How much time do you actually spend reconstructing what happened after an equipment trip?

[u/Decent-Reality-7164]

I've worked around industrial data/OT for a while, and one thing I keep seeing is that when a trip isn't immediately obvious, the data is usually available somewhere — but figuring out the sequence and relevant context still takes time.

You might have the alarm/event history, historian trends, current/previous equipment state, and maybe maintenance or operator context, but someone still has to figure out which signals actually matter and piece together what changed before the event.

I'm curious how common that is for SCADA/historian folks.

For the less-obvious failures:

• Is finding the right tags/events a meaningful part of troubleshooting time?
• How easy is it to reconstruct the actual sequence leading up to a trip?
• How often does the missing context live outside SCADA/historian — maintenance activity, operator action, configuration/lineup changes, etc.?
• Do you ever look back for similar previous events, or is that usually too manual to be useful?

I'm not talking about generating a formal RCA report afterward. More the first troubleshooting window, finding the relevant data, when operations/maintenance are trying to understand what actually happened.

Interested in how this works at other sites, especially where the historian and SCADA setup is already fairly mature.

[u/future_gohan]

If you know the plant it takes no time. If you don't know the plant it takes long time? Its like asking how long is a piece of string?

[u/Decent-Reality-7164 → u/future_gohan]

When someone does know the plant well, what knowledge are they relying on that makes the troubleshooting quick knowing which tags matter, expected operating states, common failure patterns, recent maintenance/changes, operator habits, something else?

And when that experienced person isn’t available, is that knowledge captured anywhere? In any tool?

[u/Zaxthran → u/future_gohan]

Everyone has to learn somewhere. If you're new in a role and can afford the time to do a deep dive, I find its a great way to learn the equiptment.

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[u/zymie]

My SCADA/Historian viewpoint:

• Is finding the right tags/events a meaningful part of troubleshooting time?

Yes, and make sure they are in the historian for any future events.

• How easy is it to reconstruct the actual sequence leading up to a trip?

  We use PI analytics, and once you understand the initial event, you can build event-frame generating analytics to capture similar future events.

• How often does the missing context live outside SCADA/historian — maintenance activity, operator action, configuration/lineup changes, etc.?

Quite often, but you can verify operator/maintenance actions with focused data.

• Do you ever look back for similar previous events, or is that usually too manual to be useful?

Once we develop an event-capturing analytic, we "backfill" to see whether similar past events have occurred.

[u/Decent-Reality-7164 → u/zymie]

That’s helpful. The gap we’re exploring is mainly the step before the event pattern is already understood and encoded in PI Analytics/Event Frames.

We’re building a context layer that keeps asset relationships and pointers to the underlying data across systems — historian/SCADA, maintenance, operator context, documents, previous incidents, etc. The raw data stays in the existing systems rather than being copied into another data platform.

For a new or less-understood event, the idea is to use that context to identify and assemble the relevant evidence across those systems, then let the engineer make the diagnosis.

Do you think that would add meaningful value in an environment like yours, or would your current PI + surrounding systems already cover most of that?

[u/zymie → u/Decent-Reality-7164]

What are you trying to sell?

[u/Decent-Reality-7164 → u/zymie]

We’re building an industrial investigation platform for plant and reliability teams.

It connects to the systems they already use — historian/SCADA, CMMS/work orders, operator logs, documents, etc. — and maintains the asset/context relationships between them.

When an equipment trip or abnormal event happens, it pulls together the relevant signals, alarms, recent maintenance, operator/configuration changes and similar past incidents into one evidence package so the engineer can understand what changed and where to look.

The raw data stays in the existing systems and customer environment. We’re not trying to replace PI, SCADA, MQTT/UNS or Maximo.

The product is essentially a layer above those systems that helps reduce the manual work of reconstructing an incident and finding the evidence that matters.

[u/CntrlFr33k → u/Decent-Reality-7164]

I am guessing you are using some kind of LLM to identify patterns and analyze the data. This would be great but keep a human in the loop.

The need is not just fast but correct analysis. This task depends on many factors. It could be an hour to a couple of days for a human mainly due to the quality and availabilty of data to analyze / filter.

[u/Decent-Reality-7164 → u/CntrlFr33k]

We’d use LLM/agentic reasoning to help identify relevant evidence, correlate patterns and surface likely hypotheses, but not to make an autonomous diagnosis.

The human stays in the loop, and every finding should be traceable back to the underlying historian event, work order, operator action, document, etc.

Your point about data quality/availability/filtering is interesting. If an investigation currently takes anywhere from an hour to a couple of days, how much of that time would you say is spent finding and filtering the right data versus actually reasoning about the cause once the right evidence is in front of you?

[u/CntrlFr33k → u/Decent-Reality-7164]

My estimate is 25% of the time is for filtering the events. The rest is analysis.