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

Three fundamental pillars of decision-centered teamwork

This thesis introduces a novel paradigm in artificial intelligence: decision-centered teamwork. Decision-centered teamwork is the analysis of agent teams that iteratively take joint decisions into solving complex problems. Although teams of agents have been used to take decisions in many important domains, such as: machine learning, crowdsourcing, forecasting systems, and even board games; a study of a general framework for decisioncentered teamwork has never been presented in the literature before. I divide decision-centered teamwork in three fundamental challenges: (i) Agent Selection, which consists of selecting a set of agents from an exponential universe of possible teams; (ii) Aggregation of Opinions, which consists of designing methods to aggregate the opinions of different agents into taking joint team decisions; (iii) Team Assessment, which consists of designing methods to identify whether a team is failing, allowing a “coordinator” to take remedial procedures. In this thesis, I handle all these challenges. For Agent Selection, I introduce novel models of diversity for teams of voting agents. My models rigorously show that teams made of the best agents are not necessarily optimal, and also clarify in which situations diverse teams should be preferred. In particular, I show that diverse teams get stronger as the number of actions increases, by analyzing how the agents’ probability distribution function over actions changes. This has never been presented before in the ensemble systems literature. I also show that diverse teams have a great applicability for design problems, where the objective is to maximize the number of optimal solutions for human selection, combining for the first time social choice with number theory. All of these theoretical models and predictions are verified in real systems, such as Computer Go and architectural design. In particular, for architectural design I optimize the design of buildings with agent teams not only for cost and project requirements, but also for energy-efficiency, being thus an essential domain for sustainability. Concerning Aggregation of Opinions, I evaluate classical ranked voting rules from social choice in Computer Go, only to discover that plurality leads to the best results. This happens because real agents tend to have very noisy rankings. Hence, I create a ranking by sampling extraction technique, leading to significantly better results with the Borda voting rule. A similar study is also performed in the social networks domain, in the context of influence maximization. Additionally, I study a novel problem in social networks: I assume only a subgraph of the network is initially known, and we must spread influence and learn the graph simultaneously. I analyze a linear combination of two greedy algorithms, outperforming both of them. This domain has a great potential for health, as I run experiments in four real-life social networks from the homeless population of Los Angeles, aiming at spreading HIV prevention information. Finally, with regards to Team Assessment, I develop a domain independent team assessment methodology for teams of voting agents. My method is within a machine learning framework, and learns a prediction model over the voting patterns of a team, instead of learning over the possible states of the problem. The methodology is tested and verified in Computer Go and Ensemble Learning