The old Machinery Directive (2006/42/EC) has governed machine safety and compliance for over two decades, but its time is up. In its place comes the Machinery Regulation (EU) 2023/1230, bringing changes that every OEM, automation specialist, and maintenance professional in the EU—or selling into the EU—must understand.

And this time, there are no opt-outs, no delays, no loopholes: unlike a directive, a regulation applies directly and uniformly across all EU Member States. Translation: you either comply—or you’re out.

ABB’s recent webinar, led by standardisation and certification specialist Anette Wester-Odbratt and market developer Andree Hoffmann, served up a timely breakdown of what’s coming. Here’s what Mainworld readers need to know—and do.

The regulation’s scope is broader and tougher than ever. It now formally includes “quasi-machinery”, digital safety components (including software), partly completed machinery, and introduces new categories like related products (e.g., sensors, slings, chains).

One of the most significant game-changers is mandatory third-party certification for six types of machinery listed in Annex I, especially those with self-evolving behavior or AI-driven functions. For the first time, cyber risks and software integrity are part of the safety equation.

And this is not theoretical. From 2027 onwards, any new machinery placed on the EU market must meet these standards.

Practically it means, if your machine isn’t compliant, it isn’t sellable.

Even the documentation game has changed. Manufacturers can now provide digital-only instructions, but customers can still request printed manuals at the time of purchase. CE markings also get a digital upgrade—products certified by a notified body must now include that body’s ID number next to the CE mark. QR codes are encouraged to streamline access to declarations of conformity, instructions, and technical files.

The regulation also raises the bar for machine software updates, which now require detailed logging of safety-relevant changes—retained for five years and accessible by authorities. That means more traceability and less room for error—or excuses—when accidents happen.

Then there’s “substantial modification”, a term now legally defined. If a machine is modified post-market in a way not planned by the original manufacturer—and that change introduces new hazards—it must be recertified from scratch. Think of it as a forced reset button on your compliance obligations.

One standout element in the new regulation is its emphasis on cybersecurity and communication integrity. Machines must now withstand not only physical stress but also intentional digital interference—in line with the EU’s Cybersecurity Act. A new standard, EN 50742, is in development to guide manufacturers through these challenges.

Another critical evolution: human-machine collaboration is addressed with stricter rules. From ergonomic design to minimizing psychological stress when working near collaborative robots, the regulation is catching up with how automation functions today.
As for harmonized standards, over 850 are in the process of being revised or transferred to align with the new regulation. For machines not covered by an updated harmonized standard, a full notified body certification will be required.

To sum it up, the EU Machinery Regulation (2023/1230) marks a shift in how machines are certified, documented, and maintained across the continent. It’s not a bureaucratic reshuffle—it’s a digital, legal, and safety revolution.

ABB’s experts made one thing clear: 2027 is not far away. If you’re waiting until then to prepare, you’ve already waited too long. This isn’t just about ticking a regulatory box. It’s about staying competitive in an industrial world increasingly shaped by digitalization, AI, and traceable accountability. Companies that prepare now won’t just be compliant—they’ll be ahead of the curve.

Text: Mia Heiskanen   Photos: ABB

“Step into any maintenance department and you’ll see both”, Stoker says. “Younger professionals (the digital natives) navigate dashboards and AR interfaces with ease, while their more experienced colleagues (the digital immigrants) recognize subtle signs in equipment behaviour that no algorithm can yet interpret. The challenge is not to replace one with the other, but to combine them. That mix is our strength.”

This intergenerational blend, Stoker emphasizes, is fundamental to building resilience in Industry 5.0. As he explains in his recent insights on generational dynamics, Digital Natives—Millennials and Gen Z who grew up with data, smart technologies, and AI collaboration—bring agility and digital fluency to maintenance operations. Meanwhile, Digital Immigrants—Baby Boomers and Generation X who witnessed the evolution from CMMS to risk-based maintenance and ISO 55000 implementation—contribute deep judgment, tacit knowledge, and process mastery.

“These two digital cultures view Asset & Maintenance Management through very different lenses,” Stoker notes. “Where Digital Immigrants value structured processes, standards, and deep experience, Digital Natives emphasize agility, connectivity, and continuous innovation. Both perspectives are valid—but too often, they operate in parallel rather than in synergy.”

