Data-Driven Methods in Maintenance and Reliability Engineering

维护优化和可靠性工程中心（C-MORE）对基于证据的资产管理进行研究（http：cmore.mie.utoronto.ca）。 C-MORE专注于帮助公司管理其实物资产的研究，提供建议，例如购买哪些资产，何时维护它们，以及如何估计其使用寿命的结束。与依赖经验法则或行业直觉相反，驱动这些建议的核心算法基于应用于真实的数据的统计和数学模型。 拟议的研究计划的目标是开发新的数据驱动的模型和方法，将解决一些基本的数据为中心的问题，发现在维护和可靠性工程。更具体地说，我将考虑在现实世界的应用程序中经常出现的问题数据制度-小数据，昂贵的数据和大数据-并提出专门的方法和建模方法来处理这些数据结构。

The Centre for Maintenance Optimization and Reliability Engineering (C-MORE) conducts research into evidence-based asset management (http://cmore.mie.utoronto.ca). C-MORE focuses on research that helps companies manage their physical assets by prescribing recommendations such as which assets to purchase, when to maintain them, and how to estimate the end of their useful lives. As opposed to relying on rules-of-thumb or industry intuition, the core algorithms that drive these recommendations are based on statistical and mathematical models applied to real data. The goal of the proposed research program is to develop new data-driven models and methods that will address a number of fundamental data-centered issues found in maintenance and reliability engineering. More specifically, I will be considering problematic data regimes that commonly arise in real-world applications - small data, costly data, and big data - and proposing specialized methodology and modeling approaches designed to handle such data structures.