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 💡 Extending the concept of hourly averages to shorter time intervals📎 Related post: Calculations of hourly average consumption values 📌 BackgroundIn process industries, consumption monitoring of resources such as steam, water, electricity, and compressed air is key to ensuring energy efficiency and sustainable operations. While hourly averages provide a solid overview, shorter intervals like 30 minutes can deliver even more granular insights—especially for fast-changing processes or detailed performance analysis.👉 In some cases, half-hourly averages are required for regulatory reporting, for example to comply with environmental or energy-related limits imposed by authorities. Having these values available ensures transparency and compliance. 🎯 ChallengeHow can we calculate average consumption values for fixed 30-minute intervals and distinguish them from rolling values? And how can we ensure the values are visualized cleanly as stepwise changes in the trend? 🛠️ Solution✅ Rolling

In TrendMiner, time tags play a crucial role in process analysis. They allow users to define and segment data based on specific hours, days, weeks, months, quarters, and years, enabling precise time-based calculations. Many key performance indicators (KPIs) rely on time-based reporting, such as: Daily production totals Hourly average flowrates Monthly energy usage Quarterly business metrics Example of HOUR time tags in TrendMiner.​​​​TrendMiner time tags for the US Central time zone.Why Time Tags Matter in TrendMinerTime tags can be especially useful when defining formula tags that incorporate a time-based component. For example, you might need to calculate averages over specific time intervals or align production metrics with financial reporting periods. Like any other tag, time tags can also be utilized in value-based searches and monitors.Handling Less Common Time ZonesTrendMiner includes predefined time tags for commonly used time zones, making it easy to apply time-based ana

I just saw a question pop up in the community asking whether TrendMiner has built-in steam equations. While they are not built-in, here’s a way to start using steam equations in TrendMiner...A while back, I implemented the numeric steam table equations found on https://iapws.org/relguide/IF97-Rev.html into TrendMiner Formula Tags. I am posting those here in the hopes it can help some other users too.The attached zip file contains txt files for formulas that can be copy-pasted into a Tag Builder formula for steam table calculations.Formula units are currently in Celsius and bar, so you might need to adjust accordingly. Text search and replace should work to change the input units, for example 'P' -> '(P*1000)' to use tags that are in mbar. To change the output unit you can simply multiply the formula by a constant value.

Is your team making the most out of TrendMiner? As roles shift, objectives evolve, and technology advances, relying on outdated insights can lead to missed opportunities. A simple yet effective way to stay ahead is by conducting a survey to assess how your organization is using TrendMiner and the value users are getting from it. Why Conduct a Survey?Unbiased and Anonymous Feedback – Some users may feel uncomfortable sharing opinions openly. A survey provides a safe space for honest insights. Identify Areas for Improvement – Understanding user experiences helps pinpoint challenges and opportunities for optimization. Collaborate with Your CSM – Discussing survey results with your Customer Success Manager can uncover ways to enhance adoption and maximize TrendMiner’s impact. Key Questions to Include in Your SurveyConsider adding the following questions to gather meaningful insights:Name (optional) How frequently do you use TrendMiner? (Daily, Weekly, Monthly, Only when needed) How satisfi

We need your feedback to make sure our future direction fits the needs and preferences of our customers regarding deployment models, release process, security, and monitoring tooling.What to Expect?The survey covers current deployment models, release update strategies, and security/monitoring tooling. Some sections explore future possibilities like Kubernetes-based deployments, the usage of Cloud Services like EKS or AKS, and the potential evolution of the Virtual Appliance.Target audience:System Administrators & Technical StakeholdersThe survey is mostly multiple choice and should take approximately 10 minutes to complete.Thank you for your time and valuable input!Direct link: https://forms.office.com/e/cNsTs9BcLt

