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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

On June 20th, we hosted an admin webinar to prepare our admins for the upcoming 2024.R2.0 TrendMiner release. With this release, we are moving to a new Operating System (OS) given the end-of-support for Red Hat Enterprise Linux 7 (RHEL 7) and the derived CentOS 7.Two actions will be needed to upgrade your TrendMiner setup to version 2024.R2.0 once it is publicly released:The creation of a new TrendMiner server based on the new OS A migration of the application data to the new serverThis webinar details the prerequisites and steps to be taken to complete these actions.Additionally, it explains the migration options, including the new and highly recommended full system backup/restore functionality with secure storage on your own AWS S3 or Azure Blob Storage, which includes index migration. A detailed demo of this process is provided.Finally some important considerations and recommendations for planning the migration are outlined.Check out the recording below and refer to the "OS Migratio

In Edge Manager → Diagnostics the available disk space and the total disk space of TrendMiner can be checked. The disk space shown there shows the space on the /mnt/data volume (the volume on which the TrendMiner appliance is installed and on which TrendMiner stores its data).Next to the /mnt/data volume there is also the root volume, on which the OS and system services are running. Both the root volume and the /mnt/data volume can run out of disk space which in both cases will result in service interruption.The root volume total disk space and usage is not shown in Edge Manager since TrendMiner does not actively use this volume. Disk space issues should not occur on this volume if the system requirements are met.The /mnt/data volume is used by the TrendMiner application for storing data. This data mainly consists of: Info stored in the database, e.g. saved work from users. Index data Logically, the more usage of TrendMiner and the more tags are being indexed, the more disk space wil

BackgroundOften times, we find ourselves wanting to know when a certain tag changes state. For example, if we have a batch process, I’d like to know when I transition from one phase to another in the process. Or for a multi-product line, I would like to know when we switch from manufacturing product A to product B. Not only would we like to know when these state changes occur, but also it would be useful to analyze the data per state or segment. Consider the batch process example again - it would be nice to know the duration of each phase. Or even, what is the maximum temperature at each phase of the batch process?In TrendMiner, we can quickly answer these questions by conducting a constant Value Based Search (VBS). When selecting constant in a VBS, we ask TrendMiner to identify every change of state or level. I say level too because constant VBS can apply to analog tags in addition to string and discrete tags. Below I will detail the steps to create a constant VBS. Step 1 - Create the

BackgroundThis use case is centered on finding the median value of a collection of tags. In TrendMiner, it is simple to calculate the mean or average value of a set of tags. This can be done in our formula tag builder. In some cases, however, the median value proves to be more useful of a central statistic than the mean. For example, consider a furnace where there are multiple thermocouples measuring the same point temperature. If one of these thermocouples were to malfunction and read a very high or very low value, the mean value will be skewed high or low, respectively. When outliers exist in the data, the median value proves to be a useful central statistic. ChallengeThere is no built-in function in formula tag builder for calculating the median value of a collection of tags. A simple, manual formula can be created to find the median for a collection of two or three tags; however, the manual formula grows exponentially when additional tags are added. Writing a formula to calculate t

The picture shows a change agent offering a more efficient way of getting things done. Unfortunately, there are blockers that hold back people from changing their ways. What we see here did not only happen in the stone ages. It’s something very natural as our brains enjoy the comfort of the known and often try to reject the new. BUT there are actions to take to help people manage change. Let’s look at some blockers and how to deal with them below. People stick to doing what they know: this is the way it’s done for a longer period & it gets the job done The longer people do things in the same way, the more difficult it is to introduce changes. The best course of action is to know the benefits of the change and sharing them often on multiple levels (see point 2). Pro tip 1: Have 1-1 conversations with the most outspoken people showing resistance to understand concerns and help address them. People who resist the most can be your greatest advocates Pro tip 2: it makes a lot of sense

TrendMiner’s mission is to design and deliver a fast, powerful, and intuitive platform that drives the adoption of data and analytics​ for everyone in industrial operations to make smarter data-driven decisions. To deliver on the mission and democratise data analytics in your company, change management ensures that new processes, technologies or organisational changes, such as the introduction of TrendMiner, are implemented smoothly and successfully in order to minimise resistance and maximise commitment and acceptance. By managing the human side of change, project managers and coaches can facilitate a smooth transition, encourage employee engagement and drive continuous improvement, with the TrendMiner team supporting you throughout the journey.  TrendMiner’s 8 Change Management Best Practices Engage Stakeholders Early and Often: Involve stakeholders from across the organization in the adoption process from the beginning. Seek input, address concerns, and ensure that all voices are he

