Generative AI and Predictive Maintenance

This article goes into detail about Generative AI and Predictive Maintenance. It describes how Generative AI is transforming Predictive Maintenance, e, in detail, the following aspects:

• Challenges Facing Predictive Maintenance in Today’s Maintenance Organizations
• Historical evolution of condition monitoring
• AI Terminology
• IA IA IA Generativa
• Machine Learning (machine learning)
• The Digital Twin
• Benefits of Generative AI in Predictive Maintenance
• Application examples

This article is a transcript of the Webinar presented by Tümay Karaver, and ARTHESIAN, a 17 December 2025.

Generative AI and Predictive Maintenance Fig. 1 – A new era

Introduction – What about decision making??

What happens when we add big language models and generative AI on top of what we already do in condition monitoring?

We really did excellent:

• Collecting data.
• Detect patterns,
• To trigger alarms,
• Trend over time.

But the part that is not yet automated is decision making..

Generative AI and Predictive Maintenance Fig. 2 – Decision making

Transform all these signals into clear and consistent information, about what it means and what we do next, still depends on a small number of experienced people.

What to do to delete all this information?

And parallel to these developments in condition monitoring, Electrical signature analysis has grown in popularity a lot because it covers so many types of faults. Covers electrical faults, mechanical failures and even process-related issues.

And it meets many requirements if you are trying to build a sustainable predictive maintenance program. But this information comes from different worlds. Therefore, because it covers multiple aspects of condition monitoring:

• It is necessary to understand the electrical behavior.
• Must understand spectral analysis for mechanical failures.
• And needs to process context data, as flow, pressure and operating condition.
• And to understand what changes and why it changes.

And this is exactly where generative AI really starts to make a difference.

Therefore, This article explains how generative AI fits into these tools that are already being used.

And not to replace them, but to help translate the evidence into something clearer and more consistent, so reliability teams can easily scale.

From automatic detection to expert reasoning

In fact, we are moving from automatic detection to something that is much closer to expert-level reasoning.

Available all the time, 24 hours per day, 7 days a week, when necessary. If we look at what maintenance and reliability teams are handling in daily operations today, the message is quite consistent:

We have more data than we've ever had. And we have less time to interpret them. And the equipment isn't getting simpler either. We have Frequency Variators, lots of equipment in remote locations and equipment that operates intermittently.

Signals do not remain clean and predictable, and everything becomes more dynamic. And so, What happens is that an insufficiency arises, that continues to grow. Like this, each one has more measurements, more trends, but alarms. But there's barely enough time for experts to figure out what it all really means..

And this is where the risk arises. Sometimes it's obvious when early warning can be missed and this turns into downtime.

But sometimes it's the opposite problem. One can end up wasting time on useless matters and is wasting time and resources on the wrong thing.. Therefore, This is why this topic is important now and not in five years.

The challenge

To frame the entire industry challenge in terms of pain, We have four practical pain points. After naming them, You can see that most people are dealing with the same thing:

• Excess data
• Analyst shortage
• Inconsistency
• Cost pressure

Excess data

Monitoring systems are doing exactly what they are asked to do. They are collecting more measurements, more trends, more spectra, more events. That is great, but the reality is that there is now too much information for a human to review in a disciplined way.

Nobody has time to sit there scrolling through hundreds of charts and dashboards every day. And even if you try, We always end up focusing on what is most recent and not necessarily on what is most important.

Generative AI and Predictive Maintenance Fig. 3 – Excess data

The shortage of specialists

Many are already going through this. People are retiring and moving into different roles, and replacing that skill set is not just hiring someone.

It takes years to train new people and build that intuition to know what is normal for a specific location or specific applications you are managing..

• And what a true progression of failures look like,
• And what does a process change look like?,
• And this experience is hard-earned.

Information inconsistency

This is underestimated because you can have two or more very competent engineers, analyzing the same evidence, and still disagree. One calls it misalignment and the other calls it imbalance or looseness.. Like this, one says monitor and the other says scheduled maintenance.

And sometimes both are reasonable. They are just applying different mental models. But for an organization, This inconsistency becomes a problem because it means that actions are unrepeatable and the results depend on who analyzed them, at that moment.

