Diagnosis, Learning and Optimization of Smart and Connected Products

今天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态，执行远程诊断，并真实的调整其配置以提高性能。这些产品已经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的故障。然后，该中心在需要服务时提供建议。许多问题可以通过“空中”软件更新来解决。如果需要现场服务，该中心会派遣一个服务团队，并提供合适的备件。汽车配置也可以通过软件进行调整，使汽车能够适应用户的行为，以提高安全性和效率。 然而，传感器数据的不完善性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。为了更好地了解产品的真实状况，控制中心可以等待和监控产品更长的时间，但这总是会导致延迟。面对学习和干预之间的紧张关系，中心必须决定何时停止监测，以及及时准确地采取什么行动。这种学习和实践之间的权衡是智能和互联产品运营的核心。现有的传感器嵌入式系统的控制模型大多属于部分可观测马尔可夫决策过程的框架下。众所周知，多状态问题在动态规划中受到维数灾难的影响，因此最优策略即使不是不可能也很难计算，因此大多数现有的工作都考虑具有二进制状态的简单系统。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，这现在是可能的。此外，现有的工作往往假设模型参数是已知的，这并不总是在现实中的情况。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的系统状态，同时做出顺序决策。这项研究计划将：通过充分利用不完善的传感器数据的潜力，为智能产品开发最佳的监测和诊断算法;深入了解如何在真实的时间内对产品进行最佳微调，以最大限度地提高性能;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都很重要。这些主题也提供了一个很好的机会来培训HQP进行数据分析和实时优化。

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.