Keynote Speaker








Prof. Hamid Reza Karimi, Politecnico di Milano, Italy


Bio: Hamid Reza Karimi is currently Professor of Applied Mechanics with the Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy. Karimi’s original research and development achievements span a broad spectrum within the topic of automation/control systems, and intelligence systems with applications to complex systems such as wind turbines, vehicles, robotics and mechatronics. Karimi is an ordinary Member of Academia Europa (MAE), Distinguished Fellow of the International Institute of Acoustics and Vibration (IIAV), Fellow of The International Society for Condition Monitoring (ISCM), Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA), Member of Agder Academy of Science and Letters and also a member of the IFAC Technical Committee on Mechatronic Systems, the IFAC Technical Committee on Robust Control, and the IFAC Technical Committee on Automotive Control. Karimi is the recipient of the 2021 BINDT CM Innovation Award, the 2016-2021 Web of Science Highly Cited Researcher in Engineering, the 2020 IEEE Transactions on Circuits and Systems Guillemin-Cauer Best Paper Award, August-Wilhelm-Scheer Visiting Professorship Award, JSPS (Japan Society for the Promotion of Science) Research Award, and Alexander-von-Humboldt-Stiftung research Award, for instance. Karimi is currently the Editor-in-Chief of the Journal of Cyber-Physical Systems, Subject Editor, Technical Editor or Associate Editor for some international journals and Book Series Editor for Springer, CRC Press and Elsevier. He has also participated as General Chair, keynote/plenary speaker, distinguished speaker or program chair for several international conferences in the areas of Control Systems, Robotics and Mechatronics.


Speech Title: Deep Information Fusion for Fault Diagnosis
The objective of this speech is to address some challenges and recent results on fault diagnosis of mechanical systems, with a focus on advanced artificial intelligence algorithms developments. Specifically, different deep learning models such as deep supervised, unsupervised and reinforcement learning algorithms are examined to establish a trustworthy intelligence fault diagnosis model. The talk will be concluded with some results on the development of explainable intelligence fault diagnosis framework based on post-hoc visualization methods as well as multi-source information fusion with complementary transferability metric for mechanical fault diagnosis.

Keynote Speaker




























Prof. John Mo, RMIT University, Australia


Bio: Prof. John Mo is Professor of Manufacturing Engineering and formerly Head of Discipline of Manufacturing and Materials Engineering at RMIT University, Australia. The Discipline has 400 students, in which 150 students are postgraduate and PhD students. Prior to joining RMIT, he was Senior Principal Research Scientist in Commonwealth Scientific and Industrial Organisation (CSIRO), the Australian government research agency. He led research teams with 20 researchers working on many large scale government and industry sponsored projects including electricity market simulation, infrastructure protection, wireless communication, fault detection and operations scheduling. In particular, the fault detection and diagnosis projects have continued from CSIRO to RMIT. The research outcomes have been incorporated into many CNC machine controls to ensure smooth and faultless operations due to complexity and sophistication of this type of machine tools. Prof. Mo graduated from University of Hong Kong in Mechanical Engineering in 1975 and received his PhD from Loughborough University (UK) in 1989. He has over 400 publications including three monographs, 150 journal articles and 220 refereed conferences papers, 15 book chapters, 12 public reports, 15 keynote speeches and undisclosed number of commercial consultancy reports.


Speech Title: Engineering systems fault prediction

Abstract: Modern engineering systems are complex and sophisticated. Any minor issue can be escalated to complete system failure. With Industry 4.0, enormous amount of information can be collected from system processes online - the commonly knonw health monitoring paradigm. The idea is to maintain the system in good operating state. To achieve this goal, many researchers have contributed their efforts using digital signal processing. However, most researches are focused on recognition of fault conditions only. This means by the time when a fault is recognised, at least one problem has surfaced affecting performance of the system. This is not ideal for systems that requires absolute reliability in continuous operation mode.
One of the difficult issues in health monitoring is prevention of faults. Faults are abrupt deterioration of the machine’s capabilities leaving the machine operator almost no time to take remedial action preventing rejects or causing damage to the machine. Detection of this type of machinery faults requires instantaneous machine signal discrimination. Therefore, health monitoring assumes that the system deteriorates over time (but still performing satisfactorily) so that continuous monitoring of certain operating parameters of the system will be able to detect problems before they occur. Therefore, the ability to continuously measure and process signals from the system to check its status is pre-requisite to health monitoring. A good example is monitoring wear and tear on cutting tools. The idea is to monitor certain conditions on the system by some indicative measures based on theoretical model of the operation so that decision to stop and repair can be made before the cutting tool malfunctions.
This paper reviews a range of methods of fault prognosis and explains the development of a new system health condition indicator, Sum Standard Deviation Frequency. This indicator is computed from a new computational process that segments raw data streams into time segments and the segments are synchrosqueezed continuous wavelet transformed. So long as sufficient data is available, the mothod does not link to any application context information of the raw signal data stream hence making it context independent. More importantly, the trend that the system is going into faulty state can be monitored so that while the performance is deteriorating and the system is still working within acceptable conditions, preparation for providing the right repair can be made and the system can be stopped before producing bad outcomes.

