Diagnosis, Learning and Optimization of Smart and Connected Products

如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，执行远程故障诊断，并实时调整其配置以提高性能。这类产品给许多行业带来了革命性的变化，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的故障。然后，该中心会就何时需要服务提出建议。很多问题都可以通过“空中”软件更新来解决。如果需要现场服务，中心会派出一支配备合适备件的服务团队。还可以通过软件调整汽车配置，使汽车能够适应用户行为，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，难以区分。为了更多地了解产品的真实情况，控制中心可以等待和监控产品更长时间，但这不可避免地会导致延迟。面对学习和干预之间的紧张关系，该中心必须决定何时停止监测以及及时和准确地采取什么行动。这种学习和实践之间的权衡是智能互联产品运营的核心。现有的传感器嵌入系统控制模型大多落在部分可观测马尔可夫决策过程的框架下。众所周知，多状态问题受到动态规划中的维度诅咒的影响，因此最优策略很难计算，如果不是不可能的话，因此大多数现有的工作考虑的是具有二进制状态的简单系统。互联产品网络需要将优化推广到多状态系统，这现在是可能的，因为人工智能最近在压缩状态空间方面取得了进展。此外，现有的工作通常假设模型参数是已知的，但在现实中并不总是这样。拟议的工作将使用贝叶斯自适应方法来联合学习模型参数和隐藏系统状态，同时进行顺序决策。这项研究计划将：通过充分利用不完美的传感器数据的全部潜力，为智能产品开发最优的智能监控和诊断算法；深入了解如何以最佳的方式实时微调产品以最大化性能；并开发一个新的框架，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领域的学术研究人员来说都很重要。这些主题还为HQP提供了在数据分析和实时优化方面进行培训的良好机会。

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.