Cost pressure

Everyone is asked to do more with less: most active, more locations and more availability. But there are fewer resources, fewer experts and less time to investigate. Therefore, This pressure doesn't just affect budgets. It also affects behavior.

How we maintain the quality of expert judgment

Like this, teams either become overly conservative and follow all alarms, or they become desensitized and begin to ignore warnings. Therefore, when these four come together, The question is whether we can detect failures? But the real question then becomes how to maintain the quality of expert judgment and how to make it consistently available to the entire fleet we are managing.?

Evolution of condition monitoring

Condition monitoring has evolved in layers:

• The sensory inspection phase
• Portable tools and manual data collection
• In-line systems
• From automation to intelligence

Generative AI and Predictive Maintenance Fig. 4 –Evolution of condition monitoring

The sensory inspection phase

At the beginning, were mainly manual checks, rounds, temperature audit and basic inspections.

Very experience-driven, but also very limited. Therefore, you only see what you see and you only see it occasionally.

Portable tools and manual data collection

Then we moved on to portable tools. It was a huge advance because it was now possible to collect real measurements and compare them over time., but it will continue to be periodic. One captures one image, then come back a week later or a month later and take another image. This is still a very good and valid approach for semi or less critical assets where monitoring is not necessary 24 hours per day, 7 days a week.

Sistemas on-line

And after that, online systems come into play, what is needed for critical assets. With them you get continuous visibility and trends and alarms operate in context. You don't wait for the next route to discover something is wrong or something is going astray.

Machine learning and automation

The next layer was implemented by ARTESIS: machine learning and automation. The system helps detect patterns at scale, can keep up with changes faster, detect repetitions and follow trends in a group of assets without someone looking at charts all day.

From automation to intelligence

And now we're onto the next step, What it means to move from automation to intelligence. Because detection alone is not the end goal here. The true value is when the system can help answer maintenance and operation questions.:

• Something has changed, but what does that probably mean?
• Is this important or is it just an operating noise?
• What is the most likely cause and what should we do next?
• Monitor, to check, or we must plan maintenance or act immediately?

This is not just reporting that something has changed, but rather explain why it is important and translate that into actions that someone can implement.

AI Terminology

Before continuing the explanation, let's analyze the terminology, because AI has become many different things nowadays.

Like this, when referring to AI, refers to the broad umbrella, which are systems that can do things normally associated with human intelligence:

• Pattern recognition,
• Decision support,
• Sometimes, even a form of reasoning.

The objective is to tackle the repetitive and time-consuming part of the analysis, which is to repeatedly review the same type of fact, and transform it into decision support, so experts can dedicate their time to difficult decisions, extreme cases and high-risk decisions.

Another important point is that AI is not the same as Generative AI, and this is a very important distinction.

AI is not the same as Generative AI

A generative AI, or Gen-AI, it is currently a category under that umbrella. It is powerful and has to be used correctly. And it works best when it is based on real measurements and structured indicators, not just in floating text.

Here, when we talk about Gen-AI or Generative AI, This should be considered as a layer that helps translate evidence into clear explanations, but still needs solid measurements and diagnoses as a basis.

In condition monitoring, It’s important to remember that AI isn’t just one thing. It's really a set of layers that work together.

And remember, we are not talking about Gen-AI here, It is the AI ​​that is the broad umbrella.

Machine Learning (Machine Learning)

Therefore, na base, there is machine learning. This is the part that is excellent at recognizing patterns in data. And within that, there are two variants, that are used in ARTESIS systems:

• Supervised Learning
• The digital twin

Supervised Learning

Supervised learning is when you are training the system to detect known failure types to classify automatically when they reappear..

Unsupervised Learning, which is more about learning to identify what is normal. And for this to happen, takes place in the operational environment and then signals anomalies when it begins to deviate from the normal pattern.

The digital twin

After, there is a transition from digital to bits, by technology MCM and ARTHESIA, which adds another dimension. This is where physics and data-driven models come together. You don't just notice that something has changed, but there is a model of how the equipment should behave in certain conditions.

Generative AI and Predictive Maintenance Fig. 5 – The digital twin

This is the basis of our online solutions using electrical signature analysis.

Generative AI or Grand Language Model (Large Language Model -LLM)

Beyond learning and the digital twin, there is the closest layer, which is the generative AI layer, or LLM. This layer is different, because you can interpret the results produced by these systems, interconnect information and produce explanations that sound much closer to the reasoning of an expert, nomedamente:

• What does this probably mean,
• Why does this matter,
• What to do next.

