Fault propagation, detection and analysis in process systems

Process systems are often complicated and liable to experience faults and their effects. Faults can adversely affect the safety of the plant, its environmental impact and economic operation. As such, fault diagnosis in process systems is an active area of research and development in both academia and industry. The work reported in this thesis contributes to fault diagnosis by exploring the modelling and analysis of fault propagation and detection in process systems. This is done by posing and answering three research questions. What are the necessary ingredients of a fault diagnosis model? What information should a fault diagnosis model yield? Finally, what types of model are appropriate to fault diagnosis? To answer these questions , the assumption of the research is that the behaviour of a process system arises from the causal structure of the process system. On this basis, the research presented in this thesis develops a two-level approach to fault diagnosis based on detailed process information, and modelling and analysis techniques for representing causality. In the first instance, a qualitative approach is developed called a level 1 fusion. The level 1 fusion models the detailed causality of the system using digraphs. The level 1 fusion is a causal map of the process. Such causal maps can be searched to discover and analyse fault propagation paths through the process. By directly building on the level 1 fusion, a quantitative level 2 fusion is developed which uses a type of digraph called a Bayesian network. By associating process variables with fault variables, and using conditional probability theory, it is shown how measured effects can be used to calculate and rank the probability of candidate causes. The novel contributions are the development of a systematic approach to fault diagnosis based on modelling the chemistry, physics, and architecture of the process. It is also shown how the control and instrumentation system constrains the casualty of the process. By demonstrating how digraph models can be reversed, it is shown how both cause-to-effect and effect-to-cause analysis can be carried out. In answering the three research questions, this research shows that it is feasible to gain detailed insights into fault propagation by qualitatively modelling the physical causality of the process system. It is also shown that a qualitative fault diagnosis model can be used as the basis for a quantitative fault diagnosis modelOpen Acces

Cybersecurity Games: Mathematical Approaches for Cyber Attack and Defense Modeling

Cyber-attacks targeting individuals and enterprises have become a predominant part of the computer/information age. Such attacks are becoming more sophisticated and prevalent on a day-to-day basis. The exponential growth of cyber plays and cyber players necessitate the inauguration of new methods and research for better understanding the cyber kill chain, particularly with the rise of advanced and novel malware and the extraordinary growth in the population of Internet residents, especially connected Internet of Things (IoT) devices. Mathematical modeling could be used to represent real-world cyber-attack situations. Such models play a beneficial role when it comes to the secure design and evaluation of systems/infrastructures by providing a better understanding of the threat itself and the attacker\u27s conduct during the lifetime of a cyber attack. Therefore, the main goal of this dissertation is to construct a proper theoretical framework to be able to model and thus evaluate the defensive strategies/technologies\u27 effectiveness from a security standpoint. To this end, we first present a Markov-based general framework to model the interactions between the two famous players of (network) security games, i.e., a system defender and an attacker taking actions to reach its attack objective(s) in the game. We mainly focus on the most significant and tangible aspects of sophisticated cyber attacks: (1) the amount of time it takes for the adversary to accomplish its mission and (2) the success probabilities of fulfilling the attack objective(s) by translating attacker-defender interactions into well-defined games and providing rigorous cryptographic security guarantees for a system given both players\u27 tactics and strategies. We study various attack-defense scenarios, including Moving Target Defense (MTD) strategies, multi-stage attacks, and Advanced Persistent Threats (APT). We provide general theorems about how the probability of a successful adversary defeating a defender’s strategy is related to the amount of time (or any measure of cost) spent by the adversary in such scenarios. We also introduce the notion of learning in cybersecurity games and describe a general game of consequences meaning that each player\u27s chances of making a progressive move in the game depend on its previous actions. Finally, we walk through a malware propagation and botnet construction game in which we investigate the importance of defense systems\u27 learning rates to fight against the self-propagating class of malware such as worms and bots. We introduce a new propagation modeling and containment strategy called the learning-based model and study the containment criterion for the propagation of the malware based on theoretical and simulation analysis

