Bayesian Network Approach to Assessing System Reliability for Improving System Design and Optimizing System Maintenance

abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Concurrent, Integrated and Multicriteria Design Support for Mechatronic Systems

RÉSUMÉ Les systèmes mécatroniques sont une combinaison coopérative de composantes mécaniques, électroniques, de contrôle et logiciels. Dans les dernières décennies, Ils ont trouvé diverses applications dans l'industrie et la vie quotidienne. En raison de leur aspect multi-physique, du nombre élevé de leurs composantes et des interconnexions dynamiques entre les différents domaines impliqués dans leur fonctionnement, les dispositifs mécatroniques sont souvent considérés comme hautement complexes ce qui rend la tâche de les concevoir très difficile pour les ingénieurs. Cette complexité inhérente a attiré l’attention de la communauté de recherche en conception, en particulier dans le but d’atteindre une conception optimale des systèmes multi-domaines. Ainsi, cette thèse, représente une recherche originale sur le développement d'un paradigme de conception systématique, intégrée et multi-objectifs pour remplacer l'approche de conception séquentielle traditionnelle qui tend à traiter les différents domaines de la mécatronique séparément. Dans le but d'augmenter l'efficacité, la fiabilité, la facilité de contrôle et sa flexibilité, tout en réduisant la complexité et le coût effectif, ainsi que l'intégration systèmes, cette thèse présente de nouvelles approches pour la conception concurrente et optimale des systèmes mécatroniques aux stades de design conceptuel et détaillé. Les modèles mathématiques et les fondements qui soutiennent cette pensée sont présentés dans cette thèse. Les contributions des travaux de recherche de ce doctorat ont commencé par l'introduction d'un vecteur d'indices appelé le profile mécatronique multicritère (PMM) utilisé pour l'évaluation des concepts lors de la conception des systèmes mécatroniques. Les intégrales floues non linéaires de la théorie de décisions multicritères sont utilisées pour agréger les critères de conception et pour gérer les interactions possibles entre elles. Ensuite, une méthodologie de conception conceptuelle systématique est proposée et formulée. Le soutien à l'intégration d'outils d’aide à la décision multicritère dans le processus de conception est un autre objectif de cette thèse où un certain nombre de cadres de travail sont proposés pour aider les ingénieurs concepteurs à évaluer l’importance de certains critères et des paramètres d'interaction. Ces cadres de travail ne s'appliquent pas uniquement l'évaluation de la conception et de la conception optimales, mais aussi à la détermination des possibles façons d'améliorer les concepts développés. Des méthodes basées sur l’exploitation de données ainsi que des algorithmes d'optimisation sémantique sont utilisées pour identifier les paramètres flous avec le peu d’information disponibles sur les différents choix de concepts et les préférences des concepteurs.----------ABSTRACT Mechatronic systems are a combination of cooperative mechanical, electronics, control and software components. They have found vast applications in industry and everyday life during past decades. Due to their multi-physical aspect, the high number of their components, and the dynamic inter-connections between the different domains involved, mechatronic devices are often considered to be highly complex which makes the design task very tedious and non-trivial. This inherent complexity, has attracted a great deal of attention in the research community, particularly in the context of optimal design of multi-domain systems. To this end, the present thesis represents an original investigation into the development of a systematic, integrated and multi-objective design paradigm to replace the traditional sequential design approach that tends to deal with the different domains separately. With the aim of increasing efficiency, reliability, controllability and flexibility, while reducing complexity and effective cost, and finally facilitating system integration, this thesis presents new approaches towards concurrent and optimal design of mechatronic systems in conceptual and detailed design stages. The mathematical models and foundations which support this thinking are presented in the thesis. The contributions of our research work start with introducing an index vector called Mechatronic Multi-criteria Profile (MMP) used for concept evaluation in design of mechatronic systems. Nonlinear fuzzy integrals from multicriteria decision theory are utilized to aggregate design criteria and for handling possible interactions among them. Then, a systematic conceptual design methodology is proposed and formulated. Supporting the incorporation of multicriteria decision making tools into the design process, is another focus of this work where a number of frameworks are proposed to help the designers with assessment of criteria importance and interaction parameters. These frameworks are not only applicable in optimal design and design evaluation procedures, but also for determining possible ways for design improvements. Both data-driven methods as well as semantic-based optimization algorithms are used to identify the fuzzy parameters with limited available information about the design alternatives and designer preferences. Moreover, a fuzzy-based multi-objective approach has been undertaken for proposing and formulating a detailed design methodology. A unified performance evaluation index is introduced by the means of Choquet integrals and then optimized using a constrained particle swarm optimization (PSO) algorithm

