To be updated in late 2021


Matthias Buderath Ph.D.
Senior Expert – Integrated System Health Management at Airbus.


Aeronautical Engineer with more than 35 years of experience in:

  • structural design,
  • system engineering,
  • system development,
  • product- and service support engineering and
  • technology strategy and technology development

In his professional life he worked for a broad product portfolio at Airbus in different engineering disciplines e.g. development of a flight management system for Rotorcrafts, fatigue and damage tolerance for Combat- and Space Systems, system and structural Integrity management for Combat Systems. He also worked as Chief Engineer in the field of new maintenance concepts including Reliability-, Maintainability- and Supportability Engineering.

His senior expert nomination at Airbus Defence and Space is linked to the field expertise in aircraft and space vehicle system integrity management covering health monitoring and management including structural health monitoring and data driven services.

Today he is responsible for the Airbus Defence and Space Technology Strategy and for the Technology Roadmap and Enabling Technology development for Innovative Platform Design for Maintenance and Flight Operations & Data driven Services. In the year 2019 he has also nominated as technology coordinator for Future Combat Air System [FCAS] in Airbus Defence and Space Engineering.

He actively supports national and international research activities, is member of national and international Working Groups related to Integrated Vehicle Health Management and Structural Health Management and Aircraft Integrity Management. He is Member of the
SAE – AISC-SHM, Chairman of the SHM Gap Analysis Working Group and Member of the SAE – IVHM Working Group.

He has published more than 100 papers addressing System- and Structural Health Monitoring and Management, New Maintenance Strategies and Maintenance Optimization, System and Structural Integrity Management including Fleet Management.


Title of presentation:
Aircraft Fleet Availability Optimization using CBM strategy


The quest for continuous refinement of maintenance strategies is driven by the need to improve fleet availability, increase mission readiness / mission capabilities and reduce operation and support cost. The quest for continuous refinement is also strongly link to the increasing complexity of our systems and their operations.

Recently, a lot of interest is invoked in approaches for realizing business value from analytics in aviation. For instance, increasingly adopted predictive maintenance approaches leverage integrated knowledge, fleet-wide sensor data and Artificial Intelligence (AI) to detect early signatures of failure and to allow for timely flight operation and maintenance actions. Recent trends of digitalization across different industries have led to generation of massive amounts of data. As a natural consequence, there is a surge in advanced machine learning techniques being applied to this big data causing smarter and cost effective solutions of on-board and off-board diagnostics and prognostics capabilities.

Furthermore, the way we perceive and interact with IT technologies is undergoing a radical transformation at all levels of society. Identifying technology trends and prioritizing those which have the biggest business potential bring sources of competitive advantage, but this also means rethinking the way people interact, work and exchange information now. As the amount of data that “things” produce increases exponentially, machine computing power shifts to the edge to process streamed data and sends summary data to central systems. Cloud computing provides more flexibility and openness toward customers and other third parties. These trends are driving the next generation of business and the ecosystem within which we operate.

The scope of the key note will cover several main aspects of Predictive Maintenance to ensure expected value proposition which are:

  • Continuous refinement of maintenance strategies – designing a Predictive Maintenance System
  • Leverage from IT technologies to support collaborative development of services based on data sharing and new technologies
  • Roadmap of Next Generation  Maintenance Strategies


Dr. Elizabeth Cross
Senior Lecturer, University of Sheffield, UK

Lizzy Cross is a Senior Lecturer (equivalent to Associate Professor) in the Dynamics Research Group at the University of Sheffield. Before starting her lectureship in 2012 she completed a Bachelors in Mathematics, and Masters and PhD in Mechanical Engineering. She now holds an EPRSC Innovation Fellowship, which is a three-year funding award that allows her to focus solely on research, the topic is on the development of grey-box models (combining physics-based models with machine learning technology). Lizzy’s main research interests span the fields of structural health monitoring (SHM), machine learning and nonlinear system identification. Most of her research projects focus on the analysis of large datasets from monitored structures, where she employs data-driven algorithms to extract useful information. For SHM, these efforts attempt to address the problem of confounding influences – where benign changes in the measurements of structural parameters caused by the environment mask the detectability of damage.