Sometimes we get contacted by admins who are concerned about the memory usage of their TrendMiner installation. In Edge Manager -> Diagnostics you can check the amount of memory (RAM) available for the TrendMiner server and the percentage of memory which is used. In most cases, when you check these numbers you'll notice that the usage is over 90%, often even close to 100%.  Rest assured, this is not an issue. Linux (the OS on which TrendMiner runs) uses all available memory for optimal performance, instead of leaving it unused. If you are interested to read up on all the details we can advise to check this site: https://www.linuxatemyram.com (disclaimer: this is an external website, unaffiliated to TrendMiner and TrendMiner is not accountable for the site nor its content).  So how does TrendMiner use its available memory?Each microservice in TrendMiner gets assigned a fixed amount of memory based on the needs of the service. Our engineers make sure everything fits within the minimal

We all love the monitor feature in TrendMiner, however sometimes it can also be annoying when the monitor is triggered too many times and creates an email spam. In this article we will discuss how you can prevent an endless stream of TrendMiner monitor alert (emails) and just get an alert when a real event occurs.1. Use search results as indication for number of alertsA TrendMiner search is often the backbone of a monitor and thus we can use the number of search results as indication to the amount of monitor alerts we will get within the search period. For example, if we perform a search with a context bar period of 6 months and get 15 results, then we know that when we apply a monitor with this search we will get 15 monitor alerts within 6 months.2. Finetune your value based searchWhen you use a value based search the first thing you can tune to optimize your monitor is the minimal duration setting of your value base. In this example we want to know for when our flow out of our pump i

LyondellBasell rolled out TrendMiner globally after the initial success of the pilot with a dedicated project team and TrendMiner’s Customer Success team. Highlighted below are the key steps taken to achieve success.  Identify the business needs for a solution Identify challenges and key users who are SMEs to drive the project Select pilot sites with the most potential to test & evaluate the solution Start with smaller but practical use cases to build confidence and gain trust of SMEs. Avoid the temptation to take on the most challenging tasks first to prove value. In the value matrix, start from the lower left-hand corner and work towards the upper right-hand corner Communicate and celebrate even the smallest wins Scale to more challenging use cases for higher valueValue Matrix With the successful pilot and proven business value, operationalize the pilot approach to other areas with a similar business need.Operationalize the pilot template for rollout Implement a project team con

BackgroundThis use case shows how to create a dashboard that shows the number of events per day of the week. This can be emptying/filling of a tank or number of produced batches, or any event indicated by a tag in TrendMiner. Having such an overview can help you gain better insights in your process operations.SolutionThe solution includes the following steps:Value based search: Create a value based search to identify the events you want to count on your dashboard. Based on the search results create context items as stated in step 2. Also save the search to enable a monitor based on it for future context item creation. Context Item Creation: Each time an event happens, a context item is created to record the event via the search results or the monitor. This context item captures pertinent details such as the timestamp of the event and the day of the week as a keyword. Keyword Tagging: The day of the week is tagged as a keyword for every context item, facilitating easy categorization and

Red Hat Enterprise Linux 7 (RHEL 7) and the derived CentOS 7 are nearing their end of support. To ensure continued reliability and security for TrendMiner, we are transitioning TrendMiner to run on to the long-term supported Red Hat Enterprise Linux 9 (RHEL 9) and AlmaLinux OS 9 distributions. Infrastructure can already be prepared based on these new OS’s and the new system requirements. A new pre-packaged virtual appliance image based on AlmaLinux 9 has been made available for this purpose.Migration optionsThere are 2 options to migrate the existing server data to the new instance:Full migration including index data based on remote backup & restore (Azure Blob Storage or AWS S3 Bucket are the supported options) Work migration excluding index data based on local backup & restoreThe full migration is highly recommended as it will ensure the smoothest migration path with the least disturbances for end-users.Full details, with additional considerations, can be found in our documen

With dedicated individuals in each role, we maximize our chances of project success and organizational advancement. The successful implementation and continuous value from TrendMiner is tied to a strong project manager and change management practices guided by one of our Customer Success Managers (CSMs). ​ To ensure success, CSMs partner with three key roles: Sponsor, Project Manager, and Team Lead(s).​ The Sponsor sets the project's direction, ensuring alignment with organizational goals and providing clear priorities tied to business initiatives.​ The Project Manager drives the project forward, navigating its planning and execution while adhering to time, cost, and scope constraints.​ The Team Lead(s) focus on the human aspect of change, facilitating engagement and adoption of our TrendMiner platform.​  

 At the top of the page, click on the 'Events' button located to the right of the TrendMiner logo to view all our currently active events.Or you can click here to be redirected to the page.

BackgroundIn our batch process, ensuring optimal operating conditions is critical for maintaining production, product quality and worker safety. Monitoring various process parameters such as low average temperature, constant pressure, high end- concentration and low energy consumption in real-time is essential to maintain consistency and quality across batches. ChallengeOur challenge was to implement a comprehensive system for monitoring operating conditions in a batch process with 7 regularly changing recipes. Over the past month, we have experienced problems with parameters deviating from optimal process conditions, resulting in poor product quality. We needed a solution that would accurately monitor the trend profiles at each step of the process and alert operators when these deviations occurred. The alert should provide actionable insight for operators and supervisors to optimize production processes and minimize product loss. Solution1. Visualizing the trendsA list of tags was add