The key, according to Stoker, lies in creating frameworks that connect these generational approaches. His Asset & Maintenance Management Lemniscate and SSAMM Maintenance Landscape Model provide what he calls “a shared language, rooted in standards, where both experience and innovation can thrive.”

“It’s not about speed alone,” he adds. “It’s about combining perspectives to ensure long-term value. In Industry 5.0, where human-centric, resilient, sustainable, and intelligent systems converge, bridging these digital mindsets isn’t optional—it’s essential.”

New roles are emerging as well. One is the “information steward,” who guarantees the accuracy and accessibility of operational data. The other is the “AI model trainer,” who ensures that intelligent systems align with international frameworks such as ISO 55000:2024 and CEN/TC 319. “When these two roles work in harmony, AI stops being a mysterious black box and becomes a trustworthy partner,” Stoker explains. “That’s when digitalisation truly serves the human, not the other way around.”

Education becomes the bridge to this future. Training programmes must remain rooted in Reliability-Centred Maintenance, FMEA, and condition-based maintenance, yet be modular and adaptive enough to integrate new technologies in the Maintenance 5.0.

“Immersive learning is essential,” he notes. “Federated twins and VR simulations allow people to practise real-world complexity without real-world risk.” Stoker also predicts that scenario-based “disruption drills” will become more common, preparing professionals to adapt when supply chains break down or extreme weather interrupts operations.

International certification is another corner stone. EFNMS qualifications such as the European Maintenance Manager (EMM) and European Maintenance Technician (EMT) provide Europe-wide recognition, while WPiAM’s CAMA and Global Certification Scheme ensure global mobility. “Certification gives us a shared language of competence,” he says. “It allows professionals to move across borders with credibility and organisations to know exactly what skills they are hiring.”

But competence alone is not enough. Diversity is equally critical. “We cannot afford to leave talent untapped,” Stoker argues.

Gender balance widens the pool, but neurodiversity brings unique capabilities. He points out that “many neurodiverse professionals excel at pattern recognition, lateral thinking, and creative problem-solving. These are exactly the skills needed for Industry 5.0, where challenges are complex and non-linear.”

Some countries are already leading the way. Finland integrates human-centric digitalisation and neurodiverse inclusion into its education system. Germany has modernised its dual apprenticeship model to include AI and sustainability. The Netherlands embeds ISO and EN standards directly into higher education, while Sweden actively recruits underrepresented groups into engineering programmes. “These are best practices Europe should share more widely,” Stoker notes. “Cross-border collaboration ensures no country is left behind.”

Looking ahead, Stoker envisions 2035 as a milestone. By then, he believes maintenance education will have matured into a stable yet agile learning ecosystem. “Its stability will come from timeless standards and proven methodologies,” he says. “Its agility will come from modular design that adapts as technology changes. Professionals will follow two clear but complementary paths—information stewards and AI model trainers—working in deliberately mixed teams of digital natives, digital immigrants, and neurodiverse thinkers.”

And above all, education will no longer be seen as a phase of professional life, but as an ongoing ecosystem shaping industrial culture itself. “By 2035, education won’t just prepare people for jobs,” Stoker concludes. “It will shape a culture where technical excellence, sustainability, resilience, and human-centric values are inseparable.”

Text: Mia Heiskanen   Photo: SHUTTERSTOCK

The EU’s tightening emissions policy—especially the Emission Trading System (ETS)—means companies will soon pay more for every ton of CO₂ they emit. While the number of emission allowances is shrinking, prices are simultaneously rising sharply.

“If you don’t act now, you’ll be forced to buy expensive allowances later. It takes time to reduce emissions—it’s not something you fix overnight,” Wim Vancauwenberghe, Director at BEMAS warns, “and under the current economic conditions, energy-efficient production plants already enjoy a major competitive advantage today.”

The Challenge and the Opportunity

“Sustainability isn’t just about reporting. It’s about awareness, goal setting, and – above all – execution. And maintenance and asset management professionals are right at the centre of it,” says BEMAS’ Vancauwenberghe.

“Sustainability in asset management isn’t achieved with one big leap—it’s built through many small, consistent actions,” continues Mark Haarman, Managing Partner at Mainnovation, a consulting firm that specializes in maintenance and asset management for companies in industry, fleet, and infra.

“Actions like cleaning filters, precisely aligning and balancing rotating equipment, or upgrading drives may seem minor on their own, but collectively they make a significant impact on reducing an organization’s environmental footprint,” Haarman adds.