Human robot interaction in a crowded environment

Human Robot Interaction (HRI) is the primary means of establishing natural and affective communication between humans and robots. HRI enables robots to act in a way similar to humans in order to assist in activities that are considered to be laborious, unsafe, or repetitive. Vision based human robot interaction is a major component of HRI, with which visual information is used to interpret how human interaction takes place. Common tasks of HRI include finding pre-trained static or dynamic gestures in an image, which involves localising different key parts of the human body such as the face and hands. This information is subsequently used to extract different gestures. After the initial detection process, the robot is required to comprehend the underlying meaning of these gestures [3]. Thus far, most gesture recognition systems can only detect gestures and identify a person in relatively static environments. This is not realistic for practical applications as difficulties may arise from people‟s movements and changing illumination conditions. Another issue to consider is that of identifying the commanding person in a crowded scene, which is important for interpreting the navigation commands. To this end, it is necessary to associate the gesture to the correct person and automatic reasoning is required to extract the most probable location of the person who has initiated the gesture. In this thesis, we have proposed a practical framework for addressing the above issues. It attempts to achieve a coarse level understanding about a given environment before engaging in active communication. This includes recognizing human robot interaction, where a person has the intention to communicate with the robot. In this regard, it is necessary to differentiate if people present are engaged with each other or their surrounding environment. The basic task is to detect and reason about the environmental context and different interactions so as to respond accordingly. For example, if individuals are engaged in conversation, the robot should realize it is best not to disturb or, if an individual is receptive to the robot‟s interaction, it may approach the person. Finally, if the user is moving in the environment, it can analyse further to understand if any help can be offered in assisting this user. The method proposed in this thesis combines multiple visual cues in a Bayesian framework to identify people in a scene and determine potential intentions. For improving system performance, contextual feedback is used, which allows the Bayesian network to evolve and adjust itself according to the surrounding environment. The results achieved demonstrate the effectiveness of the technique in dealing with human-robot interaction in a relatively crowded environment [7]

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

A review on deep learning applications in prognostics and health management

Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Sensor Fault Detection and Isolation Using System Dynamics Identification Techniques.

A sensor, generally composed of a power supply, a sensing device, a transducer, and a signal processor, behaves like any other dynamic system. A damage in any of its components can cause unexpected deviations in the sensor measurements from the actual values. Due to its increasing importance in system diagnosis and controls, a faulty sensor may lead to a process shut down or even a fatal accident in safety-critical systems. One of the the challenge is to detect and isolate a fault in the sensor from one in the monitored system once abnormal behaviors are observed in the measurements. This work first tackles such a challenge in a single-input-single-output system by tracking the dynamic response and the associated gain factor of the sensor and the monitored system. Inspired by the fact that sensor measurements depict the dynamics of the monitored plant and the sensor, a subspace identification approach is proposed to detect, isolate, and accommodate a sensor failure under regular operation conditions without additional hardware components. In order to deal with the increased complexity in a multiple-input-multiple-output system, an approach is then proposed to identify the underlying relations in a nonlinear dynamic system with a set of linear models, each capturing the system dynamics in the representative operating regime. Evaluated based on the minimum description length principle, the proposed approach identifies the most correlated system inputs for the target output and the associated model structure using genetic algorithm. An approach is finally developed to detect and isolate sensor faults and air leaks in a diesel engine air path system, a highly dynamic multiple-input-multiple-output system. The proposed approach utilizes analytical redundancies among the intake air mass flow rates and the pressures in the boost and intake manifolds. Without the need for a complete model of the target system, fault detectors are constructed in this work using the growing structure multiple model system identification algorithm. Given the addition information on operation regime from the identified model, the proposed approach evaluates both the global and local properties of the generated residuals to detect and isolate the potential sensor and system faults.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89790/1/jiangli_1.pd

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A Novel Data-Driven Fault Tree Methodology for Fault Diagnosis and Prognosis

