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.

Building an index of well-being for children living in Ireland: conceptual, measurement and policy considerations

This study conceptualised and measured children’s well-being in Ireland and considered how such conceptualisations and approaches to the measurement of well-being might inform social policy for children and families living in Ireland. This research explored what is meant by children’s well-being and how it can be conceptualised and measured so as to reflect the multi-dimensionality of the concept. The study developed an index of well-being that was both theoretically and methodologically robust and could be meaningfully used to inform social policy developments for children and their families. For the first time, an index of well-being for children was developed using an explicitly articulated unifying theory of children’s well-being. Moreover, for the first time an index of wellbeing was developed for 13-year old children living in Ireland using data from Wave 2 of the national longitudinal study of children. The Structural Model of Child Well-being (SMCW), the theoretical framework that underpins the development of this study’s index, offers a comprehensive understanding of well-being. The SMCW builds on, and integrates, a range of already-established theories concerning children’s development, their agency, rights and capabilities into a unifying theory that explains well-being in its entirety. This conceptualisation of well-being moves beyond the narrow focus on child development adopted in some recent studies of children’s well-being and which perpetuate individualised and self-responsibilising conceptualisations of well-being. This study found that the SMCW can be meaningfully applied, both theoretically and operationally, to the construction of an index of well-being for children. While it was not the purpose of this study to validate the SMCW, in the process of developing the index, I concluded that there was a theoretical ‘fit’ between the conceptual orientation of the SMCW and the wider children’s well-being literature. The ‘nested’ structure of the SMCW facilitated the identification of domains, sub-domains and indicators of well-being reflecting typical conventions of index construction. The findings from the resulting index, in both its categorical and continuous forms, demonstrated how a comprehensive theory of well-being can be used to illustrate how children are faring and which children are experiencing poorer or better well-being. Furthermore, this study demonstrated how the SMCW and the resultant index can be meaningfully used to support the implementation and review of the national policy framework for children and young people in Ireland.

Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.