Title of presentation:
Grey-box models for structural health monitoring; combining machine learning and physics-based modelling


Dr. Yi-Qing Ni,
Chair Professor of Smart Structures and Rail Transit, 
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong

Dr. Yi-Qing Ni is a Chair Professor of Smart Structures and Rail Transit at The Hong Kong Polytechnic University, Hong Kong, and the Director of Hong Kong Branch of National Engineering Research Center on Rail Transit Electrification and Automation. His research areas cover structural health monitoring, smart materials and structures, and monitoring and control in rail engineering. Professor Ni has published more than 190 SCI-cited journal papers with an H-index of 38 in Web of Science Core Collection (an H-index of 50 in Google Scholar), and over 310 international conference papers. He received the 2017 “SHM Person of the Year Award” during the 11th International Workshop on Structural Health Monitoring held at Stanford University in September 2017. He is a Vice President of International Society for Structural Health

Title of presentation:
Integration of Sensing Technology and Artificial Intelligence: A Road to Smart Railway

The booming development of high-speed rail (HSR) networks in Europe and Asia is tied up with unprecedented challenges related to safety, ride comfort, environment, energy efficiency and cost-effectiveness. With demand for innovative solutions to newly-emerging multiple challenges, the railway industry is undergoing a revolutionary advance from traditional rail systems to next-generation smart rail systems. Innovative sensors and artificial intelligence (AI) constitute two driven wheels to the attainment of smart rail systems. Sensors and sensor arrays deployed on rail tracks and rolling stock enable online and onboard monitoring of HSR during its routine in-service operation, and AI-powered algorithms making use of the monitoring data help the development of predictive models. The resulting predictive analytics in connection with the industrial internet of things (IIoT) not only enables quick and timely structural condition/fault diagnosis, but can also forecast any potential failures before their occurrence. In particular, cloud-based AI technology, through its convenient integration and connection with other software platforms, can immediately provide all necessary information for fast decision making. This presentation will provide some newly devised sensing systems and AI-enabled damage/fault diagnosis and prognosis algorithms, especially the predictive models from the Bayesian machine learning perspective. Applications to structural damage/fault detection and health assessment of railway systems are illustrated.


Dr. Marco Protti
Leonardo Aircraft Division

Marco Protti joined Leonardo Aircraft Division (formerly Aeritalia) in 1987.
He is currently the Head of Advanced Research and he has the responsibility to oversee the Division Innovation process including research projects portfolio definition as well as the industrial exploitation of the developed technologies and management of the national and International research collaboration network.
In the past 30 years he has matured a large experience in managing National and International research projects acting as Technical Project coordinator and Program Manager.
Since 2014 is member of the Governing Board of the Clean Sky JU where he acts as deputy chairman.
He is the chairman of ACARE Italy, member of the General Assembly of ACARE, of the ASD R&T Commission, of the European Materials Modelling Council – Industrial Advisory Board (EMMC-IAB), of the Technical Committee of the Italian Aerospace Technology Cluster.
He has been also member of the SESAR JU Administrative Board.


Dr. Joseph L. Rose
Paul Morrow Professor Emeritus of Engineering Design and Manufacturing in the Engineering Science and Mechanics department at Penn State University


Dr. Joseph L. Rose is the Paul Morrow Professor Emeritus of Engineering Design and Manufacturing in the Engineering Science and Mechanics department at Penn State University. He is also Founder and Chief Technology Officer of FBS, Inc. doing business as Guidedwave that plans, designs, builds and maintains guided wave solutions for pipelines, rail, aviation, power generation, manufacturing, civil infrastructure, natural gas and oil, nuclear and military industries. Dr. Rose has been an international leader in the fields of wave mechanics, ultrasound and ultrasonic guided waves for over four decades He has received many awards for his ground-breaking work in ultrasonic guided waves for Nondestructive Evaluation and Structural Health Monitoring. Dr. Rose received his Ph.D. from Drexel University in 1970. Dr. Rose is currently a fellow of ASNT, ASME, IEEE, and the British Society for Nondestructive Testing. Dr. Rose is an author of twenty five patents, five text books, over a dozen chapters in various text books, and over 600 articles on ultrasonic NDE, ultrasonic guided waves, wave mechanics, medical ultrasound, adhesive bonding, concrete inspection, pipe and tubing inspection, composite material inspection, ice detection, structural health monitoring, signal processing, and pattern recognition. Dr. Rose has also given countless plenary and keynote lectures at conferences all over the world.  Textbooks include Basic Physics in Diagnostic Ultrasound, John Wiley & Sons Inc., New York, 1979, and Ultrasonic Waves in Solid Media, Cambridge University Press, 1999, and Ultrasonic Guided Waves in Solid Media, Cambridge Press, 2014. He has served as a principal advisor to over 60 Ph.D. and 100 M.S. students.