 IntroductionDaily production is a key performance indicator (KPI) in continuous processes, helping process experts track efficiency and performance. However, in many cases, daily production is not readily available as a default variable in data historians. A recent question in the TrendMiner Community addressed this challenge: How to calculate a daily average of a signal with a single result per day?. This article explains four different approaches to calculating daily production in TrendMiner, depending on the user’s needs. 1️⃣ Continuous Rolling Last 24 Hours Production 📌 BackgroundThis method ensures that at any given time, the production of the last 24 hours is always available as a continuously updating value. This is particularly useful for real-time monitoring and trend analysis.🛠 Steps Load the relevant tag related to production. Set the context and focus chart to visualize the required time range. Create a new aggregation: Select the production signal. Use a back

One of the customers I work closely with, always starts meetings with a Safety Moment. It’s a brief discussion about a safety topic to create awareness and encourage safe practices at work. This simple habit stuck with me, and it got me thinking about how safety isn’t just important at work—it matters just as much at home too.Following a similar theme from my previous post in this Community, after meeting with this customer and hearing him talk about doing work around the house and keeping a fire extinguisher 🔥 handy, I couldn’t help but reflect on my own habits. It reminded me of that time when I was doing some cabinetry work and cutting wood  with an electric saw 🔌 ✂, and I realized I hadn’t taken the necessary precautions, like wearing safety glasses  or checking that my fire extinguisher was where I thought it would be.If you’re in a leadership role, you might be encouraged to promote safety among your team. One very simple practice you can start implementing is having a Safety M

Applying Transformations to Your Tag Data in TrendHub In process data analysis, relationships between variables are often assumed to be linear. However, real-world process relationships can be more complex and often are. For example, the relationship between pressure drop and flowrate is only linear under laminar flow conditions. In turbulent flow, which is more common in industrial processes, the relationship follows a quadratic function, as described by the Darcy-Weisbach equation.Because of these complexities, it is crucial to apply transformations to your data to properly analyze and interpret process relationships. TrendMiner's formula tag builder provides a way to achieve this.Example: Analyzing Pressure Drop Across a Flow PumpIn this example, we analyze the pressure drop across a pump using two tags: Flowmeter Tag (Q) - “TM-HEX-FI0620” Pressure Drop Tag (ΔP) - “TM-HEX-PDI0620” When plotting these tags on a scatterplot, we observe that the relationship is not linear. Instead, i

TrendMiner’s security features, such as mandatory SSL setup and NTP server configuration, ensure secure sessions. However, if there’s a time mismatch between your local machine and the TrendMiner server, it can lead to unexpected logouts. If you're logged out immediately after logging in, it is typically related to an issue with the SSL configuration.Troubleshooting Steps: Are you logged out immediately after logging in? Check SSL: Ensure SSL is enabled and the certificate is valid. An incorrect SSL setup can cause immediate logouts. Does it take at least a few seconds before you're logged out? Verify NTP Server Configuration: TrendMiner relies on an NTP server to synchronize time. If it’s not configured, time discrepancies may occur. An NTP server can be configured in Edge manager. Check Local Machine Time: Make sure your local machine’s time matches the NTP server. You can verify this by comparing it to public time sites like time.is. if the issue persists, please contact your Tr

 📌 BackgroundIn any industrial process, there are typically periods of optimal (good) and sub-optimal (bad) performance. Identifying and comparing these periods helps in understanding the differences and optimizing operations. By analyzing the variations between good and bad periods, process engineers can uncover key factors affecting efficiency and quality. ⚠️ ChallengeOne of the main challenges in process optimization is recognizing the factors that differentiate a good period from a bad one. Some common difficulties include: 🔎 Identifying relevant time periods or batches in continuous and batch processes. 📊 Establishing objective quality metrics to categorize periods. 🔄 Effectively comparing periods visually and numerically to extract actionable insights.  🛠 SolutionTrendMiner provides a structured approach to compare good and bad process periods through the following steps:✅ 1. Feature Search Use the value-based search function to find operating periods or batches. For b

