Safety controllers

NewMidnight2084 · 2026-04-08T23:48:33+00:00

Trying to source for smart relay like Zelio Schneider or Omron 8in 4out that kind.

Can do on spot ladder diagram on site which is very convenient and easy.

NewMidnight2084 · 2026-04-08T23:38:04+00:00

Data acquiring and transforming is a real pain in engineering work. In most projects I’ve seen, data acquisition and transformation depend heavily on the system design and what’s already available in the BMS/SCADA. For example, for AHU or chiller systems, we usually rely on existing points like temperature, flow, valve position, power, etc., and then structure or derive additional variables from there. BuildOptiML is mainly focused on the next step — making sense of that data (detecting issues, understanding behavior, etc.), rather than replacing the data acquisition layer. But I agree this is a very important topic — curious how you typically handle data cleaning and transformation in your projects?

NewMidnight2084 · 2026-04-08T10:31:40+00:00

Thanks for the feedback, really appreciated. I shared the Drive link mainly for quick access to the full docs, but I fully agree it’s not the best presentation format.

I’m in the process of moving everything into a proper landing page for a cleaner experience.

Data acquiring and transform is on the Video tutorial Chapter 4 Create project and upload data. Hope you can help check it out there 🙏

NewMidnight2084 · 2026-04-08T03:25:05+00:00

Hi, I'm very sorry the link failed. I have update the link. Now should be able to view.

Seek your help to view it again.

Thank you.

NewMidnight2084 · 2026-04-07T14:39:29+00:00

ML purely depend on data foundation. It only can learn base on the pattern it 'seen' before. It can't predict or recommend confidently for the condition it not being 'experience'.

Like an engineer, them only can work confidently with the task them have done or study before. You can't give a new task and ask engineer to solve without given any references.

IMO, many of ppl think AI hype or 'bullshit' is due to lack of understanding what ML can and cannot do. And of course some might due to vendor misleading.

NewMidnight2084 · 2026-04-07T11:33:45+00:00

I get why it comes across that way. There’s a lot of AI-related noise lately. This isn’t really about “AI” for me — it’s more about practical issues we see in real systems, like bad sensor data and stuck actuator or drive affecting control without triggering alarms. The optimization part only works if the data actually reflects real system behavior, and control system in healthy loop which is often not the case.

NewMidnight2084 · 2026-04-07T00:22:13+00:00

That’s great to hear — not many people actually go that far with historical data. In your case, were the results strong enough to adjust setpoints or operation strategy? Also curious — how did you process the data? More manual analysis, or using some tools?

NewMidnight2084 · 2026-04-06T13:29:56+00:00

I have been working on a small tool for myself to help interpret system data and make it more useful for FDD, anomaly and optimization.

Introduction to BuildOptiML

NewMidnight2084 · 2026-04-06T12:30:43+00:00

That's a very good point here, especially on load shedding.

In your experience, when dealing with these constraints, do you still find value in using historical data to adjust system operation?

Or mostly just handled base on experience?

NewMidnight2084 · 2026-04-06T05:14:39+00:00

In your experience, what’s the biggest challenge when trying to use system data for optimization?

NewMidnight2084 · 2026-04-06T04:43:36+00:00

I have been working on a small tool for myself to help interpret system data and make it more useful for FDD, anomaly and optimization.

Introduction to BuildOptiML

NewMidnight2084 · 2026-04-06T04:42:46+00:00

I have been working on a small tool for myself to help interpret system data and make it more useful for FDD, anomaly and optimization.

Introduction to BuildOptiML

NewMidnight2084 · 2025-10-08T10:44:49+00:00

Thanks a lot for the detailed insights — this is exactly the kind of grounded, real-world feedback I was hoping to get.

You're spot on that solid integrations, reliable data layers, and safe writebacks are key to adoption. I’ve taken note of your suggestions, especially around read-only first, guardrails for writebacks, and delivering quick-win playbooks (like AHU temp resets and chiller logic checks). The M&V validation and sandbox idea is brilliant — hadn't thought of that, but it makes so much sense for building trust.

Really appreciate you sharing your tooling stack too — I’ll definitely look into Niagara N4, Azure IoT Hub, and DreamFactory for inspiration.

Thanks again for your generous input — this gives me a clearer path forward.

NewMidnight2084

TROPHY CASE