What is important to note is that these are not competing methods. You don't choose one and throw the others away.

They are complementary, and the best results come when they work together. Therefore, o “Machine Learning”, which was the first layer, It's something that has been used in condition monitoring for a long time, our existing systems. It should be noted that it does several things extremely well.

• Can detect anomalies early;
• Can recognize repetitive patterns in signals;
• Can track failure progression over weeks or months;
• Can constantly compare current behavior to a learned signature or baseline.

And even when the equipment has a lot of operational variation, such as with Frequency Variators, load variations and fluctuations, etc. Like this, in practice, the existing system, based on machine learning, already automates a large part of the diagnostic workload.

E, Of course, for those familiar with ARTESIS systems, Much of the analysis is already automated. There is a classification of different faults into certain categories, so you know what to watch out for. This helps to organize information and highlight what is changing, pointing to the asset that deserves attention.

But there is still a limitation

But there is still a limitation, what you feel when you look at the ARTESIS system from a broader perspective in a critical way:

Good, I see a problem and I know it's a transmission problem or a bad base, and then?

“Machine Learning” is often very good at reporting what is happening; this appears abnormal and resembles a degrading bearing pattern and this is a tendency. But it doesn't always tell you why it's happening and what the likely mechanism behind it is and information about what the next recommended action is..

Gets the user to the right tests faster, but this cycle does not always close with a clear decision explanation and recommendation that someone can implement. It is often necessary to manually analyze all the data to arrive at something more accurate..

There is also the digital twin approach, which is the second layer, which is simple, because it deals with expectation versus reality.

Like this, the system builds a digital twin, that is, mathematical model of how an engine, or a bomb, or a fan, or whatever is under monitoring, should behave under normal conditions, and then continually compare that expected behavior to what you see in the real world.

Instead of just asking if something has really changed, is asking if the equipment is behaving as it should at this moment. When there is deviation, You can often detect it early, be something electrical, mechanical or even sometimes something caused by the process. And this is why digital twin is strong for early warning and understanding the direction of trends. And without a doubt the trend is the most important indicator in monitoring the condition, so you can identify drift before it becomes a major event and whether fault symptoms are stabilizing or worsening.

But even so, there is still a gap. A digital twin can report that a deviation has been detected and there is a problem. Without a doubt this is valuable information, but it is not always complete.

Even so, a human specialist is often needed

Oftentimes, It still takes a specialist to produce actionable information: here is the most likely mechanism, Here's what's happening and here's what we should do next. Therefore, even with Machine Learning and digital models that ARTESIS and some other technologies are using, there is still missing information, and most teams feel it when they try to make decisions.

Therefore, machine learning excels at detection and classification, but usually does not explain the root cause in a simple way.

It can also signal a pattern or classify a type of failure. Does not naturally tell the story of what originates it. It also does not automatically combine multiple signals that the electrical signature analysis-based solution generates into a single narrative., which means that, sometimes, will have mechanical problems with a bearing, but if this comes from the current passing through a bearing, which is caused by induced currents in a Frequency Variator, more comprehensive analysis of multiple data sources is needed to create a single narrative.

In real life, You don’t look at a trend in isolation. We look at multiples, such as analyzing the operating conditions, the spectrum, load changes and process indicators together, trying to link them into a single coherent assessment.

There is also the context, which is also very important. Industrial units are not a clean laboratory environment and therefore there are different modes of operation, different process requirements, different maintenance histories and different applications. De facto, the bombs, compressors, etc., they are all different and traditional models do not always adapt to all situations without additional adjustments.

E, finally, This does not eliminate the need for expert judgment, especially when it comes to hundreds or thousands of assets. Experts continue to be the bottleneck because someone has to interpret, prioritize and communicate what matters.

Generative AI and Predictive Maintenance Fig. 7 – Often a specialist is still needed

 

Why Generative AI makes such a big difference

This is why Generative AI makes such a difference. Because it can rest on these layers, of information that are the results of machine learning, digital twin deviations, everything created in context, and translate them into:

• Something closer to how an expert would explain,
• What does it probably mean,
• And why does it matter,
• And how confident we are,
• And what is the next best action.