The TrendMiner server needs 2 volumes: a root volume (/root) and a data volume (/mnt/data). TrendMiner data (saved work, tag indexes) are all stored on the data volume. When your usage increases your data volume can run full and will need extension. On a single node deployment you can simply increase the volume in the console.On a virtual appliance deployment console access is not available and you need to add a second disk in your hypervisor. For TrendMiner to recognize the additional disk you need to click the ‘extend disk’ button which you can find in Edge Manager diagnostics. Important note: once a disk is extended it is not possible to claim back the disk space! More details and info on this disk extension procedure can also be found in our documentation: https://documentation.trendminer.com/en/disk-space-extension.html 

Using TrendMiner’s Cross-Correlations Tool for Root Cause Analysis One of the most powerful features within TrendMiner is the cross-correlations tool. It is especially useful when conducting root cause analysis because it helps identify correlated tags that could serve as early indicators for your tag of interest. Identifying the Root Cause of a Process Disturbance Let’s look at an example of how to identify the root cause of a process disturbance.A process engineer monitoring a reactor unit notices an abnormal temperature spike in the reactor. The unit supervisor asks the engineer to determine what may have caused this increase. However, without immediate input from board operators and field operators, it can be difficult to know where to start. Reactor temperature fluctuations can result from many different factors, making troubleshooting a complex task.This is where TrendMiner can help.By using the cross-correlations tool, TrendMiner can quickly compute correlations between the reac

For rotating equipment, we need to seal off moving components with mechanical seals that use a barrier fluid. There is no way to seal off moving components perfectly so there will always be some leakage. If product leakage to the environment is unacceptable (typically for SHE reasons), a zero emission seal needs to be used. For zero emission seals, there can only be leakage from the barrier fluid into the product, which is why a barrier fluid that is under higher pressure than the pumped fluid needs to be used that is compatible with the product. After all, at least some of this barrier fluid will end up in the product. A classical example of this is the usage of N2 as a barrier fluid to natural gas.Mechanical seals are common but critical components, and they can be monitored quite easily in TrendMiner as the monitoring criteria are simply value-based. We can sometimes use a value-based search directly, but if we need to compare two measurement (e.g. to ensure a difference in pressure

TrendMiner's monitoring functionality empowers users to create alerts for critical process behaviours. This enables engineers to receive immediate notifications of events that match specific parameters or deviate from expected patterns. (See real-world examples on how to be compliant with ISA 18.2 standards from Bayer and a helpful Tip&Trick to detect short duration events). Learn more about setting up monitors in our user guide.While TrendMiner's default setting allows for 10 active monitors per user, we understand that your needs may exceed this limit. To ensure you can fully leverage TrendMiner's capabilities, we offer the option to increase your active monitor capacity. ↗️By expanding your monitor limit, you can gain even greater control over your processes, proactively identify potential issues, and optimise your operations. Don't let limitations hold you back from maximising the value TrendMiner brings to your company‼️Contact your Customer Success Manager (CSM) or TrendMiner

 Hello 🖐🏻Identifying periods where parameter readings increase or decrease is crucial for process monitoring!🤔Why it matters? Changes often indicate potential issues or opportunities for optimization.✍How to analyze? Calculating the derivative of a tag allows for a more precise assessment of how sensitive a process is to fluctuations in specific parameters.📊 In TrendMiner : Different methods are available to determine the derivative based on the type of analysis needed. 👉🏻 Preparation: Smoothing Signals For noisy or highly fluctuating signals, it is recommended to perform an aggregation before calculating the derivative.This helps achieve a more accurate approximation of the derivative of the selected function.Recommended Aggregation:Tag: The tag for which the derivative should be calculated Operator: Average Direction: e.g., Backward Aggregation interval: e.g., 10 minutes   👉🏻 Option A: Calculating the Derivative Using a Formula The derivative can be determined by calculating