Developing Novel Machine Learning Techniques with Human-in-the-Loop Approach to Enable Better Decision Making on Operations Maintenance

该项目将支持钢铁行业以人为本的预测性维护新方法的开发。通过实现运营商（及其知识）与资产维护数据模型之间的协同作用，可以优化维护计划，从而节省成本并提高运营安全性。工人也将受益，因为它将使他们能够在正确的时间计划维护，避免需要处理不可预见的情况，从而改善工人的福祉，他们暴露在一个非常具有挑战性和复杂的环境中。委员会的宗旨和目标是：1）与拥有领域知识的现场工程师合作，了解数据的来源及其与流程物理现实的关系。2）使用具有人工智能和机器学习的新型数据分析技术，创建资产的数字孪生模型。3）创建可跨Azure环境中的资产扩展的模型。4）将基于事件的输出从模型创建到现有仪表板中，以供维护团队使用。5）创建有关输入数据格式和代码语言的标准要求的指南（Python，C++，在这个研究项目中，我们将解决上述挑战，并研究新的机器学习工具和工作流程，（监督者）在环“，将数据驱动方法与知识建模相结合，为在线和真实的时间预测维护开发健壮、可转移、可适应和可用的ML模型。该研究将遵循三个重要方面：检测数据分析环境，将呈现检测数据的真实显示，并将用作人类（监督者）在环方法，以了解预测故障方面的领域知识。监督者预测失败的行为将有助于标记未标记的数据，这将有助于开发ML模型。因此，主管的反馈将有助于生成可转移、可适应和可用的ML模型。研究和应用迁移学习方法，以提高ML模型在整个制造现场的可扩展性和适应性（减少培训过程中的时间和复杂性）研究、开发和研究以人为中心的混合PdM方法，将语义丰富的数据与数据驱动的模型集成，以学习与故障相关的适当纠正措施，并推动最佳决策策略。该研究项目将使用塔塔钢铁集中资产管理平台（AMDC）的数据集，重点关注特定用例。研究结果将为工业赞助商创造直接利益，因为它将使塔塔钢铁公司。降低维护的总体成本并减少故障的发生，从而提高钢铁厂的可持续性并改善工人的安全和福祉。该研究方法将采用以人为本的方法和用户参与来推动PdM中新型ML工作流的开发，以增强人类在复杂工业环境中的决策能力。我们希望其中的一些研究成果和方法能够普遍应用于PdM，从而通过改进、更安全和更可持续的工业流程带来社会和经济效益。学生将与具有领域知识的工业用户（现场工程师和管理人员）密切合作，了解数据的来源以及它与物理过程的关系。利益相关者（车间工人、经理和维护操作员）将通过正式的研讨会和工厂的日常互动参与解决方案的研究设计和开发。用户将以反复的方式参与对解决方案的评价，以获得持续的反馈，从而进一步改进。

The project will support the development of new human-centric approaches to Predictive Maintenance in the steel industry. By enabling synergy between operators (and their knowledge) and data models for assets maintenance, it will be possible to optimize the maintenance schedule, resulting in cost savings and increased safety of operations. Workers will also benefit because it will enable them to plan maintenance at the right time avoiding the need to deal with unforeseen circumstances, hence improving the wellbeing of workers who areexposed to a very challenging and complex environment.The board aims and objectives are:1) Work with onsite engineers who have the domain knowledge to understand the origin of data and how it relates to the physical realities of the process.2) Use novel data analysis techniques with artificial intelligence and machine learning to create a digital twin of the asset.3) Create models which are scalable across assets within the Azure environment.4) Create event-based outputs from models into existing dashboards for use by maintenance teams.5) Create a guidance on the standard requirements for input data formats and code language(s) (Python, C++, C# etc) to be used.In this research project we will address the above challenges and study novel ML tools and workflows with 'humans (supervisors) in the loop' that integrate data driven approaches with knowledge modelling to develop robust, transferrable, adaptable and usable ML models for in-line and real time predictive maintenance. The research will follow three important strands:An inspection data analysis environment that will present a realistic display of inspection data and will be used as a human (supervisors)-in-the-loop approach to learn about the domain knowledge in terms of predicting failures. Supervisors' actions to predict failure will help with labelling the unlabelled data which will help develop ML models. Consequently, supervisors' feedback will help generate a transferrable, adaptable and usable ML model. Investigate and apply Transfer Learning approaches to improve scalability and adaptability of ML models across a manufacturing site (reducing time and complexity during the training process).Investigate, develop and study hybrid human-centric PdM approaches that integrate semantically enriched data with data-driven models to learn appropriate corrective actions associated to failures and drive optimal decision-making strategies. The research project will use datasets from the centralised asset management platform (AMDC) at Tata Steel, focusing on specific use cases. The findings of the research will create direct benefit for the industrial sponsor as it will enable Tata Steel. to reduce the overall cost of maintenance and reduce occurrences of failures, leading to increased sustainability and improved worker safety and wellbeing in the steel works. The research methodology will employ human-centric approaches and user involvement to drive the development of novel ML workflows in PdM to enhance human decision making in complex industrial environment. We expect that some of the research findings and methodologieswill be of general application to PdM and hence will bring societal and economic benefits through improved, safer and more sustainable industrial processes.The student will work closely with industrial users (onsite engineers and managers) who have the domain knowledge to understand the origin of data and how it relates to the physical processes. Stakeholders (shop floor workers, managers and maintenance operators) will be involved in the research design and development of solutions through formal workshops and day to day interactions at the plant. The users will be involved in evaluation of solutions in an iterative way to gain continuous feedback that will lead to further improvements.