Development of Data-driven Decision Support System using Deep Learning Techniques

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This proposal is motivated by the need to develop a data-driven decision support system (DDSS) framework that can be used in two application areas, namely, prognostics and health management (PHM) of aerospace systems, and forest management services. The main goal of this collaborative research is to develop a decision support framework that uses advanced deep learning (DL) based algorithms to perform data processing, change/anomaly detection, classification, predictive analytics, and decision support in a sequential order to aid decision makers in the applications areas of PHM and forest management. There are two industrial partners involved in this project, Tecsis Corporation and Hegyi Geomatics Inc. Tecsis will use the proposed framework for their PHM applications to provide users with the ability to perceive the health state of a component or subsystem, and to predict its future maintenance policies. Hegyi will adopt the proposed framework for their forest management applications to acquire and analyze forest-based information to assist managers in optimizing the overall benefits of forest resources.In this proposal we have three research themes. In Theme 1, enhancements to multi-class classification will be investigated using hybrid formulations various DL methods by exploiting the strengths each method. Further, some of the challenges associated with DL methods will be addressed by exploring transfer learning, domain adaptation, and data augmentation techniques. Theme 2 will focus on the remaining useful life prediction and biomass estimation by exploring the strengths various DL methods in handling time series data and imagery data. To further improve the performance of these DL methods in terms of accuracy and robustness, domain-informed DL methods, ensemble learning techniques, and uncertainty quantification methods will be investigated. Theme 3 will focus on the development of decision support module based on the accumulation and understanding information gain through classification and predictive modeling methods supported by deep learning techniques included in the proposed DDSS framework. The effectiveness and advantages of the proposed DDSS framework will be demonstrated using the data sets provided by Tecsis and Hegyi for their respective applications. Three Master's, four undergraduates, one PhD and one PDF will be trained in this project in the upcoming areas of data analytics and deep learning. By adopting the results from this research, Tecsis and Hegyi expect to expand their R&D services and cliental base in their respective sectors.