Leading analytics for operation and maintenance
Visplore is an easy-to-use software for advanced self-service analytics on process and asset data. It speeds up root-cause analysis by 90%, detects anomalies in complex dynamic patterns such as start-ups, and enables operators as well as reliability engineers to make data-driven decisions instantly.
Visplore is trusted in power generation worldwide
“Pattern anomaly detection and fast correlation with contextual data empowers the engineers to make data-driven decisions that save millions of Euro.” Thor Arne Hvam Bruun, Turbine Specialist, Hafslund Eco
Your affordable head start for digital transformation
Visplore bridges the gap between subject matter expertise and data science. Enable your operators to use data independently for decision-making and optimization like never before. For reducing downtime, and operating as efficiently as possible.
Visplore gives you the best RoI at less than 20.000€ per year!
How it works: Advanced asset health analytics made transparent
Visplore is not yet another black-box AI. Instead, Visplore delivers unexpected findings by making processes transparent, and empowers your operators and reliability engineers to set up advanced analytics based on their experience – covering all steps to automated results.
Connectivity, suitable for your IT landscape
Visplore provides plug-and-play connectivity to dozens of different data sources, enabling integrated analyses across multiple sources. As official partner of OSIsoft/AVEVA, Visplore provides direct connectivity to the PI Data Archive and PI Asset Framework (AF). Or connect to SQL data bases, Influx, data warehouses, and various file formats.
For custom integrations and processing, Visplore connects to any source that offers a Python API (e.g. custom file formats, REST interfaces, legacy databases, …)
Visplore is fine with on-premise – no data has to leave your site!
Visplore is deployed on your existing infrastructure and connects to your existing data sources. No data has to be transferred to the cloud.
Use case example: Downtime of hydro turbine reduced by 50%
Comparing starts of a hydro turbine of Norwegian hydropower producer Hafslund Eco revealed anomalous temperature behavior of a bearing. Further analysis excluded ambient conditions as root-cause. This led operators to the data-driven decision to repair.
- 3 weeks maintenance in off-season instead of 6 weeks in expensive season
- 1m EUR revenue loss avoided
(1) Sudden increase of bearing temperature is observed, rising further even after multiple restarts. (2) engineers compare historical start-ups to confirm incident as anomaly, and (3) quickly rule out ambient conditions as a “natural” explanation, as the internal cooling water is not nearly heating up as much. (4) After deciding for maintenance, the behavior is improved with a maximal temperature that is 10°C lower than before the problem occurred.
Talk to our experts how you can make a leap forward in your digitalization journey!
