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  Background Monitoring thresholds is a common use case in the chemical industry, essential for maintaining consistent production quality and ensuring operational safety. Thresholds act as predefined limits, signaling potential issues in the process when exceeded. Failure to effectively monitor these thresholds can lead to production inefficiencies, equipment damage, and reduced product quality. Such deviations can be caused by factors like temperature fluctuations, pressure variations, or torque instability, as well as unexpected changes in raw material quality. TrendMiner provides an effective solution by enabling reliable and continuous monitoring of these thresholds. ChallengeThe challenge in monitoring thresholds lies in precisely defining and continuously tracking them. TrendMiner offers a solution by enabling users to set up monitoring through value-based searches, which can trigger automatic alerts. Additionally, the creation of context items can be activated to allow for detai

If you ever downloaded TrendMiner from our file server you might have noticed there are a lot of files on there. The .tar.gz files are the actual TrendMiner installation/upgrade packages. The .zip file is the connector installation/upgrade package. But do you know what these .md5 and .sha1 files are? md5 and sha1 are 2 different cryptographic hash functions. A cryptographic hash function is a mathematical algorithm that takes an input of any size and produces a fixed-size, unique output (hash) that appears random. It is designed to be secure, meaning even a small change in the input drastically changes the output, and it is computationally infeasible to reverse or find two different inputs with the same hash.TrendMiner provides the hashes of the installation packages for you as a customer to verify that the file you downloaded is identical to the one which is hosted on our server and the file was not corrupted during download.How do you do that?Download and open the .md5 from our file

Sometimes, for better visualization and monitoring, it is helpful to represent the durations of certain important events in the plant within a variable. This is possible in TrendMiner by following a couple of steps.First, it is necessary to create a slope tag, from 0 to 1, that connects the start and end points of the event.This can be done with a conditional formula and time changes. For example, an event from the moment a batch starts until the “Phase 3” starts: It is important that the obtained variable consists of constant slopes from 0 to 1 and from 1 to 0. We do not want it to remain constant at values of 0 or 1 for long periods, as this could affect the second step of the workaround. This is why time shifts are used, indicating only the exact moment of start and end.Secondly, we need to transform the slope tag into another variable that indicates the duration of each slope. This is done with the formula 'if((a-b)>0,1/(a-b)/60,,0)' following the explanation provided below (to

Communication and collaboration in the form of a user community has anchored itself as a powerful technique that enhances the overall experience for everyone, from users to admins to developers. User communities foster collaboration, serve as inspiration, propel innovation, harvest a support system, and enablement of users. How well the users engage in the community can amplify the success of an organization. Here are at least four reasons to bring users to the TrendMiner community.Share knowledge and expertise. Every user brings a unique insight that can help the organization achieve its OKRs (objectives and key results). In a shared community space, everyone can tap into this resource and reap the benefits. It is not uncommon that users in different plants or business units and even in other organizations are facing similar challenges. By sharing these challenges, a solution can be reached faster and inspire new ideas.  Sense of belonging and support. Plant life can be busy and probl

BackgroundIn one of the distillation units, one of the product temperatures is being lower than expected for around 1 year, leading to lower product recovery, as the separation is not as efficient as it should. This means a 10% recovery loss in the company, which is an important economical loss. ChallengeTroubleshoot the causes for this deterioration in performance, by detecting the influencing variables and the respective influence of each. Solution-Load all the related tags, as well as the key temperature-Run the correlations engine to find correlations against the potentially correlated variables and against all the variables of the asset-Layer 2 periods (high and low recovery) and compare statistics and trends of the potentially influencing parameters Results and value-Two process variables were identified as the main influencing factors. -It was identified that both variables weren't acting at the same time, but separately. That's why by themselves, the correlation is not to high,

BackgroundAs a process engineer, understanding the relationship between two variables is crucial and often necessary. For example, how does tower pressure impact overhead rates? How does temperature affect conversion? Operations are constantly making adjustments, and process engineers need to grasp the impact of various levers or handles on final product specifications, operating envelopes, and process safety. TrendMiner enables users to quickly analyze relationships between tags or variables, both visually and quantitatively.Scatterplot of natural gas usage vs carbon emissions.Steps to Analyze Relationships in TrendMiner:Step 1: Add the tags you are interested in analyzing to the active tag list for bivariate relationships.Step 2: Switch to the Scatterplot view.Change view from “Trend” to “Scatter”.Step 3: Adjust the time period to include relevant data for your analysis.Step 4: Evaluate the relationship. Use the scatterplot to qualitatively assess the relationship between the variabl

