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

该项目将支持开发新的以人为中心的钢铁行业预测性维护方法。通过实现运营商(及其知识)和资产维护数据模型之间的协同作用，将有可能优化维护计划，从而节省成本并提高操作的安全性。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，董事会的目标和目标是：1)与具有领域知识的现场工程师合作，了解数据的来源以及数据如何与流程的物理现实相关。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兄弟。3)创建可在Azure环境中跨资产扩展的模型。4)将基于事件的输出从模型创建到现有仪表板中，供维护团队使用。5)创建关于输入数据格式和代码语言的标准要求的指南(PYTHON，C，C，在这个研究项目中，我们将解决上述挑战，并研究新颖的ML工具和工作流，将数据驱动的方法与知识建模相结合，以开发用于在线和实时预测性维护的健壮、可转移、适应性和可用的ML模型。这项研究将遵循三个重要方面：检查数据分析环境，该环境将呈现检查数据的真实显示，并将被用作人(主管)在循环中的方法，以了解关于预测故障的领域知识。监管者预测失败的行动将有助于对未标记的数据进行标记，这将有助于开发ML模型。因此，监管者的反馈将有助于生成一个可移植、可适应和可用的ML模型。研究和应用转移学习方法，以提高制造现场ML模型的可扩展性和适应性(减少培训过程中的时间和复杂性)。调查、开发和研究混合的以人为中心的PDM方法，将语义丰富的数据与数据驱动的模型相结合，以了解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，重点放在具体的用例上。这项研究的结果将为工业赞助商创造直接利益，因为它将使塔塔钢铁成为可能。以降低维护的总体成本和减少故障的发生，从而提高可持续性并改善钢铁厂的工人安全和福祉。该研究方法将采用以人为中心的方法和用户参与来推动在PDM中开发新的ML工作流，以提高人类在复杂工业环境中的决策能力。我们希望一些研究成果和方法将广泛应用于产品数据管理，从而通过改进、更安全和更可持续的工业过程带来社会效益和经济效益。学生将与具有领域知识的工业用户(现场工程师和经理)密切合作，了解数据的来源及其与物理过程的关系。利益相关者(车间工人、经理和维护操作员)将通过工厂的正式研讨会和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对解决方案的评价，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.