Fault diagnosis-based SDG transfer for zero-sample fault symptom

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes

A probabilistic risk-based decision framework for structural health monitoring

Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems. Probabilistic risk assessment (PRA) is an established methodology that allows engineers to make risk-informed decisions regarding the design and operation of safety-critical and high-value assets in industries such as nuclear and aerospace. The current paper aims to formulate a risk-based decision framework for structural health monitoring that combines elements of PRA with the existing SHM paradigm. As an apt tool for reasoning and decision-making under uncertainty, probabilistic graphical models serve as the foundation of the framework. The framework involves modelling failure modes of structures as Bayesian network representations of fault trees and then assigning costs or utilities to the failure events. The fault trees allow for information to pass from probabilistic classifiers to influence diagram representations of decision processes whilst also providing nodes within the graphical model that may be queried to obtain marginal probability distributions over local damage states within a structure. Optimal courses of action for structures are selected by determining the strategies that maximise expected utility. The risk-based framework is demonstrated on a realistic truss-like structure and supported by experimental data. Finally, a discussion of the risk-based approach is made and further challenges pertaining to decision-making processes in the context of SHM are identified

Review of graph-based hazardous event detection methods for autonomous driving systems

Automated and autonomous vehicles are often required to operate in complex road environments with potential hazards that may lead to hazardous events causing injury or even death. Therefore, a reliable autonomous hazardous event detection system is a key enabler for highly autonomous vehicles (e.g., Level 4 and 5 autonomous vehicles) to operate without human supervision for significant periods of time. One promising solution to the problem is the use of graph-based methods that are powerful tools for relational reasoning. Using graphs to organise heterogeneous knowledge about the operational environment, link scene entities (e.g., road users, static objects, traffic rules) and describe how they affect each other. Due to a growing interest and opportunity presented by graph-based methods for autonomous hazardous event detection, this paper provides a comprehensive review of the state-of-the-art graph-based methods that we categorise as rule-based, probabilistic, and machine learning-driven. Additionally, we present an in-depth overview of the available datasets to facilitate hazardous event training and evaluation metrics to assess model performance. In doing so, we aim to provide a thorough overview and insight into the key research opportunities and open challenges

Dynamic risk assessment of process operations

Process engineering systems have become increasingly complex and more vulnerable to potential accidents. The risks posed by these systems are alarming and worrisome. The operation of these complex process engineering systems requires a high level of understanding both from the operational as well as the safety perspective. This study focuses on dynamic risk assessment and management of complex process engineering systems’ operations. To reduce risk posed by process systems, there is a need to develop process accident models capable of capturing system dynamics in real-time. This thesis presents a set of predictive process accident models developed over four years. It is prepared in manuscript style and consists of nine chapters, five of which are published in peer reviewed journals. A dynamic operational risk management tool for process systems is developed, considering evolving process conditions. The obvious advantage of the developed methodologies is that it dynamically captures the real time changes occurring in the process operations. The real time risk profile provided by the methodologies developed serve as performance indicator for operational decision making. The research has made contributions on the following topics: (a) process accident model considering dependency among contributory factors, (b) dynamic safety analysis of process systems using a nonlinear and non-sequential accident model, (c) dynamic failure analysis of process systems using principal component analysis and a Bayesian network, (d) dynamic failure analysis of process systems using a neural network and (e) an integrated approach for dynamic economic risk assessment of process systems

Topological changes in data-driven dynamic security assessment for power system control

