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What is the best way to dynamically identify data shifts like this in Trendminer?  Control chart calculations would be ideal, but I don’t think that’s possible. 

Hi team Trendminer,I am encountering a bug with the Collapsed Filters. In the Trendhub View below I have 4 signals over 2 trends on which a filter is applied. The filter is based on a simple Value Based Search, which is returning the results I expect. This is confirmed in both the Trendview and the Context View.  As soon as I toggle ‘Collapsed Filters’, the filtering overlay disappears and no collapsed plotting is applied: With every toggle an error is spawn in the browser console: ​​​

Condition-based maintenance 🔧 is powerful. Monitor what's happening and act only when needed. But some maintenance tasks still need to track time 🕑. Regulatory requirements don't care if your asset is running smoothly. Some failure modes are unpredictable and preventive by nature. And if your equipment runs 2 hours in winter and 18 in summer, a calendar-based schedule is useless. You need to track actual operating hours since the last maintenance event.The strongest programs use both. You monitor bearing temperature and oil quality in real time. You also ensure critical seals get replaced every 5,000 operating hours. With TrendMiner's custom calculation feature, you can build a time-since-maintenance tag by defining the operating conditions that "count" toward your interval, then tallying the actual hours. Set a monitor and get alerted when you hit the threshold. When the equipment has actually earned the maintenance.Want to build operating-hour counters for your equipment? Reach out

Hello 👋,The recording of the webinar “What's New in TrendMiner 2026.R2.0? “ is now available.Watch the recording:  Release Notes SlidesHere is the summary of the webinar: Mobile Dashboards: Your TrendMiner insights are now on your phone, with no app to install, so they travel with you on the plant floor, on a walkround, or away from the control room. You can even scan a QR code on a piece of equipment to open the right dashboard in seconds.Increased Level of Nesting for Calculated Tags:Calculated tags can now be nested up to 20 levels deep, so you can build hierarchical roll-ups that mirror your plant: compute KPIs at unit level, aggregate at area level, and roll them up to plant level, with each step saved as its own reusable tag. The tag dependency browser also gains an Edit button that takes you straight to the Tag Builder.TrendMiner's New MCP Server:It connects your process data and analytic tools to the AI assistant your team already uses. Engineers and operators can ask question

I was able to follow the instructions on another thread on how to create an aggregation tag for an integration over 24 hours of a binary tag for line running or not and it will give me the downtime over the last 24 hours at any specific point. Is there a way to lock in a time so that on any given day at 7 am, it will show me the downtime over the last 24 hours to the previous day at 7 am? We have a few metrics like that that we report out on over the 7 am - 7 am window and that feature would be very helpful.

Weather and climate conditions have a measurable impact on manufacturing operations. Temperature fluctuations affect material properties, humidity influences product quality, and atmospheric pressure can impact equipment performance. By bringing weather data directly into TrendMiner, you can correlate these environmental factors with your production metrics in real time, revealing patterns that spreadsheet-based analysis misses.Quick exampleHere's a Python script you can run within custom calculations to extract the latest weather data and create a new tag with it. You can also create your own custom connector using our API documentation; reach out to your Customer Success Manager or the TrendMiner Support Team for more information.import pandas as pdfrom datetime import datetimeimport pytzimport osimport requestsfrom trendminer import TrendMinerClientclient = TrendMinerClient.from_token(token=os.environ['ACCESS_TOKEN'])output_file = os.environ["OUTPUT_FILE"]# Set your facility timezon

Do you have spaced events such as batches, equipment startups or production runs of a particular product type that you want to visualize side by side? Apply a manual filter or a search-based filter that captures the timeframes between each event, then collapse the filters. You can now see these events stitched next to each other for a clearer view of each.