Invited Speaker

Assoc. Prof. Dmitry Ivanov, University of Bristol, UK


Bio: Dmitry Ivanov (Dr Dmitry Ivanov - Our People (bristol.ac.uk), Dmitry Ivanov-Google Scholar) is Associate Professor in Composites Manufacturing at the University of Bristol, Theme Lead in Manufacturing and Design at Bristol Composites Institute (Bristol Composites Institute | Bristol Composites Institute | University of Bristol). His research path started in KULeuven, Belgium in 2002 and spanned from multi-scale theories to applications in process modelling and innovative manufacturing. Dmitry works in Bristol since 2011 and led projects funded by industry, EU and UK research councils on additive manufacturing, multi-matrix composites, process analysis and control.


Speech Title: Manufacturing approaches for multi-material hybrid composites

Abstract: Manufacturing of polymer composites is a field of incredible creativity. There is a wide range of processing solutions tailored to the requirements of performance, cost, and functionality. With the innovations in fibre deposition, steering, hybridisation, and forming the composite structures become increasingly more efficient and find new applications featuring challenging geometries and service conditions. In this talk we will discuss some of the new directions, where the composite properties can be boosted even further through emerging manufacturing concepts that bring together various materials in one interconnected system. Driven by the requirements of sustainability and efficiency, the concepts such as Multi-Matrix Continuously-Reinforced Composites (MMCRC) have been recently introduced. The concept allow designing and mapping the zones of different functions within one structure. The presentation will cover successful examples of MMCRC, examine implications of using these materials, and discuss their new features.

Invited Speaker



Assoc. Prof. Mainul Islam, University of Southern Queensland, Australia

Bio: Dr Mainul Islam is an academic in Mechanical Engineering with specialisation in Composite Materials in the School of Mechanical and Electrical Engineering at the University of Southern Queensland (USQ), Australia. He also belongs to the Centre for Future Materials at USQ for conducting research. He completed his PhD in Mechanical Engineering at the University of Newcastle, Australia. He is a graduate and also former Assistant Professor in Mechanical Engineering of Khulna University of Engineering & Technology (KUET), Bangladesh. He got Master degree in Structural Engineering from Kyushu University, Japan. He has been with USQ since 2008 just after completing PhD. His current research interests are in the areas of smart and sustainable composites and shape memory polymeric materials especially for infrastructure and biomedical applications. He has over 150 research publications based on his research outcomes. He has been able to secure a total of over $1.5M research funding jointly and individually during his academic career so far. He has supervised over 15 PhD students to their completion. He serves as Editorial Board member for several renowned journals and Technical Committee member for several international conferences. He is a Fellow (FIEAust) and Chartered Professional Engineer (CPEng) of Engineers Australia.

Speech Title: Building the Future: Innovation in Green Sandwich Structures for Construction Excellence

Abstract: This study explores three sandwich structures utilizing Formica sheets and perlite/sodium silicate foam as the core, with or without a paper honeycomb. By employing three-point bending tests and Lee's thermal conductivity apparatus, we assessed flexural characteristics and thermal conductivity. The results showcased a remarkable enhancement in flexural properties, including core shear stress and energy absorption, with the incorporation of paper honeycomb reinforcement. Thermal conductivity and flexural properties of these structures proved highly compatible with existing building materials, as described in relevant literature. Failure analysis revealed premature core buckling in paper honeycomb-reinforced sandwiches, contrasting with the ability of foam-filled honeycomb core sandwiches to sustain higher loads, exhibiting core shear failure, skin rupture, and delamination. Foam-filled paper honeycomb structures emerged as capable of withstanding higher bending loads, presenting promising applications as eco-friendly materials for building thermal insulation.