The integration of renewable energy sources into the power system requires new operating paradigms. The higher uncertainty in generation and demand makes the operations much more dynamic than in the past. Novel operating approaches that consider these new dynamics are needed to operate the system close to its physical limits and fully utilise the existing grid assets. Otherwise, expensive investments in redundant grid infrastructure become necessary. This thesis reviews the key role of digitalisation in the shift toward a decarbonised and decentralised power system. Algorithms based on advanced data analytic techniques and machine learning are investigated to operate the system assets at the full capacity while continuously assessing and controlling security. The impact of topological changes on the performance of these data-driven approaches is studied and algorithms to mitigate this impact are proposed. The relevance of this study resides in the increasingly higher frequency of topological changes in modern power systems and in the need to improve the reliability of digitalised approaches against such changes to reduce the risks of relying on them. A novel physics-informed approach to select the most relevant variables (or features) to the dynamic security of the system is first proposed and then used in two different three-stages workflows. In the first workflow, the proposed feature selection approach allows to train classification models from machine learning (or classifiers) close to real-time operation improving their accuracy and robustness against uncertainty. In the second workflow, the selected features are used to define a new metric to detect high-impact topological changes and train new classifiers in response to such changes. Subsequently, the potential of corrective control for a dynamically secure operation is investigated. By using a neural network to learn the safety certificates for the post-fault system, the corrective control is combined with preventive control strategies to maintain the system security and at the same time reduce operational costs and carbon emissions. Finally, exemplary changes in assumptions for data-driven dynamic security assessment when moving from high inertia to low inertia systems are questioned, confirming that using machine learning based models will make significantly more sense in future systems. Future research directions in terms of data generation and model reliability of advanced digitalised approaches for dynamic security assessment and control are finally indicated.Open Acces

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Identification of Causal Paths and Prediction of Runway Incursion Risk using Bayesian Belief Networks

In the U.S. and worldwide, runway incursions are widely acknowledged as a critical concern for aviation safety. However, despite widespread attempts to reduce the frequency of runway incursions, the rate at which these events occur in the U.S. has steadily risen over the past several years. Attempts to analyze runway incursion causation have been made, but these methods are often limited to investigations of discrete events and do not address the dynamic interactions that lead to breaches of runway safety. While the generally static nature of runway incursion research is understandable given that data are often sparsely available, the unmitigated rate at which runway incursions take place indicates a need for more comprehensive risk models that extend currently available research. This dissertation summarizes the existing literature, emphasizing the need for cross-domain methods of causation analysis applied to runway incursions in the U.S. and reviewing probabilistic methodologies for reasoning under uncertainty. A holistic modeling technique using Bayesian Belief Networks as a means of interpreting causation even in the presence of sparse data is outlined in three phases: causal factor identification, model development, and expert elicitation, with intended application at the systems or regulatory agency level. Further, the importance of investigating runway incursions probabilistically and incorporating information from human factors, technological, and organizational perspectives is supported. A method for structuring a Bayesian network using quantitative and qualitative event analysis in conjunction with structured expert probability estimation is outlined and results are presented for propagation of evidence through the model as well as for causal analysis. In this research, advances in the aggregation of runway incursion data are outlined, and a means of combining quantitative and qualitative information is developed. Building upon these data, a method for developing and validating a Bayesian network while maintaining operational transferability is also presented. Further, the body of knowledge is extended with respect to structured expert judgment, as operationalization is combined with elicitation of expert data to create a technique for gathering expert assessments of probability in a computationally compact manner while preserving mathematical accuracy in rank correlation and dependence structure. The model developed in this study is shown to produce accurate results within the U.S. aviation system, and to provide a dynamic, inferential platform for future evaluation of runway incursion causation. These results in part confirm what is known about runway incursion causation, but more importantly they shed more light on multifaceted causal interactions and do so in a modeling space that allows for causal inference and evaluation of changes to the system in a dynamic setting. Suggestions for future research are also discussed, most prominent of which is that this model allows for robust and flexible assessment of mitigation strategies within a holistic model of runway safety

Врахування помилок перемикального пристрою для системи із ковзним резервуванням на основі динамічного дерева відмов

