MindSphere is the cloud-based IoT open operating system from Siemens. It connects your products, plants, systems and machines, enabling you to harness the wealth of your data with advanced analytics. In addition, it gives you access to a growing number of apps and the Mendix low-code app delivery platform. By leveraging MindSphere you will get a tremendous amount of Big Data. The question is: what are you going do with it?
TimeSeries leverages the Mendix platform and open-source technologies to not only rich web and mobile interfaces and visualizations of this data, but more importantly, we create full-stack Smart Applications. Smart Applications are not only realtime, but also proactive. You can rapidly setup alerts and trigger workflows and action items inside and outside the organization based on volumes of data and logic.
TimeSeries can support your IoT and Big Data initiatives, whether these are large number of assets or actually high volume of data points per asset.
Several examples of industrial MindSphere use cases:
- Visualizations and User Interfaces on top of MindSphere
- Orchestration on top of MindSphere, combined with other data sources, such as Teamcenter, SAP and Salesforce.
- Rich smart applications that go beyond business intelligence by being proactive and interactive. As an example, a wind farm not only having real-time visualizations about the performance of the windmills, but also provide proactive alerts, dispatching and scheduling maintenance events.
The 3-minute video below shows a use case related to elevators where IoT and big data technologies are applied. Elevators are often maintained based on fixed schedules and the usage of elevators inside a building can differ greatly.
Several features and functionalities in this template include:
- Determine elevator usage, independent of brand and type of elevator without structural changes to the elevator itself
- Measure duration, speed and direction for each ride
- Predict maintenance moment based on actual usage
- Determine best time of maintenance, limiting the amount of disruption for the customer
As a result of using IoT and this template there are less asset breakdowns due to timely maintenance and cost savings in maintenance of at least 1,5 maintenance jobs per elevator per year, which saves 37,5% on maintenance costs per year. Machine Learning is applied to predict breakdowns and track lifespan of individual components.
TimeSeries has delivered this application to demonstrate the value that IoT and Big Data offers. Although for this demo MindSphere has not been used yet, it is a great example where such a powerful platform can be applied.