Data model in "Asset Manager"

This chapter describes the data model of Asset Manager within MindSphere. The data model will show you what processes are necessary to connect and use your asset data in MindSphere.

Definition asset

An asset is a digital representation of a machine or an automation system with one or multiple automation units (e.g. PLC) connected to MindSphere.

MindSphere data collection and data provisioning is based on so called (virtual) assets. This can be anything like a pump, motor, PLC, an entire tool machine, a production line, a robot, a crane, a car, a wind turbine and so on. The data of an asset is collected and sent to MindSphere to make that data available for further processing and analytics.

Definition aspect

Aspects are a data modeling mechanisms for assets. Aspects group related data points based on their logical association. For example: The pump skid has an aspect e.g. "Energy_consumption" that contains the data points: "power", "current", "voltage" etc. Aspect is specified in Asset Manager and its name can be freely chosen, but should have conjunction to datapoints and a physical asset. An aspect can consist of several variables.

Data model

The following graphic shows the data model in Asset Manager:

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In Asset Manager devices like MindConnect Nano are defined as the data source. The device sends data points to MindSphere. These data points must be connected to the aspects and variables. Asset Manager uses aspects and variables as data containers.

Within MindSphere you add data points to a data source to collect the data, for example from a control unit. In the next step you have to link the data points to the respective variables of an aspect.

To enable data connecting and in order to use your data in MindSphere you have to fulfill the following processes:

  • Onboarding: Onboarding is the process of attaching a MindConnect Element to MindSphere.

  • Data mapping: Data mapping matches variables of an aspect with the respective data points of a data source.