Student Welcome

First, thanks for your initiative to participate in the PHM Society’s PHMEurope Conference in Oslo. The Society so values your efforts that it provides special events for you, as well as subsidizing your registration. Have a look at what you can look forward to:

1. Welcome reception on the evening of July 7 just near the hotel with poster sessions and snacks.
2. New for 2026: On Friday, July 10, three Lunch Speed Mentoring sessions will be held, giving you the chance for discussions with PHMers from industry, academia, and the public sectors. Examples of questions to motivate discussions can be found below. [Register early: https://phm-europe.org/mentorat-session.] Read the bios and answers from the mentors.

3. Colour coded “interest identifiers” on name badges to help identify people to start conversations.
4. Event app: Set up meetings, check and set your schedule, find rooms, message others. See the conference website for the link. Access free conference proceedings to check papers ahead of time.
5. Meet our sponsors: DNV Norway, Collins, Directorate of Higher Education Norway, The Research Council of Norway, MathWorks and OsloMet.
6. Participate in technical demonstrations and hands-on workshops with MathWorks: Day 1 and 2 after lunch. [register early on site]
7. Tuesday-Friday Program of keynotes, tutorials, paper, and panel sessions: Program at-a-glance – PHME2026
   1. Panels of diverse experts where the emphasis is on what’s coming and hearing from the audience: ask a question or identify someone that you can ask for a meeting or find at coffee breaks.
   2. Paper sessions with 3-4 papers allotted 10-15 for summary points and 3-5 minutes for questions.
   3. Free tutorials from recognized experts each morning before paper and panel sessions.
8. Gala dinner and boat cruise on day 2 included with your registration.
   1. Preceded by a free guided walking tour downtown [register early on site].
9. Breakfast provided by the hotel on site. Lunch and coffee breaks covered by the conference.
10. Doctoral Symposium: Competitive program selected 8 new PhD students developing their research programs gives advice from a panel of experts (July 7 but see their posters at the welcome reception).
11. Remember business casual attire throughout. You could dress up a little for the gala dinner.

Examples on questions for the Lunch Speed Mentoring

Below are some examples to motivate the discussions during the Lunch Speed Mentoring session; however, you are welcome to bring your own!

• What skills or methods do you see becoming essential for future prognostics research in industry?
• How do you decide whether a degradation model is “good enough” for deployment in real assets?
• What are the most common mistakes early career researchers make when designing experiments for PHM?
• Which datasets or benchmarks do you consider most credible for validating diagnostic or prognostic algorithms?
• How do you balance model interpretability with predictive accuracy when presenting results to non technical stakeholders?
• What indicators tell you that a research idea has strong industrial relevance?
• How should I approach publishing work that uses proprietary or sensitive asset data?
• What career paths are emerging for researchers who specialize in asset health management?
• How do you evaluate whether a new sensing technology is worth integrating into an existing asset management workflow?

What’s the best way to build long term collaborations with companies working in reliability, maintenance, or PHM?

Research-to-Industry gap

The gap between PHM research and industrial practice remains a recurring challenge, visible in methods validated only on clean benchmarks, research questions that diverge from operational reality, and results that rarely reach deployment.

• What is the actual nature of this gap?
• Where is it widest?
• Who carries the greater responsibility for closing it: academia, industry, or the funding bodies that shape research agendas?

Data challenges

PHM relies on a mix of sensor data, physics-based models, expert knowledge, failure histories, and synthetic sources. What is available in practice is rarely what the methods need. Failures are rare, labels inconsistent, models incomplete, and the meaning of “good” shifts across teams, sectors, and projects.

• Which availability problem most often causes PHM methods to fail in practice, and what alternatives are people using to work around it?
• Which constraints are genuinely unsolvable, and how should methods be designed around them?
• If these constraints are unlikely to disappear, what alternative strategies should the field pursue to still deliver on PHM’s promises?

PHM system design (hardware)

Most PHM today is retrofitted onto assets that were never designed for monitoring, which constrains sensor placement, data quality, and architecture. If PHM were instead designed into a new asset from the start, including decisions about where the model runs, many of today’s assumptions would no longer hold. Edge computing and on-asset inference are making this increasingly feasible.

• What would built-in PHM actually look like, from sensors to decision-making?
• What are the main challenges to developing and adopting built-in PHM?
• Built-in PHM sounds intuitively right, but how do we justify the return on investment to those paying for it?

PHM models

Models are at the core of PHM, yet the gap between what they promise and what they deliver in operation remains wide. Choices about model type, complexity, and assumptions shape not only accuracy but also trust, maintainability, and the ability to generalise beyond the conditions in which they were developed.

• How should models be designed to operate honestly when the data and context they would need for accurate prediction are incomplete?
• Where is the field overinvesting, and where underinvesting, in model development?
• Perfect model performance is unattainable, especially when the data and context required are not available. What would be acceptable in industry, and what commitments would that require?

Explainability, regulation, and adoption

PHM is expected to enable timely decision-making, but it has to operate within regulatory and organisational frameworks built around determinism, explainability, and slow approval cycles. The best-performing models are often opaque, and even when predictions are accurate, the people who act on them may not trust or be allowed to use them in time.

• What most often blocks PHM from reaching operational use?
• How can PHM’s need for timely decisions be reconciled with slow approval processes?
• What does it take for maintenance planners and operators to trust PHM outputs enough to act on them?