Special Sessions

Special Sessions @TESConf2020

 

This year's conference has acquired specifc interests to key areas of TES and a variety of Special Academic Sessions have been proposed. Please click the topic for more information on each of the sessions. Organisers are listed for information only and please DO NOT send abstracts or full-length papers to them. The submissions will be through the submissions portal.

 

SS1: Artificial Intelligence-Enabled Data Analytics for Through-life Engineering System Health Monitoring

Session Lead: Professor Ruqiang Yan, Xi’an Jiaotong University

Session Abstract:

Advancement of sensor technologies significantly improves the observability of through-life engineering systems towards improved system controllability and reliability. The broad applications of sensors for health monitoring in through-life engineering systems provide a large amount of data with high variety and pose a big challenge on the data processing side. The significant development on artificial intelligence (AI) provides a promising approach for effectively learning from the complex and integrated multimodal data. This special session is envisioned to drive the fundamental advances in the field of  AI-enabled data analytics for through-life engineering system health monitoring, ranging from manufacturing to healthcare, civil, and energy systems. It is intended that this Special Session will show the current state of the art of AI techniques for data analytics, and outline a roadmap to a generalized approach for through-life engineering system health monitoring across many domains and sectors. Original research contributions and review papers are sought in areas including (but not limited to):

  • Feature extraction through unsupervised learning and decision-making through supervised learning

  • Fusion of physical knowledge and data-driven models for reliable and robust decision-making

  • Fusion of homogenous and/or heterogeneous data at data, feature, and decision level

  • Deep learning and transfer learning in through-life engineering application

 

SS2: AI and Robotics applications for inspection and repair of complex systems

Session Lead: Dr Luca Zanotti Fragonara, Professor Antonios Tsourdos, Cranfield University

Session Abstract:

The virtuous synergy between the recent developments in deep learning and robotics allowed for revolutionary applications in the area of inspection and repair of complex structures for relatively small costs.

Inspection tasks that require non-contact or contact technologies are being used for different industrial, aerospace or naval applications. Several issues are still arising, as only few non-destructive testing methodologies have been successfully integrated for inspections and even less methods have been successfully automated for repair. For instance, there seem to be great promise in the applications of imaging techniques for NDT jointly with deep learning-based feature extraction for damage detection, classification and sizing. This should contribute to render the inspection operation more reliable and robust, once automatized, and less-prone to operator’s mistake.

 Envisaged topics:

  • Design and control of robots for inspection

  • Manipulation control for inspections

  • AI-based perception techniques

  • Reinforcement learning for manipulation/planning

  • Localization and mapping in visually-degraded/operational conditions

  • Sensors for robotic inspections

  • Data management of robotic inspections

  • Digital twinning for inspection planning

  • Applications of robotics for aerospace, industrial or naval inspections & testing

  • Experiences from real-life infrastructure inspections

 

SS3: Industrial Product-Service System (iPS2)

Session Lead: Professor Tomohiko Sakao, Linköping University

Session Abstract:

Industrial Product-Service System (iPS2) is a system that integrates products and services based on a systems thinking. Compared with services alone, it creates opportunities for better economic and environmental outcomes because of the additional variable, i.e., product design, and the inter-dependency between the products and services: better product design has a potential to enlarge the solution space for effective services provided. But, designing and delivering iPS2 leading to the expected outcomes is a highly challenging task due to the increased complexity. Now, recent development of information technologies such as connectivity and data analytics provides possibilities to realize iPS2 in new ways. Therefore, many industrial actors invest substantially on iPS2. This session invites contributions from academia and industry aiming to share the new knowledge and experience and also exchange ideas that tackle this highly challenging task.

 

SS4: Self-engineering and associated areas

Session Lead: Dr Sam Brooks, Professor Rajkumar Roy, City, University of London

Session Abstract:

What is a self-engineering system?
A system is self-engineering when it registers and responds to a loss in function or operation capability, and automatically takes action to return the functionality. Some key characteristics include:

  1. There must be no human/user intervention, and system response/behaviour should be automatic.

  2. It must have the ability to restore or partially restore lost function.

  3. It must be built into the system, not added later.

  4. The aim should be to avoid/reduce maintenance, prolong life and/or increase the system robustness.

Inspiration for new self-engineering systems can come from biology or existing technology. Examples of current self-engineering technology and possible topics include:

  • Self-healing materials and systems

  • Self-reconfiguration for function redistribution

  • Self-adapting robots and systems

  • Automated repair and maintenance

  • Self-sealing structures and systems

  • Self-repairing parts or systems

  • Biology inspired repair or redistribution

  • Self-maintenance machines

  • Built-in functional or component redundancy

  • Automatic non-destructive testing

  • Self-managing or self-optimising systems

This is not an exhaustive list of topics. This session aims brings together researchers from a range of areas science and engineering. In computer science and electronics, self-engineering concepts are regularly utilised. However, in many other engineering sectors, these technologies are mostly still in the laboratory or early development stages. Self-engineering systems could form a key part of future through-life engineering services.

 

SS5: Sustainability and Through-life Engineering

Session Lead: Professor Konstantinos Salonitis, Cranfield University
                        Dr Fiona Charnley, Dr Okechukwu Okorie, Exeter University

Session Abstract:

Through life engineering promises to address the needs of high-value products and systems from conceptual design all the way to end of life.  Sustainability on the other hand is focused on the implications that products and systems might have in the future generations. The special session invites papers on how the principles of through life engineering are addressing the sustainability needs through a number of different approaches such as:

  • Circular economy

  • Design for sustainability

  • Design for remanufacturing

  • Industry 4.0 and Industrial internet of things

The proposed papers can be based on emerging new techniques such as:

  • Simulation

  • Artificial intelligence

  • Big Data analytics

  • Automation

  • Virtual and augmented reality