Generative AI and Predictive Maintenance Fig. 8  – Impact of Generative AI

Therefore, on here, Generative AI is not just another algorithm assembled in the ARTESIS system. It is a different type of layer because it was built to understand, interpret and explain, and not just to detect.

The simplest way to explain this is to consider that you have a reliability technician or a diagnostic technician available 24 hours per day, 7 days a week. Not someone replacing the team of experts, but someone doing the repetitive part of the work, time consuming, that is, the part where you analyze the evidence and understand what is relevant.

Like this, instead of providing another graph or another alarm, Generative AI can review what other layers are already producing, for example Power Spectral Density spectra, learning-based trend assessments, breakdown indicators and translate all this information into actionable assessments and recommendations.

It can also help with questions in the next step, which is where teams often struggle:

• This is likely, real or just operating noise,
• And what is the most likely cause of this problem?,
• And what should we do next?

The big advantage here is the scale, because human experts can do all this very well, but they can't do it for thousands of assets, in multiple locations, every day, without becoming the stranglehold.

The main advantage of Generative AI is that it can provide the first interpretation, as if you were an expert, instantly and consistently across the entire asset set, and so experts dedicate their time to what really matters:

• The difficult cases,
• High-risk problems,
• All actions that prevent downtime.

What the Great Language Models bring

But this is where Great Language Models bring something genuinely different by presenting some pretty unique advantages..

First, understand natural language, that seems obvious, but it is very important. Like this, This means that systems can communicate using the tools that the technical team already works with: e-mails, maintenance records and not just the graphs.

And second, can reason taking into account multiple data sources. In real investigations, you are never just looking at a sign. De facto, what we do is combine all the trends, spectrum data, operating states, electrical values, process values ​​and perhaps some maintenance history, trying to construct a consistent description of the situation.

And the Great Language Models are designed for this. They are also good at identifying connections and contradictions. For example, if an indicator suggests a mechanical problem, but the operational context suggests a change in the process, can evidence this and confidently push the interpretation to the correct cause.

Another great strength is that they can summarize information over long periods of time and tell, or compress, this information in:

• This is what changed,
• When it changed,
• And how quickly it progressed.

Obviously you can also do this manually, if you know what you're looking at. But, as mentioned, this becomes automated by Generative AI and becomes easily scalable.

Generative AI and Predictive Maintenance Fig. 9  – Os Large Language Models

Besides that, can produce explanations in a way that any technician understands, and not just a person with specialist training.

This is important, because for untrained eyes so one can simply read and understand in simple language what is wrong with the asset.

When you put all this together, We can see that condition monitoring is really being transformed from a simple automation, to something closer to a level of intelligence, that is:

• We detected something,
• And we understand the context,
• And what is the likely cause of this?,
• And what should we do next.

How the Great Language Models work at Artesis

This is how all this comes together and fits into ARTESIS' electrical signature analysis solutions.

• ARTESIS systems, O E-MCM, or the AMTPro carry out and process measures, which are electrical signature analyzes which means that the system collects voltage and current, and from the engine electrical panel and no additional sensors are required.
• After, with the help of machine learning and digital layers interprets the raw signal, which is a high frequency sample waveform data converting it into fault patterns, progression rates, confidence scores and, basically, evidence or structured indicators.
• These indicators were then fed into the generative AI layer.

Generative AI and Predictive Maintenance Fig. 10  – What are Large Language Models based on?

Great Language Models are not trying to interpret raw waveforms from scratch. They are reading the condition indicators that the ARTESIS system already generates, doing what a good reliability technician would do, which is to interpret all these indicators by integrating them into a coherent scenario:

• What do they mean?
• And using the correct context, operating mode, load changes, recent events and, again, electromechanical parameters into coherent information.

Therefore, once again, the ARTESIS system is not replacing any of the previous layers that were already used, namely Machine Learning and Digital Twin. Rely on these layers to create easier-to-understand results and reports, simpler, that are legible, repeatable and scalable across assets.

So we have, the role of Great Language Models, MCM and ARTESIS system numbers, bridges the gap between data and the action decision to be developed. De facto, standards alone are not capable of recommending action.

