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

该项目将支持在钢铁行业开发以人为本的预测性维护新方法。通过实现作业者（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操作安全性。工人也将受益，因为这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。董事会的目的和目标是：1)与现场工程师合作，他们具有领域知识，了解数据的来源以及它与过程的物理现实之间的关系。2)使用新颖的数据分析技术，结合人工智能和机器学习，创建资产的数字孪生。3)在Azure环境中创建可跨资产扩展的模型。4)从模型中创建基于事件的输出到现有的仪表板中，供维护团队使用。5)为输入数据格式和使用的代码语言（Python, c++， c#等）创建标准要求指南。在这个研究项目中，我们将解决上述挑战，并研究新颖的机器学习工具和工作流程，“人类（主管）在循环中”，将数据驱动的方法与知识建模相结合，开发强大的、可转移的、适应性强的、可用的机器学习模型，用于在线和实时预测性维护。该研究将遵循三个重要方面：检验数据分析环境，该环境将呈现真实的检验数据显示，并将用作人类（监督者）在循环中的方法，以了解预测故障方面的领域知识。主管预测失败的行动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一个可转移的、适应性强的、可用的ML模型。研究和应用迁移学习方法，以提高机器学习模型在整个制造现场的可扩展性和适应性（减少训练过程中的时间和复杂性）。调查、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在一起，以学习与故障相关的适当纠正措施，并推动最佳决策策略。该研究项目将使用来自塔塔钢铁公司集中资产管理平台（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。降低维护的总成本，减少故障的发生，从而提高可持续性，改善钢铁工厂工人的安全和福祉。研究方法将采用以人为中心的方法和用户参与来推动PdM中新型ML工作流程的开发，以增强复杂工业环境中的人类决策。我们期望一些研究成果和方法将普遍应用于PdM，从而通过改进，更安全，更可持续的工业过程带来社会和经济效益。学生将与工业用户（现场工程师和管理人员）密切合作，他们具有了解数据来源及其与物理过程的关系的领域知识。利益相关者（车间工人、管理人员和维护操作员）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。用户将以迭代的方式参与解决方案的评估，以获得将导致进一步改进的持续反馈。

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.