Underutilized Operational Data

[jeem]

What operational data is most underutilized today for predictive reliability and integrity management in petrochemical plants?
And what are the measures to make sure that the data is taken, used, and analyzed to switch reliability & integrity towards predictive mode?

[sys1]

Just to start supporting your idea, petrochemical plants are sitting on massive amounts of operational data… but only a small fraction is actually converted into actionable predictive reliability or integrity intelligence.
Most sites still operate in a “reactive + preventive” mode:
Alarm happens → react
Equipment fails → investigate
Thickness drops below limit → repair
Vibration high → inspect
The real opportunity is identifying weak degradation signals months or years before functional failure.
I am not sure that is happening.

Now looking for such operational data;
- IOW deviations pickup
- Transient & upset data (leading indicator)
- Control valve behavior data
- Low level vibration
- Exchangers thermal performance trending
- PSV /flare system activation data
- Unexpected corrosion rates
- Operator log books & shift notes (early indications)
- Maintenance deferral analysis
- Temporary repair data analysis

Plants in todays time too struggle to be having a proactive approach towards issues.

[jeem]

IOW deviations pickup is required to be utilized by interfaces like inspection, integrity, and maintenance.
Similarly, for other cases like control valve behavior, low level vibration are maintenance related items.
So it is the responsibility of the interfaces to make sure all this data is correctly utilized.

[ww2i]

Some very meaningful set of data here.
I was thinking about operator log books & shift notes. Yes, early indications can be picked up from there.
PSV activation, I believe is one controlled, and measured set of data which at least at our place is very well reported, and analyzed.
Low level vibration, someone reports that?

Just to start supporting your idea, petrochemical plants are sitting on massive amounts of operational data… but only a small fraction is actually converted into actionable predictive reliability or integrity intelligence.
Most sites still operate in a “reactive + preventive” mode:
Alarm happens → react
Equipment fails → investigate
Thickness drops below limit → repair
Vibration high → inspect
The real opportunity is identifying weak degradation signals months or years before functional failure.
I am not sure that is happening.

Now looking for such operational data;
- IOW deviations pickup
- Transient & upset data (leading indicator)
- Control valve behavior data
- Low level vibration
- Exchangers thermal performance trending
- PSV /flare system activation data
- Unexpected corrosion rates
- Operator log books & shift notes (early indications)
- Maintenance deferral analysis
- Temporary repair data analysis

Plants in todays time too struggle to be having a proactive approach towards issues.

[ivani1]

Plants often ignore intermittent alarms, occasional vibration, rare excursions, nuisance trips, minor leaks.
But major failures usually begin as weak recurring signals.
Predictive organizations investigate weak signals early.

[tuan]

I would say yes, pretty closely the operational data is required to be fully utilized by all interfaces. Operating data is not just for Operations. Maintenance too even Technical personnel at site should be considered as operating the plant and should not miss on any indicative data which can be picked up to detect an early abnormality.
Discovering detectors providing erratic detections may be picked up by Operations is required to be attended by Maintenance but still be assessed by Process Safety under Technical role till that is fully functional.

IOW deviations pickup is required to be utilized by interfaces like inspection, integrity, and maintenance.
Similarly, for other cases like control valve behavior, low level vibration are maintenance related items.
So it is the responsibility of the interfaces to make sure all this data is correctly utilized.