Automated Problem-Specific Nuclide-Transition Selection for Reduced Order Modeling

A method for automated library reduction for the nuclide generation code Origen was developed for increased computational efficiency. The requirement for a reduced burnup chain micro-depletion code has been identified in many code frameworks in fuel cycles, neutronics, and nonproliferation where the increased accuracy of a micro-depletion code with hundreds, if not thousands, of nuclides is needed. These large library inventories result in relatively large memory requirements and runtimes that become burdensome within codes that require many depletion zones and/or depletion substeps per time step. However, the tracked nuclides do not equally contribute to the problem, and therefore a subset of the total nuclides can be removed from the system with little loss of accuracy. To do this in a generalized manner the application for the libraries need to be considered. To this end a number of metrics are available to measure library accuracy for a given problem, such as depletion inventory, total activity, gamma dose, decay heat, and individual nuclide inventory. Using these metrics, and their sensitivities to nuclide inventories, it is possible to reduce Origen\u27s full inventory of thousands of nuclides to several hundred nuclides while only affecting the metric of interest by less than 1 pcm (per cent mille or 10^(-5)). The method for this problem specific reduction relies on maintaining the physical meaning of the transition system to the highest degree reasonable. This means maintaining the integrity of the subsystem in relation to its behavior within the full system. Though a number of methods to achieve this have been studied, with varying degrees of success, the most successful method is one that takes a layered approach. This method makes an estimate of the final system through the cutting planes method then makes successive corrections to that estimate in each layer to account for the physical behavior of truncating the transition system that is not present in standard system problems

From multiscale modeling to metamodeling of geomechanics problems

In numerical simulations of geomechanics problems, a grand challenge consists of overcoming the difficulties in making accurate and robust predictions by revealing the true mechanisms in particle interactions, fluid flow inside pore spaces, and hydromechanical coupling effect between the solid and fluid constituents, from microscale to mesoscale, and to macroscale. While simulation tools incorporating subscale physics can provide detailed insights and accurate material properties to macroscale simulations via computational homogenizations, these numerical simulations are often too computational demanding to be directly used across multiple scales. Recent breakthroughs of Artificial Intelligence (AI) via machine learning have great potential to overcome these barriers, as evidenced by their great success in many applications such as image recognition, natural language processing, and strategy exploration in games. The AI can achieve super-human performance level in a large number of applications, and accomplish tasks that were thought to be not feasible due to the limitations of human and previous computer algorithms. Yet, machine learning approaches can also suffer from overfitting, lack of interpretability, and lack of reliability. Thus the application of machine learning into generation of accurate and reliable surrogate constitutive models for geomaterials with multiscale and multiphysics is not trivial. For this purpose, we propose to establish an integrated modeling process for automatic designing, training, validating, and falsifying of constitutive models, or "metamodeling". This dissertation focuses on our efforts in laying down step-by-step the necessary theoretical and technical foundations for the multiscale metamodeling framework. The first step is to develop multiscale hydromechanical homogenization frameworks for both bulk granular materials and granular interfaces, with their behaviors homogenized from subscale microstructural simulations. For efficient simulations of field-scale geomechanics problems across more than two scales, we develop a hybrid data-driven method designed to capture the multiscale hydro-mechanical coupling effect of porous media with pores of various different sizes. By using sub-scale simulations to generate database to train material models, an offline homogenization procedure is used to replace the up-scaling procedure to generate path-dependent cohesive laws for localized physical discontinuities at both grain and specimen scales. To enable AI in taking over the trial-and-error tasks in the constitutive modeling process, we introduce a novel “metamodeling” framework that employs both graph theory and deep reinforcement learning (DRL) to generate accurate, physics compatible and interpretable surrogate machine learning models. The process of writing constitutive models is simplified as a sequence of forming graph edges with the goal of maximizing the model score (a function of accuracy, robustness and forward prediction quality). By using neural networks to estimate policies and state values, the computer agent is able to efficiently self-improve the constitutive models generated through self-playing. To overcome the obstacle of limited information in geomechanics, we improve the efficiency in utilization of experimental data by a multi-agent cooperative metamodeling framework to provide guidance on database generation and constitutive modeling at the same time. The modeler agent in the framework focuses on evaluating all modeling options (from domain experts’ knowledge or machine learning) in a directed multigraph of elasto-plasticity theory, and finding the optimal path that links the source of the directed graph (e.g., strain history) to the target (e.g., stress). Meanwhile, the data agent focuses on collecting data from real or virtual experiments, interacts with the modeler agent sequentially and generates the database for model calibration to optimize the prediction accuracy. Finally, we design a non-cooperative meta-modeling framework that focuses on automatically developing strategies that simultaneously generate experimental data to calibrate model parameters and explore weakness of a known constitutive model until the strengths and weaknesses of the constitutive law on the application range can be identified through competition. These tasks are enabled by a zero-sum reward system of the metamodeling game and robust adversarial reinforcement learning techniques

Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation

This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference

Dynamic Trees: A Hierarchical Probabilistic Approach to Image Modelling

Institute for Adaptive and Neural ComputationThis work introduces a new class of image model which we call dynamic trees or DTs. A dynamic tree model specifies a prior over structures of trees, each of which is a forest of one or more tree-structured belief networks (TSBN). In the literature standard tree-structured belief network models were found to produce “blocky” segmentations when naturally occurring boundaries within an image did not coincide with those of the subtrees in the rigid fixed structure of the network. Dynamic trees have a flexible architecture which allows the structure to vary to accommodate configurations where the subtree and image boundaries align, and experimentation with the model showed significant improvements. They are also hierarchical in nature allowing a multi-scale representation and are constructed within a well founded Bayesian framework. For large models the number of tree configurations quickly becomes intractable to enumerate over, presenting a problem for exact inference. Techniques such as Gibbs sampling over trees are considered and search using simulated annealing finds high posterior probability trees on synthetic 2-d images generated from the model. However simulated annealing and sampling techniques are rather slow. Variational methods are applied to the model in an attempt to approximate the posterior by a simpler tractable distribution, and the simplest of these techniques, mean field, found comparable solutions to simulated annealing in the order of 100 times faster. This increase in speed goes a long way towards making real-time inference in the dynamic tree viable. Variational methods have the further advantage that by attempting to model the full posterior distribution it is possible to gain an indication as to the quality of the solutions found. An EM-style update based upon mean field inference is derived and the learned conditional probability tables (describing state transitions between a node and its parent) are compared with exact EM on small tractable fixed architecture models. The mean field approximation by virtue of its form is biased towards fully factorised solutions which tends to create degenerate CPTs, but despite this mean field learning still produces solutions whose log likelihood rivals exact EM. Development of algorithms for learning the probabilities of the prior over tree structures completes the dynamic tree picture. After discussion of the relative merits of certain representations for the disconnection probabilities and initial investigation on small model structures the full dynamic tree model is applied to a database of images of outdoor scenes where all of its parameters are learned. DTs are seen to offer significant improvement in performance over the fixed architecture TSBN and in a coding comparison the DT achieves 0 294 bits per pixel (bpp) compression compared to 0 378 bpp for lossless JPEG on images of 7 colours

Error handling in multimodal voice-enabled interfaces of tour-guide robots using graphical models

