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For organizations utilizing online sensors, oil analysis, vibration analysis, thermography, ultrasound, or other predictive maintenance technologies - how is condition monitoring data operationalized after collection?

Specifically, I am interested in understanding:
• Who owns the review and diagnostic interpretation of incoming data?
• How are alarms/exceptions prioritized for action?
• Is the information integrated into your CMMS/EAM for work order generation?
• Where do you experience the greatest bottleneck: data overload, lack of analyst resources, delayed maintenance response, or limited business visibility?

Many organizations have become highly effective at collecting reliability data, but the true value seems to depend on how efficiently that information is converted into planned maintenance action and measurable asset improvement.

I would value hearing how others are successfully closing that loop.

Miguel,

Thank you for the detailed response. This is very helpful and exactly the kind of operational insight I was hoping to better understand.

I appreciate you taking the time to explain your workflow and how the monitoring data is translated into maintenance action.

Best regards,
Linda Perry

Hi Miguel,

That's really interesting.  It is a very clear picture of how the flow works in practice, especially the way monitoring, SAP, prioritization, and manpower availability all interact.

What stands out to me most is the bottleneck you mentioned, where stoppage work starts crowding out the monitored work and the backlog builds up. That feels like the real operational pressure point.

Out of curiosity, when that happens, what usually makes the biggest difference in keeping the monitored work from slipping too far?

In our setup, condition monitoring data (oil, vibration, thermography) is reviewed by the engineering team, Alarms are prioritized based on criticality and risk to production/safety, then routed into the connected worker tool which is integrated with SAP for automatic work order generation.

Hello Linda,

Miguel gave you a great top-to-bottom example of most of the organizations out there. I will try to answer from a more standard perspective, also considering my own experience when I was an RE (Reliability Engineer).

1. I've been visiting customers very often, and AI + Modern IoT are getting more popular. They are capable of providing high-fidelity insights for 90% of the trends that can be captured by Vib-Temp-UT-Oil Analysis. Even though MCA (Motor Circuit Analysis) and other electrical diagnostics are developing fast, and there are a couple of great tools out there, I really like Howard's solution with MotorDoc (<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>https://www.linkedin.com/in/motordoc/<response-element class="" ng-version="0.0.0-PLACEHOLDER"></response-element>). At the end of the day, you see a PdM (Predictive Maintenance) technician, a Maintenance Supervisor, or a Planner receiving those insights and taking the first steps to generate a WO (Work Order), investigate further, or reject it.

2. If the organization does not have a clear Asset Criticality analysis, the chance of wasting labor hours is high. I've seen it and lived it... Basics first, PdM later.

3. Yes. I worked for SLB as an RE over 10 years ago, and it was already integrated. Nowadays, you should get your PdM solution integrated if you are doing Continuous Monitoring; otherwise, it will be forgotten. Now, if you do it as a separate task-for example, collecting oil samples monthly-then this data is input into the system as an inspection. I would suggest getting this report integrated into your CMMS/EAM/ERP somehow. There are lots of options on the market for that. Lately, Tractian launched a tool called Analysis, where you can just receive the report via email, and it will be interpreted by an AI model that gives you actionable insights.

4. It is a mix of everything you mentioned. But if the basics are in place-i.e., Asset Criticality + Planning and Scheduling + Precision Maintenance-then you are limited only by technology quality and precision. The catch here is: are you using the correct technology for the failure mode you are trying to mitigate? One thing you did not mention was validation. How do I know if the vibration trend that generated the insight related to bearing wear was really the root cause? Connecting early signs with the root cause is the key here. If the action you took is based on the technology's recommendation, you should see an improvement in asset health, sure. But if that failure happens over and over, we still need REs to investigate the root cause of those cases.
   
   

Thank you for the questions Linda. Do you mind sharing what exactly you are looking for with it?