Introducing MORE4Sustainability

The MORE4Sustainability project funded by the European Union and initiated by the Belgian Maintenance Association (BEMAS), reveals how industrial maintenance and asset management teams can become powerful agents of change toward more sustainable and profitable European industry.

Backed by the Interreg North-West Europe program, the project aims to train professionals working in Maintenance, Overhaul, Repair and Engineering on how to adopt sustainable asset management at industrial production sites.

The MORE4Sustainability project kicked off with a study revealing that companies maintaining consistent effort over nine years are achieving up to a 30% reduction in emissions and energy consumption —a figure with both environmental and economic significance.

The Framework:Four Key Areas

BEMAS’ Vancauwenberghe says that strategy definition is at the core of the MORE4Sustainability framework. You start with translating the company’s sustainability strategy into the asset management strategy. Then, in a specific sequence, you can focus on sustainability optimization in the following areas:

1. Asset Portfolio Optimization – Electrify systems, retire inefficient equipment, and invest in modern, low-emission assets.

2. Asset Health Optimization – Apply predictive maintenance and precision care to reduce energy use.

3. Energy Consumption Optimization – Maximize energy efficiency and minimize energy waste.

4. GHG Emission Optimization – Implementing strategies and actions focused on minimizing GHG and other pollutant emissions.

Training is the Cornerstone

The MORE4Sustainability report offers companies a much-needed practical roadmap for transformation.

“The framework is grounded in real-world success stories, modern strategies, and proven methods that help companies cut greenhouse gas emissions and boost energy efficiency—without sacrificing reliability or profitability,” Vancauwenberghe explains.

One of the project’s key outcomes is a comprehensive online training program designed for technical professionals at all levels.

The course is available in English, German, French, and Dutch, making it accessible across much of Europe.

“So far, over 500 professionals have taken part in one of the different training formats.”

“We offer introductory trainings, in-person classroom sessions, and also a full e-learning course that includes certification.”

The e-learning course is free to access, and those who complete the program and pass the final exam earn the title of Certified Sustainable Asset Management Practitioner—the first designation of its kind in Europe.

“Our goal is for participants to return to their companies as ambassadors for sustainable maintenance and implement at least one sustainability initiative.”

Policy Backing and Business Value

So, what’s the fastest way to make European industry more sustainable? With high energy prices and strict emissions targets approaching, policymakers have a key role to play. But instead of relying solely on penalties, experts are urging a shift toward positive incentives—such as ISO certification programs, subsidies, and other supportive measures.

“Yes, rising CO₂ prices will push companies to change. But we also need rewards and support: investment aid, standards, training subsidies. Especially for SMEs,” says Mainnovation’s Haarman Mark Haarman – an expert behind the MORE4Sustainability study.

Governments could, for example, use ETS revenues to fund training programs or introduce accelerated depreciation for sustainable retrofitting and electrification projects, Haarman suggests.

When asked for concrete examples of how sustainable maintenance benefits businesses, Mark Haarman points to one clear finding from the study: Companies that have been early adopters of sustainable asset management have achieved, on average, a 10% improvement in energy efficiency every three years.

What’s notable is that these gains can often be made without major capital investments—simply by optimizing maintenance practices. Predictive maintenance, for instance, does not only increase reliability and uptime, but it also eliminates excess energy consumption by faulty equipment and energy waste by idle running when a part of the production process is down.

Meanwhile, high precision maintenance practices not only extend the lifespan of rotating equipment, but also significantly impact energy efficiency. Companies that integrate sustainability into their full asset management approach—from strategic planning to renewable energy—are seeing fast payback and stronger long-term value.

“In terms of financial value, a 1% sustainability improvement can generate higher ROI than equivalent maintenance cost or uptime gains,” Haarman says.

Bridging the Gap

According to the industry experts at BEMAS and Mainnovation, the lack of a clear framework and tools has been one of the main reasons why many maintenance & asset management organizations are lagging in the sustainability transition.

“Many companies are committed to climate targets at the corporate level,” says Vancauwenberghe, “However, the connection to daily practices on the shop floor is often missing. This training helps bridge that gap.”

The MORE4Sustainability approach is applicable to both organizations already implementing sustainability and those who may not yet have a structured approach to sustainability.

“Whether you’re on the shop floor or in a strategic role, you can make a difference. Sustainability in maintenance is not just for big players—it’s accessible to SMEs too,” says Mark Haarman.