Mobile service robots are going to play an increasing role in the society of humans. Voice-enabled interaction with service robots becomes very important, if such robots are to be deployed in real-world environments and accepted by the vast majority of potential human users. The research presented in this thesis addresses the problem of speech recognition integration in an interactive voice-enabled interface of a service robot, in particular a tour-guide robot. The task of a tour-guide robot is to engage visitors to mass exhibitions (users) in dialogue providing the services it is designed for (e.g. exhibit presentations) within a limited time. In managing tour-guide dialogues, extracting the user goal (intention) for requesting a particular service at each dialogue state is the key issue. In mass exhibition conditions speech recognition errors are inevitable because of noisy speech and uncooperative users of robots with no prior experience in robotics. They can jeopardize the user goal identification. Wrongly identified user goals can lead to communication failures. Therefore, to reduce the risk of such failures, methods for detecting and compensating for communication failures in human-robot dialogue are needed. During the short-term interaction with visitors, the interpretation of the user goal at each dialogue state can be improved by combining speech recognition in the speech modality with information from other available robot modalities. The methods presented in this thesis exploit probabilistic models for fusing information from speech and auxiliary modalities of the robot for user goal identification and communication failure detection. To compensate for the detected communication failures we investigate multimodal methods for recovery from communication failures. To model the process of modality fusion, taking into account the uncertainties in the information extracted from each input modality during human-robot interaction, we use the probabilistic framework of Bayesian networks. Bayesian networks are graphical models that represent a joint probability function over a set of random variables. They are used to model the dependencies among variables associated with the user goals, modality related events (e.g. the event of user presence that is inferred from the laser scanner modality of the robot), and observed modality features providing evidence in favor of these modality events. Bayesian networks are used to calculate posterior probabilities over the possible user goals at each dialogue state. These probabilities serve as a base in deciding if the user goal is valid, i.e. if it can be mapped into a tour-guide service (e.g. exhibit presentation) or is undefined – signaling a possible communication failure. The Bayesian network can be also used to elicit probabilities over the modality events revealing information about the possible cause for a communication failure. Introducing new user goal aspects (e.g. new modality events and related features) that provide auxiliary information for detecting communication failures makes the design process cumbersome, calling for a systematic approach in the Bayesian network modelling. Generally, introducing new variables for user goal identification in the Bayesian networks can lead to complex and computationally expensive models. In order to make the design process more systematic and modular, we adapt principles from the theory of grounding in human communication. When people communicate, they resolve understanding problems in a collaborative joint effort of providing evidence of common shared knowledge (grounding). We use Bayesian network topologies, tailored to limited computational resources, to model a state-based grounding model fusing information from three different input modalities (laser, video and speech) to infer possible grounding states. These grounding states are associated with modality events showing if the user is present in range for communication, if the user is attending to the interaction, whether the speech modality is reliable, and if the user goal is valid. The state-based grounding model is used to compute probabilities that intermediary grounding states have been reached. This serves as a base for detecting if the the user has reached the final grounding state, or wether a repair dialogue sequence is needed. In the case of a repair dialogue sequence, the tour-guide robot can exploit the multiple available modalities along with speech. For example, if the user has failed to reach the grounding state related to her/his presence in range for communication, the robot can use its move modality to search and attract the attention of the visitors. In the case when speech recognition is detected to be unreliable, the robot can offer the alternative use of the buttons modality in the repair sequence. Given the probability of each grounding state, and the dialogue sequence that can be executed in the next dialogue state, a tour-guide robot has different preferences on the possible dialogue continuation. If the possible dialogue sequences at each dialogue state are defined as actions, the introduced principle of maximum expected utility (MEU) provides an explicit way of action selection, based on the action utility, given the evidence about the user goal at each dialogue state. Decision networks, constructed as graphical models based on Bayesian networks are proposed to perform MEU-based decisions, incorporating the utility of the actions to be chosen at each dialogue state by the tour-guide robot. These action utilities are defined taking into account the tour-guide task requirements. The proposed graphical models for user goal identification and dialogue error handling in human-robot dialogue are evaluated in experiments with multimodal data. These data were collected during the operation of the tour-guide robot RoboX at the Autonomous System Lab of EPFL and at the Swiss National Exhibition in 2002 (Expo.02). The evaluation experiments use component and system level metrics for technical (objective) and user-based (subjective) evaluation. On the component level, the technical evaluation is done by calculating accuracies, as objective measures of the performance of the grounding model, and the resulting performance of the user goal identification in dialogue. The benefit of the proposed error handling framework is demonstrated comparing the accuracy of a baseline interactive system, employing only speech recognition for user goal identification, and a system equipped with multimodal grounding models for error handling