Keynote speakers

Beyond Functional Safety – Leveraging Prognostics and Health Management to Achieve Next-Level Safety

Frank Børre Pedersen, Dr.-Ing.
Vice President
Programme Director Maritime R&D
Group Research and Development

DNV AS, Norway
frank.borre.pedersen@dnv.com

Industrial cyber-physical systems are increasingly optimized for performance and cost using advanced digital technologies, including AI. Sensor data, performance models, degradation models, and digital twins offer unprecedented asset information and health status. However, digitalization also introduces new complexity, making design-time decisions on both performance and safety more difficult. Traditional safety approaches—such as physical separation of performance and safety systems and barrier-based strategies toward known failure modes—struggle to provide adequate risk management for the increased dynamics and complexity of these systems. While periodic or adaptive testing of dormant safety systems can confirm integrity during operations, they cannot fully account for the evolving risk picture and unknown failure modes at design-time.

Prognostics and Health Management (PHM) offers a transformative approach by linking design-time assumptions with run-time evidence through real-time condition monitoring, predictive analytics, and remaining useful life estimation. This enables continuous validation of reliability models and dynamic risk assessment as conditions change. By shifting from reactive and preventive maintenance and static failure-rate assumptions to proactive, data-driven decision-making, PHM can optimize performance and enhance safety. This keynote will explore why traditional methods plateau in effectiveness, illustrate scenarios where PHM is indispensable, and present strategies for integrating PHM into safety-critical domains such as Maritime.

“From EVM to PHM, a personal journey”

Dr. Richard Greaves
Fellow of SAE, REng and Institute of Physics

My initial induction to the realm of vibration measurement occurred with the UKAEA (United Kingdom Atomic Energy Authority) at Winfrith Heath in the late 1960’s.  I then moved to Switzerland and worked in the field of aircraft Engine Vibration Monitoring (EVM).  EVM had become usual on civil air transport aircraft in the late 60’s and early 70’s being led by the FAA regulation FAR 25.1305(d)(3) dated October 31 1974 which required an indicator to indicate rotor system unbalance.  The current advisory circular AC 33.83-2B dated February 2 2023 is in force today.

In the late 1960’s engine sensors had evolved from the moving coil type to the solid-state piezoelectric type, operating up to 650^oC (1200^oF) enabling the engine hot section to be monitored.  In initial EVM systems the on-board analysis electronics was analog, but soon evolved to be digital for the 1980’s aircraft, although the front end of the electronics remained analog to handle the charge input from the piezoelectric sensor.

Through work at the SAE E-32 standards committee, AIR1839 helped to standardize EVM systems in the aerospace industry. In 2010 SAE established the HM-1 (Health Management) committee.  This went beyond the realm of engines to include the whole vehicle. This had been   preceded in 2008 when a certain group of companies, lead by Boeing, established the IVHM (Integrated Vehicle Health Management) institute at Cranfield University in the UK.

Whilst diagnostics had pretty well become “run of the mill” by this time concepts such as RUL (Remaining Useful Life) and Prognostics became of major importance.  The PHM society, founded in 2009 became a leading contributor to the discipline.

“Quantum Computing for Prognostics and Health Management”

Enrique López Droguett
Professor, Civil and Environmental Engineering Department
Director, Center for Reliability Science & Engineering
University of California, Los Angeles (UCLA)

Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of risk, reliability, and maintainability systems to predict physical asset’s performance and aid in integrity management. State of the art monitoring systems now generate large amounts of multidimensional data. Moreover, customers are no longer requiring that their new asset investment be highly reliable; instead, they are requiring that their assets possess the capability to perform fault diagnostics and prognostics and provide alerts when components need to be intervened. With this new Big Data at the engineer’s fingertips, more sophisticated methodologies to handle this data have been developed and expanded within the Prognostics and Health Management (PHM) area. Indeed, in the past decade, the availability of powerful computers and special-purpose information processors have led to the development and application of machine and deep learning models for PHM of complex engineering systems (CES) that can identify multifaceted and subtle degradation patterns in monitoring data.

In recent years, a new computing paradigm has gained momentum: quantum computing, which encompasses the use of quantum mechanical phenomena to perform computations. The power and flexibility of a quantum computer comes from the use of qubits that have the ability to be in a superposition state, or multiple states at once, and share entanglement with each other. By leveraging on these properties, quantum computers can perform operations that are difficult to achieve at scale in classical digital computers. This opens the door to new exciting opportunities for the design and performance assessment of complex engineering systems in general, and for the development of new quantum methods for PHM that might be able to recognize intricate interdependent scenarios and components as well as multilayered degradation patterns in CES from multidimensional monitoring data that classical machine learning approaches cannot.

In this lecture, we discuss the main concepts underpinning quantum computing and its advantages, disadvantages, and potential impact on the prognostics and health management of complex engineering systems. We present state-of-the-art quantum optimization, quantum inference, and machine learning algorithms for developing predictive solutions for PHM of CES. We then examine potential opportunities, limitations, and challenges for the future development and deployment of quantum computing based PHM solutions.