Amazon Web Services
The combination of TrendMiner and AWS, helps accelerate initiatives like industry 4.0, sustainability and analytics-enabled operations. With its plug-and-play approach, TrendMiner can be deployed on AWS’s scalable infrastructure and can be found on the AWS Marketplace. TrendMiner is the perfect solution for industrial analytics on top of AWS solutions like IoT SiteWise and Timestream and its functionality can be extended with AWS services like Lookout For Equipment and Sagemaker.
Overview
Featured
Connectivity
AWS IoT SiteWise
With plug-n-play connectivity to AWS IoT SiteWise, users can bring in the asset hierarchy, sensor data and alarms from the AWS service to TrendMiner to bring it closer to the people in production.
When connected, users will be able to navigate the asset hierarchy. Visualize, navigate and analyze historical sensor data and use it to build models, predictions and to contextualize the data. Use TrendMiner as a place to follow up on alarms defined on IoT SiteWise and give users the ability to visualize the underlying timeseries data attached to the alarm in a few easy clicks or present them in the dashboards and reports with the rest of their data.
Amazon Timestream
The connector for Amazon Timestream makes the data from this timeseries database accessible in TrendMiner. Users can search for signals from Timestream by their name in TrendMiner to add them to their view to navigate and analyze historical sensor data, use it to build models, predictions and to contextualize the data.
Machine learning
Amazon Sagemaker
If you are already invested in building your models in Sagemaker in stead of TrendMiner’s MLHub, you can bring your Sagemaker models into TrendMiner as well to use the output of your model in the platform for monitoring, bringing in the predictions or early warnings. TrendMiner’s diagnostic capabilities make it easy to test/validate your models.
Predictive maintenance
Amazon Lookout for Equipment
Use the APIs from Lookout for Equipment to bring the results of your trained ML model’s early warning to TrendMiner, so the people closest to the process can be brought into the loop and can start the investigation to the root cause of the problem right from within TrendMiner.
TrendMiner gives you a great environment to display the early warnings as Context Items. Making them visible on top of the timeseries data, on dashboards and ready for deeper statistical analyses to use for reporting on MTBF (mean time between failure), improving asset uptime and comparing the health of multiple assets.
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Blog Posts
Low-code and No-code AI & Analytics Solutions Yield High-Value Use Industrial Use Cases13 September 2024 - 15:19- Building a Foundation for AI & Analytics with an Industrial Data Fabric30 August 2024 - 23:12
- TrendMiner’s 2024.R2 Release; A Complete Platform to Facilitate Event Analytics and Operationalize Industrial Data8 August 2024 - 16:38
