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

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 are
exposed 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 methodologies
will 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.

The Centre will nurture 55 new PhD researchers who will be highly sought after in technology companies and application sectors where data and intelligence based systems are being developed and deployed. We expect that our graduates will be nationally in demand for two reasons: firstly, their training occurs in a vibrant and unique environment exposing them to challenging domains and contexts (that provide stretch, ambition and adventure to their projects and capabilities); and, secondly, because of the particular emphasis the Centre will put on people-first approaches. As one of the Google AI leads, Fei-Fei Li, recently put it, "We also want to make technology that makes humans' lives better, our world safer, our lives more productive and better. All this requires a layer of human-level communication and collaboration" [1]. We also expect substantial and attractive opportunities for the CDT's graduates to establish their careers in the Internet Coast region (Swansea Bay City Deal) and Wales. This demand will dovetail well with the lifetime of the Centre and provide momentum for its continuation after the initial EPSRC investment.

With the skills being honed in the Centre, the UK will gain a important competitive advantage which will be a strong talent based-pull, drawing in industrial investment to the UK as the recognition of and demand for human-centred interactions and collaborations with data and intelligence multiplies. Further, those graduates who wish to develop their careers in the academy will be a distinct and needed complement to the likely increased UK community of researchers in AI and big data, bringing both an ability to lead insights and innovation in core computer science (e.g., in HCI or formal methods) allied to talents to shape and challenge their research agenda through a lens that is human-centred and that involves cross-disciplinarity and co-creation.

The PhD training will be the responsibility of a team which includes research leaders in the application of big data and AI in important UK growth sectors - from health and well being to smart manufacturing - that will help the nation achieve a positive and productive economy. Our graduates will tackle impactful challenges during their training and be ready to contribute to nationally important areas from the moment they begin the next steps of their careers. Impact will be further embedded in the training programme with cohorts involved in projects that directly involve communities and stakeholders within our rich innovation ecology in Swansea and the Bay region who will co-create research and participate in deployments, trials and evaluations.

The Centre will also impact by providing evidence of and methods for integrating human-centred approaches within areas of computational science and engineering that have yet to fully exploit their value: for example, while process modelling and verification might seem much removed from the human interface, we will adapt and apply methods from human-computer interaction, one of our Centre's strengths, to develop research questions, prototyping apparatus and evaluations for such specialisms. These valuable new methodologies, embodied in our graduates, will impact on the processes adopted by a wide range of organisations we engage with and who our graduates join.

Finally, as our work is fully focused on putting the human first in big data and intelligent systems contexts, we expect to make a positive contribution to society's understandings of and involvement with these keystone technologies. We hope to reassure, encourage and empower our fellow citizens, and those globally, that in a world of "smart" technology, the most important ingredient is the human experience in all its smartness, glory, despair, joy and even mundanity.

[1] https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/