Dr. Rose also published a book, Seeking the Edge: Thoughts on Wisdom and Success, iUniverse, Inc., 2011. At Penn State, besides teaching ultrasonic and ultrasonic guided wave courses, Rose taught a class on Business Opportunities in Engineering where he alerted students to many entrepreneurship and intrapreneurship paths to success. One of his most famous quotations for both engineering and business students is as follows. “Failure is on the path to success. If you’ve never failed, it means that you are not doing anything.”

A few selected awards:

2014 Roy Sharpe Prize, The British Institute for Nondestructive Testing, Manchester, England for lifetime contributions to Ultrasonic Nondestructive Testing.
2011 SPIE Smart Structures/ NDE Lifetime Achievement Award, San Diego, CA.-In recognition of sustained contributions to the advancement of Nondestructive Evaluation and SHM
2010 Honored at the 16th U.S. National Congress on Theoretical and Applied Mechanics, Celebration of Joseph Rose Accomplishments in Ultrasonics at Penn State University.
2006 ASNT Research Council Innovation Award for work in Guided Waves
2003 ASME Nondestructive Evaluation Engineering Division Founders Award
1996 Penn State University Faculty Scholar Medal for Achievement in Engineering
1995 Finalist in the Discover Awards for Technological Innovation in Aviation and Aerospace, Disneyworld Florida and Discover magazine, for the development of a hand held guided wave probe for aging aircraft inspection.

Title of presentation:
Ultrasonic Guided Waves for Enhanced Acoustic Emission Analysis

Great strides forward in acoustic emission testing have been made over the past few decades in both NDT and SHM. Excellent field tests are being obtained in defect detection and location analysis in countless applications. Presented in this talk will be a discussion of possible benefits of guided wave understanding and utilization for enhanced acoustic emission analysis.

The talk will start with a description of the acousto-ultrasonic technique developed in the 90’s and used in acoustic emission studies and how the subject lends itself to guided waves and acoustic emission. A brief comparison of ordinary ultrasonic testing versus ultrasonic guided wave inspection will be presented followed by a comparison of acoustic emission and ultrasonic guided wave basic principles and applications.

Conventional acoustic emission sensors are basically normal beam receiving sensors that actually operate as a Lamb-type guided wave receiving transducer. A new sensor is proposed in this talk that is an omnidirectional horizontal shear wave sensor. In addition to the excellent detection and location analysis when using the conventional sensor, the new sensor can improve defect location analysis because of the nondispersive character of the low frequency shear horizontal waves that could be propagated from a defect. The shear horizontal sensor is also basically insensitive to water loading or rain on a structure being tested since there is no out-of-plane displacement component reception that does exist with a conventional acoustic emission transducer. Also, because of the three-dimensional receiving characteristics obtained when using both the conventional Lamb-type and shear horizontal sensor, source identification via pattern recognition and artificial intelligence will be possible in many cases.

Basic ultrasonic guided wave and acoustic emission theory will be presented with illustrations on what occurs as a guided wave impinges onto a defect or onto a receiving transducer in ultrasonic inspection as well as what might occur when guided waves are emitted by a crack or other defect in acoustic emission.

Some successes to date in ultrasonic guided wave development and versatility will be presented to visualize benefits that could lie ahead in acoustic emission studies. As a few examples including highlights of long-range pipe inspection, composite repair bond integrity evaluation, transverse crack rail inspection, tomographic imaging in plate-like structures, and applications of a guided wave phased array tool for radar type rapid scanning in plate-like structures from a single sensor location will be reviewed. Function of a guided wave sensor in ice detection is also discussed to illustrate the utility of the guided wave technique.

Finally, aspects of the new omnidirectional shear horizontal wave sensor design and its impact  on  possible enhanced acoustic emission analysis will be discussed.

More to be announced.