A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.

Finding optimal alternatives based on efficient comparative preference inference

Choosing the right or the best option is often a demanding and challenging task for the user (e.g., a customer in an online retailer) when there are many available alternatives. In fact, the user rarely knows which offering will provide the highest value. To reduce the complexity of the choice process, automated recommender systems generate personalized recommendations. These recommendations take into account the preferences collected from the user in an explicit (e.g., letting users express their opinion about items) or implicit (e.g., studying some behavioral features) way. Such systems are widespread; research indicates that they increase the customers' satisfaction and lead to higher sales. Preference handling is one of the core issues in the design of every recommender system. This kind of system often aims at guiding users in a personalized way to interesting or useful options in a large space of possible options. Therefore, it is important for them to catch and model the user's preferences as accurately as possible. In this thesis, we develop a comparative preference-based user model to represent the user's preferences in conversational recommender systems. This type of user model allows the recommender system to capture several preference nuances from the user's feedback. We show that, when applied to conversational recommender systems, the comparative preference-based model is able to guide the user towards the best option while the system is interacting with her. We empirically test and validate the suitability and the practical computational aspects of the comparative preference-based user model and the related preference relations by comparing them to a sum of weights-based user model and the related preference relations. Product configuration, scheduling a meeting and the construction of autonomous agents are among several artificial intelligence tasks that involve a process of constrained optimization, that is, optimization of behavior or options subject to given constraints with regards to a set of preferences. When solving a constrained optimization problem, pruning techniques, such as the branch and bound technique, point at directing the search towards the best assignments, thus allowing the bounding functions to prune more branches in the search tree. Several constrained optimization problems may exhibit dominance relations. These dominance relations can be particularly useful in constrained optimization problems as they can instigate new ways (rules) of pruning non optimal solutions. Such pruning methods can achieve dramatic reductions in the search space while looking for optimal solutions. A number of constrained optimization problems can model the user's preferences using the comparative preferences. In this thesis, we develop a set of pruning rules used in the branch and bound technique to efficiently solve this kind of optimization problem. More specifically, we show how to generate newly defined pruning rules from a dominance algorithm that refers to a set of comparative preferences. These rules include pruning approaches (and combinations of them) which can drastically prune the search space. They mainly reduce the number of (expensive) pairwise comparisons performed during the search while guiding constrained optimization algorithms to find optimal solutions. Our experimental results show that the pruning rules that we have developed and their different combinations have varying impact on the performance of the branch and bound technique.

Non-product related environmental process and production methods (NPR-PPMs): a view from international trade law and international environmental governance

The primary aim of this thesis is to analyse legal and governance issues in the use of Environmental NPR-PPMs, particularly those aiming to promote sustainable practices or to protect natural resources. NPR-PPMs have traditionally been thought of as being incompatible with the rules of the World Trade Organization (WTO). However, the issue remains untouched by WTO adjudicatory bodies. One can suggest that WTO adjudicatory bodies may want to leave this issue to the Members, but the analysis of the case law also seems to indicate that the question of legality of NPR-PPMs has not been brought ‘as such’ in dispute settlement. This thesis advances the argument that despite the fact that the legal status of NPR-PPMs remains unsettled, during the last decades adjudicatory bodies have been scrutinising environmental measures based on NPR-PPMs just as another expression of the regulatory autonomy of the Members. Though NPR-PPMs are regulatory choices associated with a wide range of environmental concerns, trade disputes giving rise to questions related to the legality of process-based measures have been mainly associated with the protection of marine wildlife (i.e., fishing techniques threatening or affecting animal species). This thesis argues that environmental objectives articulated as NPR-PPMs can indeed qualify as legitimate objectives both under the GATT and the TBT Agreement. However, an important challenge for the their compatibility with WTO law relate to aspects associated with arbitrary or unjustifiable discrimination. In the assessment of discrimination procedural issues play an important role. This thesis also elucidates other important dimensions to the issue from the perspective of global governance. One of the arguments advanced in this thesis is that a comprehensive analysis of environmental NPR-PPMs should consider not only their role in what is regarded as trade barriers (governmental and market-driven), but also their significance in global objectives such as the transition towards a green economy and sustainable patterns of consumption and production.

