Several years ago, I published an article titled, “How to Get Started with Onsite Oil Analysis: A Step-by-Step Guide”. In that article, I outline 7 steps to developing a successful onsite program:
- Developing an equipment criticality profile
- Determining sampling frequency
- Developing a sample test slate and alarms
- Designing a lab
- Designating a lubricant storage space (and keeping it tidy and clean)
- Training
- Software
Let’s build on that article and review some key advancements in the industry that are highlighted in Spectro Scientific’s TruVu 360TM software and the new AI-enabled oil health forecasting tool, TruVu 360TM Fluid IQ. We will look at how the new features in the software can help with defining equipment criticality and risk, sampling frequency, and oil analysis alarms.
Equipment Criticality
Defining equipment criticality remains the first step in getting started with managing a program onsite. There are several standard methods the end user can use to evaluate criticality, including ASTM 7874, Standard Guide for Applying Failure Mode and Effect Analysis (FMEA) to In-Service Lubricant Testing, and ASTM D6224, Standard Practice for In-Service Monitoring of Lubricating Oil for Auxiliary Power Plant Equipment.
Once criticality is determined, reliability engineers need to use that information and incorporate it into the overall workflow of the oil condition monitoring program. That means prioritizing maintenance checks, sampling, and testing based on the risk profile.
In TruVu 360TM, the equipment risk profile is defined using concepts from ISO 13381-2025. Users can assign these risk levels within the TruVu 360TM software, with TruVu 360TM Fluid IQ enabled, to each component. Risk and recommendations from the software go hand in hand. The greater the risk associated with the component, the more conservative the recommendations for sampling and oil changes. An important concept to define early and evaluate often to ensure the program continues to meet the organization’s goals.
Sampling Frequency
Determining the sampling frequency for critical components is often the most complex part of setting up a program. Reliability engineers can find themselves oversampling, but more commonly, not enough. Relying on industry documentation and OEM’s has been the norm, but new advances in artificial intelligence have enabled smarter sampling strategies.
TruVu 360TM Fluid IQ users now have the opportunity to implement smart sampling into their maintenance strategies. Building on the idea of risk evaluation, sampling recommendations are made using sampling history and historical trends, and comparing to a broad database of like-components, operating history, and sample history to forecast when the next sample needs to be taken. This strategy enables optimized sampling aligned with the condition rather than fixed schedules.
Alarms
Setting proper alarm levels is also a challenging part of managing an onsite program. Again, OEM recommendations, ASTM standards, and reference materials are available to help.
Users may find the ideas outlined in ASTM D7720 helpful (ASTM D7720: Standard Guide for Statistically Evaluating Measurand Alarm Limits when Using Oil Analysis to Monitor Equipment and Oil for Fitness and Contamination). ASTM D7720 outlines condition-based alarm concepts, helping users adjust alarm levels based on the component’s condition.
This methodology is particularly helpful for users who may have a large number of severe alarms to manage (and reduce) but simply can’t address everything at once. The statistical models referenced in D7720 enable alarm adjustments based on historical data to effectively identify extremely elevated alarms and address maintenance concerns promptly.
By employing this approach as a systematic process, prioritizing and resolving the most significant alarms first, then reassessing after each stage, it is possible to incrementally return all alarms to their normal status. When utilizing this technique, it is important to use the equipment criticality profile, which is directly correlated with safety, and ensure that adjusting alarms to support condition-based alarming, as outlined in ASTM D7720, is appropriate.
This condition-based alarming (ASTM D7720) concept is implemented in TruVu 360TM and can be easily applied if sufficient oil sample history is available for the component.
Software
There is software for every function of life, personal or work. It can be overwhelming at times. The key is to purchase software that offers the most options for quickly and effectively implementing an oil condition monitoring program onsite. Spectro Scientific’s MiniLab with TruVu 360TM solution is a valuable tool for a complete, all-in-one oil analysis solution onsite.
With the new AI-enabled forecasting tool within TruVu 360TM users can:
- Evaluate and record criticality of components using risk profiles
- Utilize AI to optimize sampling frequency
- Create condition-based alarms using ASTM D7720
- Utilize AI to predict the remaining useful life of the oil
- Understand limiting properties and catch issues early (even when the oil condition is normal)
Conclusion
It’s exciting to be part of the industry’s integration of AI techniques into onsite lab workflows. While the core principles of condition monitoring programs remain relevant, they are adapting to include advancements in AI. If you would like more information about onsite oil analysis solutions, please reach out to me.
