A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear, non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators, and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classication for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to nd a good trade-o between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data, is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate (possibly) non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines , (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines,(c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) technologies aim at improving the availability, reliability, maintainability, and safety of systems through the development of fault diagnostic and failure prognostic algorithms. In complex engineering systems, such as aircraft, power plants, etc., the prognostic activities have been limited to the component-level, primarily due to the complexity of large-scale engineering systems. However, the output of these prognostic algorithms can be practically useful for the system managers, operators, or maintenance personnel, only if it helps them in making decisions, which are based on system-level parameters. Therefore, there is an emerging need to build health assessment methodologies at the system-level. This research employs techniques from the field of design-of-experiments to build response surface metamodels at the system-level that are built on the foundations provided by component-level damage models.

The miniaturization of electronics packaging is an ongoing trend. The manufacturers are increasing the packaging density to accommodate for more complex designs and increase in operating frequencies. The surface mount devices (SMDs) and today's manufacturing processes are starting to become a limiting factor to this miniaturization. The solution to these problems are embedded passives and new fully-additive manufacturing processes. In this work, a planar inductor is fabricated using a fully-additive process called Sequential Build-Up - Covalent Bonded Metallization (SBU-CBM). A new grafting material for the CBM process is tested, but found to be worse than the previously used one when tested on FR4 substrates. The best design of a planar inductor for high inductance and high Q factor is found to be the circular spiral inductor. A planar circular spiral inductor with a feature size of 75 µm is successfully fabricated using the SBU-CBM process.

Industrialization and globalization of companies has promoted fast, easy and profitable business solutions. A linear business model (LBM) is seen as the most common way to do business. However, recent studies have enlightened how LBMs are detrimental to the health and biological cycles of the earth and its inhabitants. To prevent this, circular business models (CBM) are being introduced as a feasible while still profitable solution. CBMs are defined by Oghaze & Mostaghel, (2018), as the “…rationale of how an organisation creates, delivers, and captures value with slowing, closing, or narrowing flows of the resource loops”, as they base their business on products and services designed to close or slowing the resource loops, decreasing the overall need of virgin resources.  However, to make these major changes in the current way of designing products and services has to be made, taking into consideration the change in design objectives from a linear to a circular model. Today, there are many circular design methods (DM) developed by academia to aid designers in designing sustainable products and services, however, the uptake of such DMs in the industry is quite low. Such a low level of uptake is often due to a poor fit between the DM and the context it is adopted in, which does not aid its seamless integration in existing processes.  Therefore, this research aims to identify DM characteristics that will aid industries to be adopted or adapted by companies transitioning towards CBMs. To do so, three research questions were developed: i) What are the most critical internal and external drivers in a company that enable the successful adoption of a circular design method?  ii) What are the contextual barriers that companies encounter when adopting or adapting circular design methods? iii) How can the design method adopted or adapted be evaluated to improve their implementation in a company? To answer these research questions, a survey was initially carried out, and subsequent interviews were conducted amongst participants of five different companies from various sectors and expertise. The survey and interviews were grounded in previous research concerning types of CBM and different types of barriers and drivers influencing the adoption of circular DMs.  The result from the survey indicates that the ability to make trade-offs when confronted with sustainability aspects, management commitment to a CBM, good communication and sharing of environmental knowledge, both through different departments and with external actors like suppliers, as well as allocating resources such as time, personnel, funds, and having clear business incentives are needed to promote the use of circular DMs. From the interviews, it was also found that barriers to the effective use of DMs are lack of environmental knowledge throughout the supply chain and wrong identification of actors in the supply chain as well as limited communication with external actors. Furthermore, the research revealed several characteristics of the DMs such as simplicity, flexibility and informativity need to be adapted to leverage and overcome the identified contextual drivers and barriers respectively, for their successful deployment within the companies.