Next Step for the Industry

As companies work toward 2030 climate goals, the demand for measurable sustainability action within operations will only grow.

Training and certification in sustainable asset management may soon become a baseline expectation, driven by ESG auditors, customer procurement requirements, and tightening EU regulations.

“This project needs to spread like oil—in a positive way,” Vancauwenberghe says.

“One trained person can inspire a complete team and start implementing the best practices. The MORE4Sustainability program helps them to set ambitious but realistic goals and shows concrete examples of how to achieve them. By offering the training online for free we hope to further scale up the project’s impact.”

Text: NINA GARLO-MELKAS Photos and images: BEMAS

Camilla Munther

Let’s start with your story: What’s your background, and how did you end up working on this particular research topic?
I have a background in automation and production engineering. My first real contact with industrial maintenance came about 15 years ago. During my bachelor’s degree in automation and mechatronics, I worked part time as an automation engineer installing patented technology for sootblowing. The system didn’t just change how the sootblowers operated — it also provided valuable data to the maintenance department. I remember a technician’s reaction when we showed the new interface: ‘You mean we’ll get an alarm if the steam valve doesn’t open as expected?’. Today, many of us take that kind of alarm for granted. For them, at that time, having access to real-time data on expected versus actual steam flow was revolutionary. It meant they could act proactively, preventing serious damage to expensive equipment. So, even if the main selling point for this new technology was for operational purposes, it became clear that it also could be used to increase the maintenance performance. But that required a change of their work processes.
Maintenance wasn’t a major topic during my university studies, but I ended up doing my master’s thesis on quantifying the effects of maintenance using discrete event simulation. A few years later, I got the opportunity to start a PhD at Chalmers University of Technology, working in a research group focused on production service and maintenance systems. My PhD journey was driven by a desire to understand how people, processes, and strategies must align to successfully implement new technologies and ways of working in maintenance. This led me to explore Smart Maintenance as an organisational innovation — a perspective that I believe is essential for meaningful and sustainable change.

In a nutshell, what’s your thesis about — explained as if to a maintenance professional over coffee?
My thesis is about helping maintenance organizations and all targeted employees become more skillful, consistent, and committed to working with Smart Maintenance. The key is to treat Smart Maintenance not only as a technical upgrade, but as an organisational innovation.
By viewing Smart Maintenance through the lens of innovation theory, we can better understand how to implement it. In my thesis, I use five innovation characteristics: relative advantage, compatibility, complexity, trialability, and observability. Financial calculations alone aren’t enough — there must be a true belief in the relative advantage of Smart Maintenance. Compatibility means aligning initiatives with existing values and norms, starting at a point that fits the current state of the organization.
Complexity can be reduced by breaking down change into smaller, manageable steps, which also increase the trialability that allows experimentation. Observability ensures that progress and results are visible and measurable. The maintenance manager’s task and responsibility become to lead people in change, rather than being a technical leader.
In my thesis, I propose a cyclical, six-step process that supports Smart Maintenance implementation:
1. Benchmark the organisation.
2. Set clear goals.
3. Define strategic priorities.
4. Plan key activities.
5. Elevate implementation.
6. Follow up.
Combined with the insights gained from the perspective of organizational innovation, this process can be used as a framework to guide organizations in being more skillful, consistent, and committed to Smart Maintenance.

From the lab to the shop floor: How could your findings be applied in real-world maintenance or asset management settings?

In Sweden, we benefit from strong collaboration between industry and academia, which allows research to be conducted very close to the shop floor. I’ve worked closely with several industrial companies, and the cyclical process I propose in the thesis is designed to be applicable by industrial maintenance managers. Smart Maintenance implementation will look different in every organisation, but my findings offer a framework to follow.

A handful of industry practitioners have literally read each word in my thesis. From cover to cover. As a researcher who is driven by industrial impact, that is probably one of the most awarding compliments you can get from industry. I see this as a validation that my research is relevant and applicable for real-world settings.

Research is rarely a straight road: What were the toughest hurdles you faced, and how did you overcome them?

To be honest and a bit personal: the vulnerability that comes with doing a PhD is tough — the feeling of constantly exposing your thinking and work. I became very aware of the importance of how I expressed myself (both in text and speech), always striving for clarity and quality. It’s a demanding process, both mentally and emotionally. Luckily, I was surrounded by amazing people. My supervisor and colleagues I had during the PhD studies were always encouraging and supportive. Their feedback helped me refine my ideas and continue to always try to do a bit better.

