque université au cours de cette longue trajectoire pendant laquelle a pu s’établir la liberté universitaire. « Les libertés universitaires empruntèrent beaucoup aux libertés ecclésiastiques » : Étudiants et maîtres, qu'ils furent, ou non, hommes d'Église, furent assimilés à des clercs relevant de la seule justice ecclésiastique, réputée plus équitable. Mais ils échappèrent aussi largement à la justice ecclésiastique locale, n'étant justiciables que devant leur propre institution les professeurs et le recteur, chef élu de l’université - ou devant le pape ou ses délégués. Les libertés académiques marquèrent donc l’émergence d'un droit propre, qui ménageait aux maîtres et aux étudiants une place à part dans la société. Ce droit était le même, à travers l'Occident, pour tous ceux qui appartenaient à ces institutions supranationales que furent, par essence, les premières universités. À la fin du Moyen Âge, l'affirmation des États nationaux obligea les libertés académiques à s'inscrire dans ce nouveau cadre politique, comme de simples pratiques dérogatoires au droit commun et toujours sujettes à révision. Vestige vénérable de l’antique indépendance et privilège octroyé par le prince, elles eurent donc désormais un statut ambigu » . La révolution numérique viendra fragiliser ce statut. En effet, la révolution numérique vient bouleverser cette longue trajectoire linéaire de l’université en lui enlevant son quasi monopole dans la conservation et le partage du savoir parce qu’elle rend plus facile et somme toute, moins coûteux l’accès à l’information, au savoir et aux données. Le numérique est révolutionnaire comme l’était l’imprimé et son influence sur l’université, sera tout aussi considérable, car cette révolution impacte radicalement tous les secteurs de l’économie en accélérant la robotisation et la numérisation des processus de création, de fabrication et de distribution des biens et des services. Ces innovations utilisent la radio-identification (RFID) qui permet de mémoriser et de récupérer à distance des données sur les objets et l’Internet des objets qui permet aux objets d’être reliés automatiquement à des réseaux de communications .Ces innovations s’entrecroisent aux technologies de la réalité virtuelle, à celles des algorithmiques intelligentes et de l’intelligence artificielle et viennent littéralement inonder de données les institutions et les organisations qui doivent alors les analyser, les gérer et les protéger. Le monde numérique est né et avec lui, a surgi toute une série de compétences radicalement nouvelles que les étudiants, les enseignants et les chercheurs de nos universités doivent rapidement maîtriser pour évoluer dans ce Nouveau Monde, y travailler et contribuer à la rendre plus humain et plus équitable. En effet, tous les secteurs de l’activité commerciale, économique, culturelle ou sociale exigent déjà clairement des connaissances et des compétences numériques et technologiques de tous les participants au marché du travail. Dans cette nouvelle logique industrielle du monde numérique, les gagnants sont déjà bien identifiés. Ce sont les fameux GAFAM (Google, Apple, Facebook, Amazon et Microsoft) suivis de près par les NATU (Netflix, Airbnb, Tesla et Uber) et par les géants chinois du numérique, les BATX (Baidu, Alibaba, Tenant et Xiaomi). Ces géants sont alimentés par les recherches, les innovations et les applications mobiles (APPs) créées par les partenaires de leurs écosystèmes regroupant, sur différents campus d’entreprises, plusieurs des cerveaux qui sont au cœur de cette révolution numérique. L’université voit donc remise en question sa capacité traditionnelle d’attirer, de retenir et de promouvoir les artisans du monde de demain. Son aptitude à former des esprits critiques et à contribuer à la transmission des valeurs universelles est également ébranlée par ce tsunami de changements. Il faut cependant reconnaître que les facultés de médecine, d’ingénierie et de sciences naturelles aux États-Unis qui ont développé des contacts étroits, abondants et suivis avec les hôpitaux, les grandes entreprises et l’administration publique et cela dès la fin du 19e siècle ont été plus en mesure que bien d’autres, de recruter et retenir les gens de talent. Elle ont énormément contribué à faire avancer les connaissances scientifiques et la scolarisation en sciences appliquées ..La concentration inouïe des Prix Nobel scientifiques aux États-Unis est à cet égard très convaincante . La révolution numérique contemporaine survient également au moment même où de grands bouleversements frappent la planète : l’urgence climatique, le vieillissement des populations, la « déglobalisation », les déplacements des populations, les guerres, les pandémies, la crise des inégalités, de l’éthique et des démocraties. Ces bouleversements interpellent les universitaires et c’est pourquoi leur communauté doit adopter une raison d’être et ainsi renouveler leur mission afin des mieux répondre à ces enjeux de la civilisation. Cette communauté doit non seulement se doter d’une vision et des modes de fonctionnement adaptés aux nouvelles réalités liées aux technologies numériques, mais elle doit aussi tenir compte de ces grands bouleversements. Tout ceci l’oblige à s’intégrer à des écosystèmes où les connaissances sont partagées et où de nouvelles compétences doivent être rapidement acquises. Le but de ce texte est de mieux cerner l’ampleur du défi que pose le monde numérique au milieu universitaire et de proposer quelques idées pouvant alimenter la réflexion des universitaires dans cette démarche d’adaptation au monde numérique. Or, ma conviction la plus profonde c’est que la révolution numérique aura des impacts sur nos sociétés et notre civilisation aussi grands que ceux provoqués par la découverte de l’imprimerie et son industrialisation au 15e siècle. C’est pourquoi la première section de ce document est consacrée à un rappel historique de la révolution de l’imprimerie par Gutenberg alors que la deuxième section illustrera comment les caractéristiques de la révolution numérique viennent soutenir cette conviction si profonde. Une troisième section fournira plus de détails sur le défi d’adaptation que le monde numérique pose aux universités alors que la quatrième section évoquera les contours du changement de paradigme que cette adaptation va imposer. La cinquième section servira à illustrer un scénario de rêves qui permettra de mieux illustrer l’ampleur de la gestion du changement qui guette les universitaires. La conclusion permettra de revenir sur quelques concepts et principes clefs pour guider la démarche vers l’action. L’université ne peut plus « être en haut et seule », elle doit être « au centre et avec » des écosystèmes de partenariats multiples, dans un modèle hybride physique/virtuel. C’est ainsi qu’elle pourra conserver son leadership historique de vigie du savoir et des connaissances d’un monde complexe, continuer d’établir l’authenticité des faits et imposer la nécessaire rigueur de la science et de l’objectivité.