Face au défi des entreprises pour le maintien de leurs équipements au maximum de leur fiabilité, de leur disponibilité, de leur rentabilité et de leur sécurité au coût de maintenance minimum, des stratégies de maintenance telles que le CBM et le PHM ont été développées. Pour mettre en œuvre ces stratégies, comme pour la planification des activités de production il est nécessaire de connaître l’aptitude des systèmes à réaliser les futures tâches afin de réaliser le séquencement des opérations de production et de maintenance. Cette thèse présente les éléments d'une fonction générique qui évalue la capacité des systèmes techniques multi-composants à exécuter les tâches de production de biens ou de services assignées. Ce manuscrit présente une proposition de modélisation de systèmes techniques multi-composants représentant les différentes entités qui les composent, leurs états et leurs relations. Plusieurs types d’entités ont été identifiés. Pour chacun d’eux, des inférences sont proposées pour définir à l’intérieur du système l’aptitude de l’entité à accomplir les futures tâches de production à partir des évaluations de son état présent et futur et des évaluations des états présents et futurs des autres entités avec lesquelles elle est en relation. Ces évaluations des états présents et futurs sont basées sur l’exploitation de pronostics locaux des composants. Ces pronostics sont des prévisions qui intrinsèquement comportent des incertitudes pouvant être aléatoires ou épistémiques. La fonction proposée et les inférences prennent en compte ces deux formes d’incertitudes. Pour cela, les traitements et la fonction proposée exploite des éléments de la théorie de Dempster-Shafer. La modélisation des systèmes multi-composants pouvant être représentée sous la forme de graphes dont les états des nœuds dépendent de données comportant des incertitudes, des éléments des réseaux bayésiens sont également utilisés. Cette fonction fournit des indicateurs, sur l’aptitude de chaque entité du système à accomplir les futures tâches de production mais aussi indique les composants nécessitant une intervention afin d’améliorer cette aptitude. Ainsi, ces indicateurs constituent les éléments d'aide à la décision pour la planification des opérations de maintenance de façon conditionnelle et préventive, mais aussi pour la planification des opérations de production. / One major challenge of companies consists in maintaining their technical production resources at the maximum level of reliability, availability, profitability and safety for a minimum maintenance cost, maintenance strategies such as CBM and PHM have been developed. To implement these strategies, as with production activity planning, it is necessary to know the ability of systems to perform future tasks to order production and maintenance operations. This thesis presents the generic function that evaluates the ability of multi-component technical systems to perform the production tasks of goods or services. This manuscript presents a proposal for the modelling of multi-component technical systems representing the different entities that compose them, their states and their relationships. Several types of entities have been identified. For each of them, inferences are proposed to define within the system the entity's ability to perform future production tasks based on its own assessment of its present and future state and the assessments of the present and future states of the other entities with which it is involved. These assessments of present and future states are based on the use of local prognoses of components. These prognoses are predictions that inherently involve uncertainties that may be aleatory or epistemic. The proposed function and inferences take into account these two kinds of uncertainty. To do this, the inferences and the proposed function uses elements of the Dempster-Shafer theory. Since the modelling of multi-component systems can be represented in the form of graphs whose node states depend on data with uncertainties, elements of Bayesian networks are also used. This function provides indicators on the ability of each system entity to perform future production tasks but also indicates the components that require maintenance to improve this ability. Thus, these indicators constitute the elements of decision support for the planning of maintenance operations in a conditional and preventive way, but also for the planning of production tasks.

<p>  </p> <p>The integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management.</p> <p>This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management.</p>

Plant-based meat (PBM) fulfills the criteria set by the European Union regulations for the product to be sold in the EU and is currently a highly consumed conventional meat substitute in the region. Whereas, cell-based meat (CBM), as of July 2023, does not fulfill the criteria set by the EU regulations for the product to be legally sold in the EU. This is due to CBM companies not submitting the required Novel Food application to EFSA to recieve market approval. Therefore, CBM is currently not legally sold in the EU and not consumed in the region. This thesis analyzes the impact of EU regulations and consumer behavior, and how these factors affect the growth of the PBM and CBM markets. The restriction of the thesis, the PBM and CBM markets, has been chosen due to them being the primary forms of alternative proteins that are a more sustainable choice to conventional meat. The intention of the thesis is to bring attention to the interplay between law and business, and the implications of their interconnectedness. The thesis is written with the aspiration to answer the question; to what extent have EU regulations and consumer behavior affected the expansion of the plant and cell-based meat markets? To this end, we began with determining if the legal criteria of the regulatory framework applicable to PBM and CBM constitute equal regulatory conditions for the markets to expand in the EU. The regulatory framework referred to in the thesis is restricted to the primary legislations applicable to alternative proteins, which are the Genetically Modified Organisms (GMO) Regulation (EU) No 1829/2003, Novel Food Regulation (EU) 2015/2283, EU Food Law Regulation (EC) No 178/2002, Food Information to Consumers Regulation (EU) No 1169/2011, and EU labeling requirements. We then conducted a collection of previous research on both the PBM and CBM markets restricted to sustainability, retail market, consumer behavior, financial investment, development, and production processes and costs. Thereafter, we compared the previous research and the aforementioned EU regulations to conclude the impacts of the regulations and the differences in the legal application between PBM and CBM. We also conclude how consumer behavior impacts the growth of a market in addition to the regulatory requirements, and showcase their combined effects on the market. The results suggested that compliance with EU regulations determines if the products are authorized to be legally sold in the EU while consumer behavior influences market acceptance and the extent of growth. The key regulatory difference affecting the ability of CBM to comply and experience similar growth to PBM is the Novel Food Regulation, due to it categorizing cell-based products as novel foods. As of May 2023, no companies in the EU have submitted a Novel Food application to EFSA for CBM.

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