Artificial intelligence (AI) has expanded to various fields, and the industrial area is no exception.

In maintenance, AI has begun to automate processes and can improve the early detection of problems that cause unavailability in critical machinery, especially in energy generation and gas transportation.

Applications of AI in maintenance include:

• Risk assessment
• Early diagnosis
• Forecasting the consequences of failure
• Recommending treatments based on previous experiences

On the other hand, machine learning or ML, a subset of AI that allows computers to learn from data, has been effective in predicting other outcomes in various areas. For example, AI and ML have demonstrated greater accuracy and agility in predicting outcomes in the medical area than doctors. The early detection of cancer is a controversial example.

In the industrial area, these technologies also have the potential to improve diagnosis and the quality of recommendations or detection of potential future scenarios.

Although the experiences of these next-generation tools are little known and very few have been published, mainly due to the halo of secrecy surrounding innovative services, which protects them from their competitors, this article analyzes the application of AI and ML algorithms in the prediction of turbomachinery oil degradation, including its current applications, limitations, and future perspectives.

Oil Degradation: A Race Against Time

Premature oil degradation is among the three main causes of unplanned shutdowns and the unavailability of turbines and compressors. Although advances have been made in the design, diagnosis, and treatment of this type of oil, early identification of problems remains a major challenge.

Degradation, which in some cases can lead to the formation of varnish or lacquer, has been characterized by a high incidence, especially because of the introduction of base groups, the increase in operating temperatures, and the reduction in the size of lubrication systems in turbomachinery.

According to a study carried out on a database in 68 combined cycles¹, 68% of gas turbines have suffered lubricant degradation problems at some point, while 84% of the oils in electrohydraulic systems have had degradation problems.

On the other hand, depending on the source consulted, the consumption of cleaning agents has had an unusual and little expected rebound in the last five years.

Consumption of lubrication system cleaning agents has increased significantly in the last five years.

These results indirectly show that lubricants in the power generation sector are suffering unusual degradation, either because of cycling due to instability in demand or because the lubrication process in new machines is more aggressive on the oil, or that the marketing of this type of cleaning agent has influenced the purchase and applicability to the end customer.

Despite this, lubricant degradation is nothing new and part of the life cycle. Laboratory analyses aim to detect the physicochemical changes that occur in the lubricant, and an adequate condition-based maintenance program will take these changes into account to plan timely and appropriate maintenance actions.

Natural, Artificial Learning, and Machine Learning

Human beings have been learning in specific ways for thousands of years:

1. It has taken us thousands of years to learn particular skills.
2. What is acquired through experience. It is what each one cultivates, defines, and controls.
3. What is transmitted through culture, which is also acquired social knowledge.

Until recently, these were the only sources of learning, but with the emergence of AI, we now have another additional source of knowledge generation.

This new source is not human and, in some cases, can manage itself. Computers (or microchips) can discover knowledge through data.

One of the main characteristics of knowledge is the speed at which it is transmitted; Knowledge by evolution requires or has required much more time than knowledge by experience. At the same time, it needs more time than it is transmitted.

From this point of view, it is customary to assume that what is going to happen or is already happening in some fields is that the knowledge generated by computers will surpass the different types of natural knowledge in both speed and quantity.

In many cases, this knowledge will be a synergy between the system that constantly generates and analyzes knowledge and the human being, with characteristics that the intelligent system does not yet have.

On the other hand, algorithms are nothing new and have been with us since the beginning of mathematics applied to daily life. Algorithms, temporarily, must be developed for specific fields. A particular algorithm is needed for cancer detection, a specific one to play chess, etc.

For now, and only for now, there is no algorithm capable of being applied to various fields, making it a master algorithm. This has yet to be achieved, and that even allows human beings to be unique since we can analyze different topics simultaneously.

Something like what was described above applies to the detection of anomalies in the field of lubricant degradation, where the personal experience of the data analyst plays an important role that is usually accompanied by the knowledge transmitted by someone with greater experience, knowledge, and skills.

Additionally, an analytical sequence can be supported by a computer tool capable of, at least, storing information, either from cases or situations with which it shares some point in common.

But what is the basis on which a system can be built that, in our case, allows for the early identification of physicochemical changes that lead to potential lubricant failures?

It is probably one or a combination of some of the leading ML schools on which an application can be built and on which new applications are already being developed in other areas:

1. Learning by imitating the human brain. Inspired by neuroscience, we try to emulate the brain’s circuits, the function of neurons, and the creation of new synapses. For this, work is done by giving artificial characteristics to specific algorithms, making them work like a human brain. An applied example of this type of AI is speech recognition.
2. Imitate evolution. Where the algorithm is aimed at evolving, thus improving more and more. This type of ML has been mainly applied to improving the design of electronic components.
3. Another branch of ML is vision learning. In this branch, you start with many hypotheses, each with a degree of uncertainty. As the visualization improves, the uncertainty of each hypothesis changes until one that has a high degree of similarity is found.
4. Symbolic learning, with an approach like that of a mathematician doing data induction. Where you have a data set, hypotheses are formulated to explain those data; the hypotheses are tested with a set of data, and based on the results, the hypotheses are redefined until an adequate degree of acceptance is reached. This is how the scientific method works, but the big difference is that an entire intelligent system does this. An example of this type of ML was the discovery in 2021 of a new strain of malaria.
5. The last branch of ML is inspired, above all, by psychology and analogy. Where previously stored information is recovered and compared with the case study until analogous or similar points are found that will allow possible deviations to be identified in advance.

