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.

Resigning: a classic grounded theory of working within constraints when caring for stroke patients in the acute general care setting

This research aims to explore the challenges nurses face, when caring for stroke patients on a general medical/surgical ward, in the acute care setting and identify how nurses resolve or process this challenge. Healthcare environments continue to face the pressures of constraints such as reduced staffing levels, budgets, resources and less time, which influence care provision. Patient safety is central in care provision where nurses face the challenge of delivering best quality care when working within constraints. The incidence of stroke is increasing worldwide and internationally stroke units are the recognised minimum standard of care. In Ireland with few designated stroke units in operation many stroke patients are cared for in the acute general care setting. A classic grounded theory methodology was utilised for this study. Data was collected and analysed simultaneously through coding, constant comparison, theoretical sampling and memoing. Individual unstructured interviews with thirty two nurses were carried out. Twenty hours of non-participant observations in the acute general care setting were undertaken. The main concern that emerged was working within constraints. This concern is processed by nurses through resigning which consists of three phases; idealistic striving, resourcing and care accommodation. Through the process of resigning nurses engage in an energy maintenance process enabling them to continue working within constraints. The generation of the theory of resigning explains how nurses’ resolve or process working within constraints. This theory adds to the body of knowledge on stroke care provision. This theory has the potential to enhance nursing care, minimise burnout and make better use of resources while advocating for best care of stroke patients.