The object of research is a non-renewable system with a single sliding reservation. Such system consists of two main subsystems, one redundancy and two switching devices. While both main subsystems are operable, the spare subsystem is in an unloaded state. The redundancy system is designed to replace any major subsystem after its failure. Switching devices commute the main subsystems with a redundancy one. During the audit, it was revealed that the switching devices allow errors. In particular, a mistake of the first type, that is, they switch in advance, and a second type of error, that is, they pass the switching moment. This reduces the reliability of the system and leads to underutilization of the inherent resource.An approach is proposed that quantitatively takes into account the influence of errors of the first and second type on the probability of failure-free operation of the system under study during its design. The approach consists of two stages. At the first stage, the reliability of the system is mathematically described by the dynamic failure tree. At the second stage, based on the failure tree, a Markov model is formed. Applying it, it is possible to calculate the probabilistic characteristics of the system.The result is a mathematical relationship between the probability of trouble-free operation of the system and the parameters of the components of the system. In particular, the operating time to failure of the main and redundancy subsystems, as well as the parameters of switching devices that corresponds to errors of the first and second type. The form of presentation of the obtained results for the end user is a software product that automatically generates a family of graphs for reliability evaluation. Ignoring the errors of switching devices in the design of systems reduces their actual reliability, leads to underutilization of the reserve component resources, and also increases the probability of emergency situations.Using a more accurate mathematical model makes it possible to monitor the errors of switching devices during the design of the system. The simulation results will be useful for selecting the parameters of the switching devices.Объектом исследования является невозобновляемая система с однократным скользящим резервированием. Такая система состоит из двух основных подсистем, одной резервной и двух переключающих устройств. Пока обе основные подсистемы работоспособны, резервная подсистема находится в ненагруженном состоянии. Резервная система предназначена для замены какой-либо основной подсистемы после ее отказа. Переключающие устройства коммутируют основные подсистемы с резервной. В ходе аудита выявлено, что переключающие устройства допускают ошибки. В частности ошибку первого рода, то есть переключаются преждевременно, и ошибку второго рода, то есть пропускают момент переключения. Это снижает надежность системы и ведет к недоиспользованию заложенного в нее ресурса.Предложен подход, который количественно учитывает влияние ошибок первого и второго рода на вероятность безотказной работы исследуемой системы во время ее проектирования. Подход состоит из двух этапов. На первом этапе надежность системы математически описывается динамическим деревом отказов. На втором этапе на основе дерева отказов формируется марковская модель. Применяя ее, можно вычислить вероятностные характеристики системы.Полученным результатом является математическая зависимость между вероятностью безотказной работы системы и параметрами элементов системы. В частности, параметрами наработки до отказа основных и резервных подсистем, а также параметрами переключающих устройств, которые соответствуют ошибкам первого и второго рода. Формой представления полученных результатов для конечного пользователя является программный продукт, который автоматизировано генерирует семейство графиков для оценки надежности. Игнорирование ошибок переключающих устройств при проектировании систем снижает их фактическую надежность, приводит к недоиспользованию ресурсов резервных элементов, а также увеличивает вероятность аварийных ситуаций.Использование более точной математической модели дает возможность контролировать ошибки переключающих устройств при проектировании системы. Результаты моделирования будут полезны для выбора параметров переключающих устройств.Об'єктом дослідження є невідновлювана система з однократним ковзним резервуванням. Така система складається із двох основних підсистем, однієї резервної та двох перемикальних пристроїв. Поки обидві основні підсистеми працездатні, резервна підсистема перебуває у ненавантаженому стані. Резервна система призначена для заміни будь-якої основної підсистеми після її відмови. Перемикальні пристрої комутують основні підсистеми із резервною. В ході аудиту виявлено, що перемикальні пристрої допускають помилки. Зокрема помилку першого роду, тобто перемикаються завчасно, та помилку другого роду, тобто пропускають момент перемикання. Це знижує надійність системи та веде до недовикористання закладеного в неї ресурсу.Запропоновано підхід, який кількісно враховує вплив помилок першого та другого роду на ймовірність безвідмовної роботи досліджуваної системи під час її проектування. Підхід складається з двох етапів. На першому етапі надійність системи математично описується динамічним деревом відмов. На другому етапі на основі дерева відмов формується марковська модель. Застосовуючи її, можна обчислити ймовірнісні характеристики системи.Отриманим результатом є математична залежність між ймовірністю безвідмовної роботи системи та параметрами елементів системи. Зокрема, параметрами напрацювання до відмови основних та резервних підсистем, а також параметрами перемикальних пристроїв, які відповідають помилкам першого та другого роду. Формою представлення отриманих результатів для кінцевого користувача є програмний продукт, який автоматизовано генерує сімейство графіків для оцінювання надійності. Ігнорування помилок перемикальних пристроїв під час проектування систем знижує їх фактичну надійність, призводить до недовикористання ресурсів резервних елементів, а також збільшує ймовірність аварійних ситуацій.Використання більш точної математичної моделі дає можливість контролювати помилки перемикальних пристроїв під час проектування системи. Результати моделювання будуть корисні для вибору параметрів перемикальних пристроїв