RÉSUMÉ : La thèse développe une nouvelle méthodologie de diagnostic et de pronostic de défauts dans un système complexe, nommée Interpretable logic tree analysis (ILTA), qui combine les techniques d’extraction de connaissances à partir des bases de données « knowledge discovery in database (KDD) » et l’analyse d’arbre de défaut « fault tree analysis (FTA) ». La méthodologie capitalise les avantages des deux techniques pour appréhender la problématique de diagnostic et de pronostic de défauts. Bien que les arbres de défauts offrent des modèles interprétables pour déterminer les causes possibles à l’origine d’un défaut, leur utilisation pour le diagnostic de défauts dans un système industriel est limitée, en raison de la nécessité de faire appel à des connaissances expertes pour décrire les relations de cause-à-effet entre les processus internes du système. Cependant, il sera intéressant d’exploiter la puissance d’analyse des arbres de défaut mais construit à partir des connaissances explicites et non biaisées extraites directement des bases de données sur la causalité des fautes. Par conséquent, la méthodologie ILTA fonctionne de manière analogue à la logique du modèle d'analyse d'arbre de défaut (FTA) mais avec une implication minimale des experts. Cette approche de modélisation doit rejoindre la logique des experts pour représenter la structure hiérarchique des défauts dans un système complexe. La méthodologie ILTA est appliquée à la gestion des risques de défaillance en fournissant deux modèles d'arborescence avancés interprétables à plusieurs niveaux (MILTA) et au cours du temps (ITCA). Le modèle MILTA est conçu pour accomplir la tâche de diagnostic de défaillance dans les systèmes complexes. Il est capable de décomposer un défaut complexe et de modéliser graphiquement sa structure de causalité dans un arbre à plusieurs niveaux. Par conséquent, un expert est en mesure de visualiser l’influence des relations hiérarchiques de cause à effet menant à la défaillance principale. De plus, quantifier ces causes en attribuant des probabilités aide à comprendre leur contribution dans l’occurrence de la défaillance du système. Le modèle ITCA est conçu pour réaliser la tâche de pronostic de défaillance dans les systèmes complexes. Basé sur une répartition des données au cours du temps, le modèle ITCA capture l’effet du vieillissement du système à travers de l’évolution de la structure de causalité des fautes. Ainsi, il décrit les changements de causalité résultant de la détérioration et du vieillissement au cours de la vie du système.----------ABSTRACT : The thesis develops a new methodology for diagnosis and prognosis of faults in a complex system, called Interpretable logic tree analysis (ILTA), which combines knowledge extraction techniques from knowledge discovery in databases (KDD) and the fault tree analysis (FTA). The methodology combined the advantages of the both techniques for understanding the problem of diagnosis and prognosis of faults. Although fault trees provide interpretable models for determining the possible causes of a fault, its use for fault diagnosis in an industrial system is limited, due to the need for expert knowledge to describe cause-and-effect relationships between internal system processes. However, it will be interesting to exploit the analytical power of fault trees but built from explicit and unbiased knowledge extracted directly from databases on the causality of faults. Therefore, the ILTA methodology works analogously to the logic of the fault tree analysis model (FTA) but with minimal involvement of experts. This modeling approach joins the logic of experts to represent the hierarchical structure of faults in a complex system. The ILTA methodology is applied to failure risk management by providing two interpretable advanced logic models: a multi-level tree (MILTA) and a multilevel tree over time (ITCA). The MILTA model is designed to accomplish the task of diagnosing failure in complex systems. It is able to decompose a complex defect and graphically model its causal structure in a tree on several levels. As a result, an expert is able to visualize the influence of hierarchical cause and effect relationships leading to the main failure. In addition, quantifying these causes by assigning probabilities helps to understand their contribution to the occurrence of system failure. The second model is a logical tree interpretable in time (ITCA), designed to perform the task of prognosis of failure in complex systems. Based on a distribution of data over time, the ITCA model captures the effect of the aging of the system through the evolution of the fault causation structure. Thus, it describes the causal changes resulting from deterioration and aging over the life of the system

Model-Based Engineering of Collaborative Embedded Systems

This Open Access book presents the results of the "Collaborative Embedded Systems" (CrESt) project, aimed at adapting and complementing the methodology underlying modeling techniques developed to cope with the challenges of the dynamic structures of collaborative embedded systems (CESs) based on the SPES development methodology. In order to manage the high complexity of the individual systems and the dynamically formed interaction structures at runtime, advanced and powerful development methods are required that extend the current state of the art in the development of embedded systems and cyber-physical systems. The methodological contributions of the project support the effective and efficient development of CESs in dynamic and uncertain contexts, with special emphasis on the reliability and variability of individual systems and the creation of networks of such systems at runtime. The project was funded by the German Federal Ministry of Education and Research (BMBF), and the case studies are therefore selected from areas that are highly relevant for Germany’s economy (automotive, industrial production, power generation, and robotics). It also supports the digitalization of complex and transformable industrial plants in the context of the German government's "Industry 4.0" initiative, and the project results provide a solid foundation for implementing the German government's high-tech strategy "Innovations for Germany" in the coming years