A broad implementation of TrendMiner exponentially increases the value your organization can get from our product. If more people are using TrendMiner, more people can collaborate, learn from each other and in the end, it will be easier to maintain knowledge inside the organization. Therefore, it is crucial to ensure that new employees know about all the relevant tools used.  There are several ways to make TrendMiner a part of your onboarding curriculum. Here are some tips:  Make a checklist of all the onboarding steps, including the different important tools which are being used.  Explain clearly why TrendMiner is important for your organization.   Make sure new employees have immediate access to TrendMiner.  Interactive Workshops: Organize sessions where new hires can practice using TrendMiner with real examples from your plant. Provide access to our training videos, documentation, and best practices guides that they can refer to as needed: https://userguide.trendminer.com/?lang=

Here's how TrendMiner can be utilized at each stage:Define: Use TrendMiner to define the problem or opportunity by analyzing historical process data to identify trends, patterns, and areas for improvement => Getting started with TrendMiner Collaborate with stakeholders to establish clear project goals and objectives. Measure: Utilize TrendMiner to collect and visualize real-time and historical process data from various sources (Time series, Context and Asset data). Measure key performance indicators (KPIs) and baseline process performance to establish a benchmark for improvement. Analyze: Apply advanced analytics and visualization tools within TrendMiner to analyze process data and identify root causes of inefficiencies or deviations. Use TrendMiner's self-service analytics capabilities to empower subject matter experts and process engineers to explore data and generate insights independently => Enable them to become data engineers with TrendMiner, even if they're not data e

Our recent release webinar recording is now available. You can watch it to see detailed explanations and demos of the latest TrendMiner functionalities.  We encourage you to visit this community post and vote for your favorite functionality or share your feedback in the comments.

If you notice that a certain tag in TrendMiner is displaying data that might be incorrect, you can use the PlantIntegrations API to check which tag data is being retrieved from the historian.If the data in the historian corresponds with the data in TrendMiner, the faulty data is already present in the historian and should be rectified there.To access the PlantIntegrations API you can do the following:Open the IIS server on which PlantIntegrations is installed. Open the IIS manager and click on the PlantIntegrations website. On the right side of the screen you can now choose the ‘browse’ option which will lead you to the API.  This will lead you to the following API page: To perform an index call here you need the following information:Tag name Historian name Tag type Interpolation type Start- and enddate of the period you want to request Number of intervalsYou can find an example of an index call below in which the parts in green need to be configured depending on the call you want to

In our 2024.R1.0 release we introduced an additional layer of security, meaning an OTP code is mandatory to use the Admin account. (for ConfigHub and TrendHub login)You can read more about this in the article from Jef Vanlaer here.While trying to authenticate with the Admin account, I ran into following error message:  Invalid authenticator codeI found out that the root cause of this error is due to my TrendMiner server time not being in sync, thus the server thinks that the code I am providing is long expired/not valid yet.  After resolving the NTP issue my Admin login is working without issue again.

In our latest release, 2024R2, we’ve enhanced the Scatterplot display in TrendHub to visualize all layers of a TrendHub view, not just the base layer. This new feature enables you to compare relationships between different tags across multiple layers, making it a powerful tool for troubleshooting, root cause analysis, performance grading, and more. In this article, we'll walk you through how to use this new feature to its full potential.Step 1: Adding Different LayersThere are several ways to add layers in TrendMiner. In our example, we used value-based searches to identify high- and low-quality batches and added the three highest quality and three lowest quality batches as layers.Overview of all the layersStep 2: Switching to Scatterplot ViewBy changing the plot visualization to a scatterplot, you’ll immediately see the option to create a multi-layer scatterplot.Scatterplot visualization with multi-layer enabledStep 3: Customizing Your LayersClicking the “Customize Layers” button open

As you might already know TrendMiner provides some support scripts to make some tasks easier. Today we have 5 official support scripts: batch indexing: indexes the tags in a given list indexing performance testing: benchmarks the indexing performance provided by a historian at specific index resolutions listing tags in a historian: lists all tags available to TrendMiner for a specific historian listing indexed tags: lists all tags currently indexed in TrendMiner cleaning indexes: clears the index of those tags that match the provided criteria All details can be found in this documentation article: https://documentation.trendminer.com/en/support-scripts.html But next to these scripts you can also leverage the TrendMiner APIs to write your own script in your favourite language. For example: clean all indexes of a list of tags provided via a .csv file.Did you write your own scripts already? Which scripts would you like to see productised in TrendMiner? Let us know!

