Towards Self Driving Processes: Leveraging the Data Revolution

人们普遍认为，我们正处于第四次工业革命的黎明，这将带来一个前所未有的自动化过程工业水平。这种乐观情绪受到无处不在的网络物理系统的偶然融合，易于访问大量数据，不断扩展的计算能力和数据分析的重大理论突破的刺激。我们集团的长期愿景是创建类似于自动驾驶汽车的自动驾驶流程。一个真正的自动驾驶过程将通过生成必要的过程输入，通过学习其动态，通过自动调整其控制器以及通过检测，隔离和预测各种故障，在最小甚至没有人为干扰的情况下运行。 该提案旨在开发一套算法和计算工具，将自动化的愿景带到流程工业中。工业过程的特点是复杂的非线性和随机动态，多速率噪声测量和大型互联单元。该提案针对这些过程特征中的每一个，为自驱动过程建立一个全面系统的方法。为了实现我们的愿景，该项目分为三个子项目：1）模型监控和主动数据生成：我们将开发算法来主动监控过程模型的性能。这将在不注入外部输入的情况下完成，从而防止性能下降。一旦模型被确定为较差，自动算法将生成一组新的足够信息的数据用于模型重新识别。2)自适应建模和控制：使用生成的数据，新模型将被在线识别并嵌入控制策略中。这一办法将根据现有资料采取两种形式。当模型结构可用时，将使用最大似然法结合模拟方法和非线性模型预测控制策略。当模型结构不可用时，将使用深度强化学习等方法来设计控制器。3)故障检测和隔离：将使用基于模拟的方法和机器学习算法在大规模数据集上识别过程故障。特别是已知的故障将通过使用基于模型的算法来识别，未知的故障将通过学习和从大维度数据集中提取特征来识别。这些算法将在我们的工业合作伙伴的帮助下在真实的流程上进行测试。** 该提案是新颖的，因为它具有独特的统一方法，适用于具有相当普遍特征的过程。据我们所知，这是有史以来第一次尝试构建具有上述功能的自动驾驶过程。实现这一愿景将使加拿大的工业受益，使它们具有高效率和全球竞争优势。

It is widely believed that we are at the dawn of the fourth industrial revolution that will bring a level of automation process industry has never seen before. This optimism is spurred by the rather serendipitous confluence of ubiquitous cyber-physical systems, easy access to large volumes of data, expanding computing power and major theoretical breakthroughs in data analytics. Our group's long term vision is to create self-driving processes similar to self driving cars. A truly self-driving autonomous process will operate with minimal to no human interference by generating necessary process inputs, by learning its dynamics, by automatically tuning its controller, and by detecting, isolating and predicting various faults. This proposal seeks to develop a set of algorithms and computational tools to bring this vision of automation to the process industry.******Industrial processes are characterized by complex nonlinear and stochastic dynamics, multi-rate noisy measurements and large interconnected units. This proposal addresses each of these process characteristics to build an over arching systematic approach for self driving processes. In pursuit of our vision, this project is divided into three sub-projects: 1) Model Monitoring and Active Data Generation: We will develop algorithms to actively monitor the performance of a model of the process. This will be done without injecting external inputs and therefore preventing performance degradation. Once a model is determined to be poor an automatic algorithm will generate a new set of sufficiently informative data for model re-identificaiton. 2) Adaptive Modelling and Control: Using the generated data, new models will be identified online and embedded in a control strategy. This approach will take two forms depending on the available information. When a model structure is available a maximum likelihood approach in conjunction with simulation methods and a nonlinear model predictive control strategy will be used. When a model structure is unavailable approaches such as deep reinforcement learning will be used to design the controller. 3) Fault Detection and Isolation: Process faults will be identified using simulation based methods and machine learning algorithms on large scale data sets. In particular known faults will be identified by using model based algorithms and unknown faults will be identified by learning and extracting features from large dimensional data sets. These algorithms will be tested on real processes with the help of our industrial partners. ******This proposal is novel due to its unique unifying approach for processes with rather generic characteristic features. To the best of our knowledge, this is the first ever attempt to build a self driving process with the features described above. Realizing this vision will benefit Canadian industries by making them highly efficient and by giving them a global competitive advantage.