AI Turbine Oil Diagnosis Beta Version

This article describes the first step in using generative AI under a trained LLM model.

One advantage of applying AI is that a lot of time can be saved in the testing phase. The typical real test benches, oxidation in this case, can be replaced by their virtual peers, which generate a massive amount of data in less than 2% of the time required to do it traditionally. This beta version uses precise virtual models based on known databases and case studies.

The first step is to upload the turbine operation data and the oil analysis results. This information is sent for contrast and comparison with the database, in which the failure marker(s) that the lubricant presents is analyzed. An analogy of this process is the identification of early biomarkers of potentially cancerous cells.

Then, the results are presented based on two parameters.

• The first indicates the probability that the oil shows signs of chemical changes that can subsequently be transformed into insoluble matter, commonly identified as varnishes.
• The second parameter indicates the failure rate of oils with similar characteristics and conditions that showed degradation problems at a later stage.
• A third part, still embryonic, is oriented towards maintenance actions or solutions if the previous two show signs of failure.

The following case shows the result of a turbine oil sample showing no degradation signs.

• TAN: slight increase of 14%
• MPC: 8
• The rest of the parameters without changes

Laboratory diagnosis & comments:

Sample without significant changes.

Flagged fields do not require urgent maintenance action.

Observe the trend of the oil condition and the equipment degradation.

The particle count is at a slight level of attention; check the filtration system.

Pull another sample at the set frequency.

AI Diagnosis & Comments

• Oil Failure Probability: 37%
• Oil Failure Rate: 69%
• Recommended Action: Chemical filtration

Some Initial Steps and First Conclusions

From the approach of the different ML schools, it is logical to think that some have more application to the physicochemical area. In a couple of tests carried out on a set of data, the ones that have the best results, at least at first, are analogous methods and symbolic learning.

The analytical process generates an initial basic model from which the losses of the fluid characteristics are measured, and a degree of uncertainty is generated where it can go.

In this model, comparisons can be made with hundreds of models or with those that are available. Although this analysis can be carried out by a symbiotic human-machine system where the human part can direct where the analysis should focus, remember that humans can analyze various topics simultaneously that do not necessarily have something in common.

However, the synthetic part will be in charge of carrying out the large volume of calculations and will define the possible scenario with a confidence interval. This process is similar to the statistical method of error propagation.

On the other hand, the increasingly advanced systems for detecting particles in fluids can apply calculations not only to determine the type of particle but also to correlate various hypotheses about a potential failure until defining or discarding those that do not fit your preferred model.

All this sounds good and can be achieved tomorrow, but it is not that simple. One of the significant barriers is the transmission of human knowledge to its synthetic counterpart.

It is more challenging than writing a line of code and getting it to produce the expected results or sitting specialized programmers in a room to generate a quasi-perfect problem identification model.

Fortunately for many, what makes us unique, at least for now, is that we can identify problems based on analogies and comparisons and that the synapses that run through our neuronal system are challenging to imitate.

I have been fortunate enough to see the first steps of the application of ML in the field of fluid analysis, and without a doubt, we are at the dawn of a new dawn.

It is predictable that it will take time, but each step that is being taken brings us closer to a substantial improvement aimed at improving production, reducing machinery unavailability, and improving lubricant conditions.

References

1. Turbine Oil Analysis Data Analytics, Jorge Alarcon

This presentation was presented at the Lubrication Reliability Virtual Summit on September 20, 2023 by Blair Fraser, with UE Systems.  Discover how UE Systems cutting-edge technologies are revolutionizing maintenance practices, enhancing equipment performance and operational efficiency.

Blair explores the latest advancements in wireless ultrasound, vibration, and temperature sensors, specifically focusing on bearing monitoring and lubrication. He uncovers the power of advanced sensors and data analytics that enable real-time monitoring and condition-based lubrication.

Data, data, and more data. Our industry feeds on data to make decisions, and there’s an abundance of condition monitoring tools that allow us to use data to understand the health of operating equipment and detect potential risks of failures.

Utilizing core equipment monitoring data points like diagnostics, ultrasound, vibration analysis, thermography, fluid analysis, etc., allows us to manage our equipment maintenance, monitor conditions, reduce unexpected downtime and increase reliability.

With these condition monitoring tools available today, it can be overwhelming to interpret your data to make important, informed maintenance decisions.