This is what a Great Language Model is doing. Take all these patterns and turn them into explanation. Does not just repeat an alarm, and explains the logic behind it in a way that makes sense to the person receiving that information.

And the result here is really the scale. Expands the ability of a single expert technician to cover many more resources, hundreds, up to thousands, without sacrificing clarity.

Experts continue to make final decisions, but are no longer stuck repeating endless repetitive basic diagnostic tasks.

Examples of system results

The following are the types of results the system can produce virtually instantly.

• You can tell that misalignment patterns have been increasing over the last three weeks. Not just the alarm, but what is changing and in what time frame.
• Pump efficiency decreased by approx. 12% in most systems with hydraulic restrictions. Therefore, links a performance indicator to a likely mechanism in plain language.
• There are rotor problems. Seems stable, but I continue to monitor. No immediate action required.
• Identified energy waste. The engine appears to be underloaded, which means there is an opportunity for optimization.

E, Of course, All this information is also available on some panels, graphs and numbers.

Now you can automate this first expert intervention consistently

This means that you can now automate this first analysis, specialized interpretation, consistently, quickly and across assets. So, If all this is linked to the problems mentioned before, Generative AI aligns directly with the sector's biggest challenges in a very clear way.

Data Overload

When you're dealing with too much data, the answer isn't to look at more dashboards., but rather have a fully automated interpretation. The system reads the evidence and tells you what really matters. And when there is a lack of experts, Generative AI acts as specialized and integrated support. This is not replacing the experts, but rather capture the explanation and reasoning that normally stays in a person's head, making this guidance available to the entire team.

Fill skills gaps

When diagnoses are slow, the value is in the speed. You also get real-time information, rather than waiting for someone to have time to review and document what they are observing.

Inconsistent Reporting

And when reports are inconsistent, the Generative AI helps to spon. Like this, you receive reliable summaries in a consistent format, with the same terminology, structure and logic. Actions do not depend on who was on the shift, or what happened during it.

Speeds up decision making

And when scalability is the problem, generative AI removes this bottleneck. Makes it possible for a small team to monitor the entire fleet, not just the few active ones who had time to check in the week.

And this is how reliability teams multiply their resources, maintaining the quality of its opinions at expert level, but distributing it across more assets, places and people.

What can you ask

In the following demonstration,, what you can see is the chatbot-like interactive menu, which will be available at 2026 in the online system and- MCM.

• What were the changes this week??
• Which assets need attention?
• Explain this alarm
• The fault is evolving?
• Where are the efficiency losses?

As you can see, you can ask questions, obtain answers and interact with the system, providing more information to obtain more accurate data, such as bearing number or equipment type. If the specific type of transmission has not been mentioned, you can provide this information to obtain more precise guidance.

Standalone Reliability Assistant

Reviewing the above you can see where the system is going, and which consists of becoming an assistant that is essentially always active and that can monitor all assets continuously and diagnose problems quickly.

The features are as follows:

• Permanent monitoring
• Instant diagnosis
• Root cause explanation
• Context-aware recommendations
• Scalable across different installations

Most important, Can you explain what is happening, the probable causes, what evidence supports this and what to do next.

Application example 1 – Compressor

Example 2 Motor-driven pump with frequency inverter

Example 3 Motor with belt transmission

What does this mean for industrial units

Less unavailability

There are fewer surprises because problems are detected earlier and, therefore, there is less downtime and you are not caught by surprise.

Faster decisions

This means much faster decisions because analyzing a report only takes 40 seconds.

Greater productivity of specialists

But, even more important, Expert diagnostics become available on all machines, not only for the critical asset in question, which is reviewed by the most experienced people, but all assets receive the same treatment. Like this, you get a clear and consistent explanation you know what you should do and what kind of deadline you are considering.

Expert diagnostics of all assets

Therefore, and this is really, he knows, what AI gives you, this scalability efficiently or consistently across all assets being monitored, whether with ARTESIS portable or online systems.

Detection and diagnosis of the widest range of faults based on electrical signature analysis

The advantage of electrical signature analysis is that it covers practically all types of faults that one would expect to find in a rotating asset.. The entire power circuit is being monitored, power quality of transformers, frequency inverters and then the whole range of mechanical failures such as compressor problems, pumps or fans and also on the efficiency of the pumps.

Generative AI and Predictive Maintenance Fig. 11