Diagnosis, Learning and Optimization of Smart and Connected Products

智能互联产品的诊断、学习和优化

• 批准号: RGPIN-2019-05671
• 负责人: Wang, Jue
• 金额: $ 1.89万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715702
• 关键词: Diagnosis; Learning; Optimization; Smart; Connected

Today's smart product transmit real-time sensor data to a control centre that can monitor its condition, perform remote diagnosis, and adjust its configuration in real time to improve performance. Such products have revolutionized many industries, transforming traditional manufacturers into service providers. Tesla, for example, monitors its cars for malfunction via in-car sensors. The centre then advises when service is needed. Many issues can be rectified via “over-the-air" software updates. If on-site service is needed, the centre dispatches a service team with the right spare parts. Car configuration can also be adjusted via software so that the car can adapt to user behaviour to improve safety and efficiency. However, the imperfection of sensor data makes the operation of smart products complex. Many failure modes may share similar features and be hard to distinguish. To learn more about a product's true condition, the control centre can wait and monitor the product longer, but this invariably introduces delay. Faced with tension between learning and intervention, the centre must decide when to stop monitoring and what action to take in a timely and accurate fashion. This trade-off between learning and doing lies at the heart of the operations of smart and connected products. Existing models for the control of sensor-embedded systems fall mostly under the framework of partially observable Markov decision processes. It is known that multi-state problems suffer from the curse of dimensionality in dynamic programming and hence the optimal policy is difficult, if not impossible, to compute, and thus most existing work considers simple systems with binary states. A network of connected products requires one to generalize the optimization to multi-state systems, which is now possible due to recent advances in artificial intelligence that compresses the state space. Additionally, existing work often assumes model parameters are known, which is not always the case in reality. The proposed work will use a Bayes-adaptive approach to jointly learn model parameters and hidden system state while making sequential decisions. This research program will: develop the optimal monitoring and diagnostic algorithm for smart products by leveraging the full potential of imperfect sensor data; gain insight into how to optimally fine-tune a product in real time to maximize the performance; and develop a new framework that harnesses connectivity among products to aggregate and share information to improve efficiency of maintenance and operations. Research outcomes are important for manufacturers in the Internet of Things era and for academic researchers in industrial engineering and operations research. These topics also provide a strong opportunity to train HQP in data analytics and real-time optimization.

当今的智能产品将实时传感器数据传输到控制中心，控制中心可以监控其状况、执行远程诊断并实时调整其配置以提高性能。此类产品彻底改变了许多行业，将传统制造商转变为服务提供商。例如，特斯拉通过车内传感器监控其汽车是否出现故障。然后，中心会在需要服务时提出建议。许多问题可以通过“无线”软件更新来纠正。如果需要现场服务，中心会派遣服务团队并提供合适的备件。汽车配置还可以通过软件进行调整，使汽车能够适应用户行为，从而提高安全性和效率。 然而，传感器数据的不完善使得智能产品的操作变得复杂。许多故障模式可能具有相似的特征并且难以区分。为了更多地了解产品的真实状况，控制中心可以等待并监控产品更长时间，但这总是会带来延迟。面对学习与干预之间的紧张关系，中心必须及时准确地决定何时停止监控以及采取何种行动。学习和实践之间的这种权衡是智能互联产品运营的核心。 现有的传感器嵌入式系统控制模型大多属于部分可观察马尔可夫决策过程的框架。众所周知，多状态问题受到动态规划中维数诅咒的影响，因此最优策略即使不是不可能计算也很难计算，因此大多数现有工作都考虑具有二元状态的简单系统。连接产品的网络需要将优化推广到多状态系统，由于压缩状态空间的人工智能的最新进展，现在这一点成为可能。此外，现有的工作通常假设模型参数已知，但现实情况并非总是如此。所提出的工作将使用贝叶斯自适应方法来共同学习模型参数和隐藏系统状态，同时做出顺序决策。 该研究计划将：充分利用不完善传感器数据的潜力，开发智能产品的最佳监控和诊断算法；深入了解如何实时优化产品以最大限度地提高性能；开发一个新的框架，利用产品之间的连接来聚合和共享信息，以提高维护和运营的效率。 研究成果对于物联网时代的制造商以及工业工程和运筹学的学术研究人员来说非常重要。这些主题还为 HQP 提供了数据分析和实时优化培训的绝佳机会。