When was the last time you had a TrendMiner HealthCheck?You wouldn't drive your car for years without an oil change? Or skip your annual physical? Just like your vehicle and your body, you need a TrendMiner HealthCheck to ensure peak performance for your data analytics. What's a TrendMiner HealthCheck?Think of a HealthCheck as a tune-up for your TrendMiner implementation. It's a dedicated session with your Customer Success Manager and Data Analytics Engineer to review new features, optimize your current setup, and ensure you're squeezing every drop of value from our platform.How Can a TrendMiner HealthCheck Supercharge Your Analytics?Discover hidden gems: We're constantly rolling out new functionalities. Your CSM will showcase the latest and greatest, tailored to your specific needs. Optimize your workflow: Are you taking the scenic route when there's a shortcut available? We'll help streamline your processes for maximum efficiency. Align with best practices: Learn how industry leaders

Creating clear and effective graphs is a vital skill for anyone involved in data presentation. In process engineering, the ability to visually convey complex information can significantly enhance the impact of your work. However, not all graphs are created equal. Poorly designed graphs can mislead, confuse, and obscure the very data they aim to highlight. This functional tip outlines essential best practices for making enhanced TrendHub views that are both informative and visually engaging. By following these guidelines, you can ensure your graphs not only accurately represent your data but also resonate with your audience, facilitating better understanding and decision-making. Keep It Simple: Avoid clutter by minimizing gridlines unless they are necessary.     Try to avoid displaying excessive context items unless they are needed to emphasize a reoccurring issue.   Consider whether all data actually needs to be displayed in the focus chart, or if keeping longer-range data i

 In industrial data analysis, precisely examining short events is crucial for optimizing processes and preventing disturbances. Short events, which are shorter than the index resolution, present a unique challenge as they can easily be overlooked. Here, I will show you how short events can still be analyzed in TrendMiner using a value-based search.Examples of short eventsExamples of short events include sudden pressure spikes in pipeline systems, which may indicate valve malfunctions or blockages, temporary temperature fluctuations in reactors, which can point to unexpected chemical reactions, and brief electrical spikes in systems, which may signal short circuits or overloads. Analyzing these events accurately allows for taking preventive measures and improving process stability and system safety. Background on indexing in TrendMinerTrendMiner uses indexing to enable fast, interactive visualization and analysis of time-series data. When a tag is accessed for the first time, it undergo

 Background Fluctuating process parameters in the chemical industry present a significant challenge as they can impact the efficiency and quality of production. These variations can be caused by inaccurate measurements, unstable raw material quality, or insufficient process controls.For instance, Torque fluctuations in machinery may lead to mechanical damage, compromising operational safety. Temperature fluctuations in reactors can affect reaction kinetics, resulting in inconsistent product quality. Likewise, pressure fluctuations in pipelines or reaction vessels can affect product consistency and equipment safety. ChallengeManaging fluctuating process parameters may require setting and monitoring thresholds, tracking the range of variations, and creating context items. TrendMiner simplifies this by allowing users to define and oversee limits, measure fluctuations, and visualize all relevant data on a dashboard.  SolutionDefining upper and lower limitsIn this step, upper and lower torq

BackgroundManufacturing plants often rely on both railcars and trucks to ship their products to distributors, retailers, or directly to customers. The choice between these transportation methods depends on various factors such as the nature of the goods, cost, distance, and delivery time requirements. Railcars are typically used for bulk shipments of heavy and large quantities of goods, such as raw materials, chemicals, and pellets, due to their capacity to transport large volumes efficiently over long distances. Conversely, trucks offer greater flexibility and are ideal for shorter distances and time-sensitive deliveries, providing door-to-door service and easier access to various destinations. This combination of transportation methods allows manufacturing plants to optimize their logistics and ensure timely and cost-effective distribution of their products. ChallengeKeeping an accurate count of how many vessels have been filled can be a challenging task for manufacturing plants, par

Gather Data Sources & Learn the Data Flow: Think beyond your data historian and understand the ecosystem. When initiating a pilot with TrendMiner, most discussions center around connecting to a data historian, where the process/manufacturing sensor data resides. However, to maximize the pilot's success, it may be beneficial to integrate other sources such as Laboratory Information Management Systems (LIMS) and Manufacturing Execution Systems (MES). Your Customer Success Manager can provide insights into which use-cases would benefit from such additional data integration. Even if you are not within the IT organization, having a basic understanding of how data flows through various components of TrendMiner is crucial for effectively communicating with stakeholders across different departments. TrendMiner’s Data Architect can clarify how the platform acquires and processes data. Get Buy-In from End Users: Consider who will be the end user for TrendMiner. Those working in Operations,