Industry 5.0 is all about people, sustainability, and resilience: Where do you see your research fitting into this bigger vision?

My research fits naturally into the Industry 5.0 vision because it emphasizes the human side of technological change. Smart Maintenance, when treated as an organizational innovation, becomes a way to empower people — not replace them. It´s about making the whole organization more skillful, consistent, and committed to Smart Maintenance. It supports resilience by helping organizations and all targeted employees adapt to change, and it contributes to sustainability by enabling more efficient and proactive use of resources.

Inspired by principles from innovation management, my work encourages maintenance leaders to foster creativity, challenge existing routines, and actively seek untapped value. It’s about building a culture of learning and exploration. This aligns with Industry 5.0’s emphasis on human-centric, sustainable, and resilient production systems.

Women in maintenance and asset management: From your perspective, how can the sector attract more women and create an environment where they can thrive?

We need to highlight female role models, offer mentorship opportunities, and foster inclusive cultures where different perspectives are valued. It’s also important to challenge outdated stereotypes and show that maintenance is a dynamic, forward-looking field where women can lead, innovate, and make a real impact. Maintenance and asset management are no longer just about fixing machines — they’re about strategy, innovation, and people. This appeal to a diverse range of professionals, including women, who bring valuable perspectives to the field.

What significance has receiving the EFNMS Thesis Award had for you personally and professionally?

Receiving the EFNMS Thesis Award was a great honor! Anyone who has done a PhD — or supported someone through one — knows the level of commitment it requires. To have that work recognized at a European level is incredibly validating. Personally, I´m full of pride and motivation. Professionally, it led to a wider network and potential collaborations. I look forward to advancing maintenance and asset management together with my new contacts!

Looking ahead: If you could choose, what would be the next big challenge for researchers and industry professionals to tackle in your field?

Rather than a single “next” challenge, I see a continuous and evolving one: integrating Smart Maintenance into broader organizational strategies. We need to move from asking “How do we implement this technology?” to “How do we evolve our organization to unlock its full potential?” That means developing new models, metrics, and mindsets that reflect maintenance’s expanding role in digitalized and human-centric production systems. Inspired by innovation management, maintenance leaders should be encouraged to explore untapped value rather than solving the problems we have today, foster cross-functional collaboration, and formulate a clear vision for how maintenance contributes to the company’s future. This shift requires time, resources, and a willingness to challenge traditional ways of thinking — but it’s essential for long-term competitiveness and sustainability. Like innovation leaders promote freedom to explore and experiment, maintenance leaders must create space for discovering untapped values — and connect maintenance to the company’s broader vision and competitiveness.

Your own next chapter: What’s next for you — more research, industry work, teaching, or something else entirely?

Right now, my focus is on being a mom of two — I’m on parental leave and enjoy this chapter of life. At the same time, I’m staying connected to the field, keeping an eye on ongoing applications for new research projects, as well as planning a conference presentation. From January 2026, I’m excited to return to research and I look forward to collaborating with other researchers in the field, as well as industry partners, aiming to continue contributing to the development of maintenance as a strategic and innovative function.

Aleksanteri Hämäläinen

Let’s start with your story: What’s your background, and how did you end up working on this research topic?

I first got into coding in high school at the Päivölä School of Mathematics, which led me to study computer science at Aalto University. I was similarly first introduced to AI and deep learning in high school, and it has fascinated me ever since. That’s why I ended up majoring in Machine Learning, Data Science and Artificial Intelligence.

As I was searching for a topic for a thesis during the last year of my master’s, I was contacted about a topic on AI and condition monitoring by a doctoral researcher I had worked with earlier on a group project. I had pretty much no experience in mechanical engineering but was promised that the topic was very interesting on the AI side of things and that the data was good. In retrospect, I very much disagree with the quality of the data, but realizations about the problems with the data have become the next interesting topic I have pursued, so it’s not like I’m complaining. As a side effect, I’ve also ended up learning quite a bit about mechanical engineering and signal analysis, which I’m certain will be useful in the future.

In a nutshell, what’s your thesis about — explained as if to a maintenance professional over coffee?

My thesis addresses the challenge of using deep learning models for condition monitoring of rotating machines, particularly when data is limited. In most cases it’s not possible to have fault data from every operating condition, such as varying rotating speeds.