Managing, interpreting, and reporting within these tools as separate entities creates silos, meaning dedicated time resources spent in each system (resulting in waste) and the inability to see maintenance as a whole. In doing so, we sometimes lose the ‘big picture’ and a thorough understanding of how these data sets complement each other.

Accurate Data-Based Decisions

How can you start filtering out the ‘noise’ and only focus on the data that matters for your maintenance decisions? It begins with streamlining data from condition monitoring sources into a single platform for complete visibility into asset health.

POLARIS Laboratories^® has identified this opportunity and has designed a solution to connect fluid analysis data with maintenance management systems, Enterprise Resource Planning (ERP), Computerized Maintenance Management Systems (CMMS), reliability service providers, and OEMs.

A secure API (Application Programming Interface) allows fluid analysis data to pass from the data management system to the connected maintenance system in real time. This integration, we call DataConnect, creates visibility within a single platform – eliminating the silos of managing data within multiple systems.

In addition to seeing analysis results within your maintenance system, integration capabilities include functionality to submit sample information electronically, manage equipment and component information and view sample results within the maintenance system for complete fluid analysis integration.

The ability to receive instant high-severity alerts can trigger work orders based on the flagging severity and recommended actions within a fluid analysis sample report.

Future Possibilities with Integration

So, where is the future of maintenance integration going? Think about all of the possibilities.

Your fluid analysis results, combined with telematics fault codes, vibration analysis, and operator walk-around (inspection) reports, can give you a true understanding of the health of your equipment.

For example, if your engine oil analysis results indicate fuel dilution and your telematics fault codes indicate an issue with a fuel injector, these two combined data sources assist you in understanding what is happening and why (Cause and Effect).

This integration of data can significantly reduce troubleshooting and repair times. Another example is a gearbox with fluid analysis results showing wear – combining this data with abnormal vibration analysis results can validate a problem, remove concerns regarding ‘false positives’ in your data, and help pinpoint where the problem is and how urgently maintenance action is needed.

With the limited availability and capacity of internal IT resources, our team at POLARIS Laboratories^® has built custom integrations with several industry CMMS, reliability providers, and OEM systems. API integrations have been built with more than 15 maintenance management providers, making connecting your maintenance for optimal reliability seamless.

As our industry moves from reactive to predictive maintenance, data integration is the first step in connecting maintenance to make informed decisions, monitor asset health, and increase equipment reliability.

Oil analysis on oil reservoirs, such as a paper machine, provides much more value than most maintenance managers realize.

Regular reservoir oil sampling can provide the following:

• Reactive information, such as a spike in wear metals identifying a failed bearing or another component.
• Predictive information, such as a slight increase in wear metals indicating early stages of an impending failure.
• Or, most valuable of all, it can provide proactive information. Oil analysis can tell you when your additives are getting depleted, contamination levels are getting to a point where additional filtering may be needed, moisture levels are to the point where corrective measures are required to prevent damage, and with enough data, may even tell you if your equipment is operating outside of its intended design (too hot, too slow, cavitation).

It’s all about using facts and data to help us make better maintenance decisions and get the most out of our investments. Improving the life expectancy of equipment, extending the life of oil life, and reducing unplanned downtime (which also improves safety) are just a few reasons the value of oil analysis far outweighs the cost.

So, would continuously monitoring the oil add more value if oil analysis provides that much value? We recently decided to add a continuous oil monitoring system to the main lube on our paper machine lubricating system.

We then took that information and sent it to our historian to be trended 24/7. We are trending the oil temperature, the moisture level, and the ISO 4/6/14 particle counts.

When we started this project, we did not realize how fast it would pay for itself! Shortly after getting the system online (but before trending), the maintenance manager notified us that the paper machine dryer head had developed a steam leak.

We watched the oil condition and saw no change. A few days later, the moisture levels began to climb rapidly. Thanks to the monitoring system, we reacted to this issue by periodically draining some oil and hooking up a vacuum dehydrator.

Within 24 hours, we reduced the moisture to an acceptable level and returned to normal within 72 hours. No doubt, this quick response minimized the amount of damage the water in our system caused.

Nicholas Knott, a colleague at another paper mill, had the same experience while trending moisture levels in his paper machine. He also caught it quickly and believes this technology prevented many bearing failures. Below is the trend showing how quickly the moisture reaches unacceptable levels and how fast his team could resolve it.

The lesson from these unfortunate situations is that if we were not doing continuous monitoring, how much damage would’ve occurred? We will never know, but we know that steam leaks on a dryer head were common in the past, and oil analysis was rare.

We also know that we had to replace many bearings in the past, which is rare now. Using continuous oil monitoring systems may only be necessary for some situations. Still, in a challenging application like a paper machine, it adds way more value than it costs.

Soon we expect to have enough data to understand if situations like start-ups, shutdowns, wash-ups, and machine speeds have impacted our oil quality.