Often, we want to analyze or report on key KPIs, such as consumption, production, average temperatures, or maximum values of critical variables. However, these data need to be analyzed separately based on the recipe they correspond to.In TrendMiner, it is possible to unify all workflows so that reports for multiple recipes can be obtained with a single process. This is due to the ability to include the “constant” condition in the Value Based Search and to add string variable calculations. Here’s how it is done:Prepare the View: Load the tag indicating the recipe name and the rest of the necessary tags to create the report. Perform the Search: Go to the search menu and start a value-based search. The condition will be: Recipe name tag CONSTANT. The results obtained will correspond to the time period indicated in the context chart. Add Calculations to the Search Results: String Variables Calculation: Start value of the recipe name. This calculation will indicate which recipe each of t

BackgroundThe material management department needs the daily consumption of caustic to order the correct amount from suppliers and avoid shortages or excess.This is calculated from the reactor's weight sensor after each of the approximately 20 daily discharges.Before TrendMiner, we manually recorded these weights, a tedious process prone to errors, missing data, and a lack of consumption analysis. ChallengesUnderstand the normal daily raw material consumption to request the needed amount from providers and optimize inventory space. SolutionCreate a formula to create the derivative of the reactor weight only when it's negative (discharge).   Value based search to search every day in the last 6 months Add calculations with the day name and the integral of the formula (daily consumption) Export report  Results and value-A report of the last 6 months was created in 10 minutes, the same amount of time that it takes to manually get the report of 1 day. From now on, only 1 click is needed

🚀 Our CS team is dedicated to ensuring seamless roll-outs and maximizing the adoption of TrendMiner within your organization. To assist you, we've compiled a set of best practices. How do you approach the adoption process? What strategies do you use to activate new users or re-engage existing ones? Share your insights in the comments below! ⬇️

TrendMiner does not support changing the (interpolation) type of a tag in the data source. This implies that for example when you change a tag from linear to stepped interpolation in your historian the tag will not load anymore in TrendHub and an error will be shown. You will also notice that the tag index can no longer be updated and goes to a STALE state.TrendMiner support can typically derive this from the logfiles, but if you already know this is the root cause of the problem then there are a few steps you can perform to resolve the issue.Refresh the tag cache manually in ConfigHub for the data source the tag is synced from (or wait up to 24h for the changes to be synced automatically) Restart the tm-compute service in ConfigHub or Edge Manager. Keep in mind that restarting tm-compute will entail downtime for the users (typically a few minutes). Re-index the tag. Edit and save all calculated tags and searches which depend on the changed tag (it is not required to make any chang

Background:TrendMiner's "Event Analytics" is typically used for batch processes, but it can also optimize continuous processes. By breaking down the data into manageable hourly intervals, this approach provides detailed monitoring and identifies optimal conditions for enhanced efficiency and productivity.Steps: Preparation: Add the most important process parameters. Include the pre-installed TM_day_ tag. Data Splitting: Use the value-based search feature to segment data. Set the condition to TM_day_ constant. Ensure a minimum duration of 59 minutes. Adding Calculations: Add calculations on the search result. Example calculations: Average Temperature, Integral Cooling Water, Minimum Pressure. Event Analytics: Use the 1-hour aggregated data within Event Analytics. Analyze the data using histograms or parallel coordinate plots to visualize and identify optimal process conditions. Picture 1: This parallel coordinate plot from TrendMiner’s Event Analytics shows the relationship b

This use case demonstrates how TrendMiner's functionalities can be leveraged to perform retrospective analyses, leading to enhanced productivity and efficiency in our power plant operations. BackgroundIn our power plant, managing fuel consumption efficiently is crucial for operational cost savings and environmental impact reduction. This use case focuses on a retrospective analysis aimed at reducing fuel consumption by adjusting PID (Proportional-Integral-Derivative) parameters within our system.ChallengeThe primary challenge was to reduce fuel consumption without compromising the performance and stability of the power plant. The desired outcome was to identify the impact of PID parameter adjustments on fuel consumption by comparing data from specific periods before and after the changes were implemented.SolutionTo address this challenge, the following steps were taken: Parameter Adjustment: The PID parameters were adjusted in our power plant system to optimize fuel consumption. Data