Furthermore, even machines of the same model can differ because of manufacturing tolerances, installations, and usage histories. In my research, I demonstrated how few-shot learning, prototypical networks, and careful consideration of operating conditions can be used to get condition monitoring models to work well for gear fault diagnosis in scenarios not covered during the training. The findings specifically showcase good generalisation over rotating speed, which commonly varies in rotating machines, and sensor locations, which represent differences between machines.

From the lab to the shop floor: How could your findings be applied in real-world maintenance or asset management settings?

Many companies involved in condition monitoring are already using AI in some manner or are experimenting with ways to do so. My thesis offers a way to approach some of the key challenges, particularly generalisation over operating conditions and machines. Additionally, the important parts are not overly complex to implement. Instead of encouraging others to exactly replicate what I have, I hope they will integrate my findings into their own work and ideas. I’d be very glad if my research helped someone to overcome a long-standing problem in their systems or sparked an idea of “Aha, this is how I’ll get it to work!”

Research is rarely a straight road: What were the toughest hurdles you faced, and how did you overcome them?

The ever-present problem in using deep learning for condition monitoring is the lack of high quality, publicly available datasets. The current ones generally have a good selection of rotating speeds and loads, but the ones most often used in research all lack essential elements, such as long enough samples, multiple instances of the included fault types, repeated setups, or sufficient healthy data.

The results of training a deep learning model on 10 seconds of fault data and testing it on the next 10 seconds of the same run do not significantly correspond with real world performance.  The aim is not to recognise one exact fault instance, but all faults of the same type. The vibrations of a test rig in a lab do not significantly change within minutes either, so the model could be based on its predictions on the vibration signatures of the installation, manufacturing errors of a component, or even background noise.

This same problem was a concern in my thesis, and ultimately it was only partially overcome. My thesis includes results where model training and testing were conducted with sensors located in different parts of the powertrain in addition to just splitting the data by time. These changes in sensor location introduce significant changes to vibration signatures, simulating changes between two different machines. I was happy with this solution for my thesis but have since then strived for even more realistic test scenarios, by using entirely separate datasets for training and testing.

Industry 5.0 is all about people, sustainability, and resilience: Where do you see your research fitting into this bigger vision?
Wind farms are a perfect example of a setting where condition monitoring is essential for numerous nearly identical rotating machines. Increasing the uptime of wind turbines and decreasing their maintenance costs could help increase their portion of energy production.

What significance has receiving the EFNMS Thesis Award had for you personally and professionally?

I’m very honored to have received the EFNMS award. It showed that there is real interest in the topic and that what I was working on was worth pursuing further. I sincerely hope my next findings will garner similar, and hopefully even greater, interest.

Looking ahead: If you could choose, what would be the next big challenge for researchers and industry professionals to tackle in your field?

I would like to see the creation of better public datasets for research. The ARotor Lab recently published the Aalto Gear Fault Dataset, which includes measurements from multiple healthy and faulty gears and repeated installations of the faulty gears. I hope other research groups will include these elements in future datasets they publish.

However, it would be even more beneficial to have a company publish a dataset containing real fleet data. I of course understand that a lot of data may not be publishable, but I’m sure there is some data that would be at its most valuable when many researchers are working on developing new methods for it. It wouldn’t even have to be highly curated, I’m sure some desperate doctoral researchers, such as I (wink wink), would quickly make a cleaned version.

Afterwards, I would like to see improvements in testing methods to better reflect real-world usage.

What is the point of publishing papers with accuracy close to 99.99% if the results are only relevant to academia?

Your own next chapter: What’s next for you — more research, industry work, teaching, or something else entirely?

I’m currently working on a PhD at the Aalto ARotor Lab focusing on the same topic as my master’s thesis, so you could say my next chapter was not very different from the previous one. As I am writing this, I am at the University of New South Wales in Australia for an exchange with a research group here, so the topic of condition monitoring has taken me to interesting places. After my PhD, I’m leaning towards industry work as the most likely next step.

As I mentioned earlier, I strongly believe that significant progress in AI based condition monitoring is best achieved with larger amounts of higher quality data, which is unfortunately limited in public research. I also enjoy working on problems where I can see more immediate real-world benefits than is common in academia. However, I am not saying that is the only option. I got into this topic by taking a chance on an interesting opportunity and that is the plan for my next step too.