Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
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This doctoral thesis describes three case studies of service engineers participating in organizational change, interacting with managers and consultants. The study investigates the role of differences in professional discourse and culture when these three professional groups interact in organizational change, and how this affects the change result. We bring together two scientific fields, first change management and second, linguistics. The intersection represents the overlapping field of professional discourse and culture. The research design was an explorative multiple case study using qualitative linguistic analyses. The study found that successful organizational change is the result of interaction between professional culture, the organizational culture and the organization/change context. The differences between the professional cultures and discourses can hamper the change process. The practical contribution of this study might be the increased awareness among professionals about their own professional, and often implicit, assumptions. Managers, consultants and service engineers have to be aware of the group dynamics and the specific role of their own typical professional discourse and culture in a change project setting.
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This book is both a short introduction to the recent developments, challenges and opportunities in Aviation Maintenance, Repair and Overhaul(MRO), and at the same time, a presentation of the research focal areas and the key waypoints towards smarter and more sustainable MRO. Innovation and integration have always been key aspects of Aviation. Currently, evolutions in aircraft design, materials and production techniques are ahead of the MRO practices in use.This gap is creating demand for new knowledge to develop and operationalise adaptive, digital and sustainable MRO tools, applicable or integrated in modern aircraft systems and components.
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