Monitoring and modelling the morphodynamic evolution of a breached barrier beach system

Predicting the evolution of a coastal cell requires the identification of the key drivers of morphology. Soft coastlines are naturally dynamic but severe storm events and even human intervention can accelerate any changes that are occurring. However, when erosive events such as barrier breaching occur with no obvious contributory factors, a deeper understanding of the underlying coastal processes is required. Ideally conclusions on morphological drivers should be drawn from field data collection and remote sensing over a long period of time. Unfortunately, when the Rossbeigh barrier beach in Dingle Bay, County Kerry, began to erode rapidly in the early 2000’s, eventually leading to it breaching in 2008, no such baseline data existed. This thesis presents a study of the morphodynamic evolution of the Inner Dingle Bay coastal system. The study combines existing coastal zone analysis approaches with experimental field data collection techniques and a novel approach to long term morphodynamic modelling to predict the evolution of the barrier beach inlet system. A conceptual model describing the long term evolution of Inner Dingle Bay in 5 stages post breaching was developed. The dominant coastal processes driving the evolution of the coastal system were identified and quantified. A new methodology of long term process based numerical modelling approach to coastal evolution was developed. This method was used to predict over 20 years of coastal evolution in Inner Dingle Bay. On a broader context this thesis utilised several experimental coastal zone data collection and analysis methods such as ocean radar and grain size trend analysis. These were applied during the study and their suitability to a dynamic coastal system was assessed.

Building effectiveness communication ratios (BECs): an integrated ‘life-cycle’ methodology for mitigating energy-use in buildings

Current building regulations are generally prescriptive in nature. It is widely accepted in Europe that this form of building regulation is stifling technological innovation and leading to inadequate energy efficiency in the building stock. This has increased the motivation to move design practices towards a more ‘performance-based’ model in order to mitigate inflated levels of energy-use consumed by the building stock. A performance based model assesses the interaction of all building elements and the resulting impact on holistic building energy-use. However, this is a nebulous task due to building energy-use being affected by a myriad of heterogeneous agents. Accordingly, it is imperative that appropriate methods, tools and technologies are employed for energy prediction, measurement and evaluation throughout the project’s life cycle. This research also considers that it is imperative that the data is universally accessible by all stakeholders. The use of a centrally based product model for exchange of building information is explored. This research describes the development and implementation of a new building energy-use performance assessment methodology. Termed the Building Effectiveness Communications ratios (BECs) methodology, this performance-based framework is capable of translating complex definitions of sustainability for energy efficiency and depicting universally understandable views at all stage of the Building Life Cycle (BLC) to the project’s stakeholders. The enabling yardsticks of building energy-use performance, termed Ir and Pr, provide continuous design and operations feedback in order to aid the building’s decision makers. Utilised effectively, the methodology is capable of delivering quality assurance throughout the BLC by providing project teams with quantitative measurement of energy efficiency. Armed with these superior enabling tools for project stakeholder communication, it is envisaged that project teams will be better placed to augment a knowledge base and generate more efficient additions to the building stock.

Elicitation techniques for the evaluation of speech under stress: Towards a framework for use in a controlled environment

Existing work in Computer Science and Electronic Engineering demonstrates that Digital Signal Processing techniques can effectively identify the presence of stress in the speech signal. These techniques use datasets containing real or actual stress samples i.e. real-life stress such as 911 calls and so on. Studies that use simulated or laboratory-induced stress have been less successful and inconsistent. Pervasive, ubiquitous computing is increasingly moving towards voice-activated and voice-controlled systems and devices. Speech recognition and speaker identification algorithms will have to improve and take emotional speech into account. Modelling the influence of stress on speech and voice is of interest to researchers from many different disciplines including security, telecommunications, psychology, speech science, forensics and Human Computer Interaction (HCI). The aim of this work is to assess the impact of moderate stress on the speech signal. In order to do this, a dataset of laboratory-induced stress is required. While attempting to build this dataset it became apparent that reliably inducing measurable stress in a controlled environment, when speech is a requirement, is a challenging task. This work focuses on the use of a variety of stressors to elicit a stress response during tasks that involve speech content. Biosignal analysis (commercial Brain Computer Interfaces, eye tracking and skin resistance) is used to verify and quantify the stress response, if any. This thesis explains the basis of the author’s hypotheses on the elicitation of affectively-toned speech and presents the results of several studies carried out throughout the PhD research period. These results show that the elicitation of stress, particularly the induction of affectively-toned speech, is not a simple matter and that many modulating factors influence the stress response process. A model is proposed to reflect the author’s hypothesis on the emotional response pathways relating to the elicitation of stress with a required speech content. Finally the author provides guidelines and recommendations for future research on speech under stress. Further research paths are identified and a roadmap for future research in this area is defined.