## 3 Software Solutions
### Custom SW Tool
#### 3.3.2 Custom Visualisation SW Tool  
In the WATREYE project [10], measurements containing corrosion status information are generated using mobile and fixed sensors. These data are stored in a secure database, accessible through a Representational State Transfer application Programming interface (REST-API). A visualisation tool is needed to support O&M experts to understand the status of an offshore wind turbine, based on gathered measurement data. To this end, a visualisation SW tool was developed according to the criteria developed defined in Section 2.  
This framework was used to develop a custom web interface, which takes (maintenance staff) user input, queries a REST-database to fetch the measurement and prediction data from a windfarm database and subsequently presents the data on a custom pv-dashboard. The user can interact with these data, either visually (in 3D or 2D), or by downloading the underlaying data and processing it offline.  
This combination results in the following architecture, depicted in Figure 3. It was based on pvbrowser, but with modified architecture, as explained in Figure 2b. This SW tool is capable of:  
* Querying measurement and geometric data from the database using a REST-API, based on user inputs in the GUI.
* Pre-processing these data, merging the inputs from different sources into key performance indicators (KPI's) such as relative corrosion rate (mm/y).
* Exporting these data toward data-files and image.
* Visualising these data in interactive 3D and 2D visualisations.  
Through the browser client, the user can access and interact with the data, using a browser-like application. The user can use the software, agnostic of the architecture or changes at server side.  
The server architecture allows us to centrally manage and adapt both the data and visualisation output from different windfarms and users.  
Based on the chosen parameters, the server component makes a query to the data-server, parses the data and presents them to the user in predefined widgets in the browser side (see Figure 4). There, the user can interact with the data (zoom, query, rotate) to increase his/her understanding of spatial and time relationships, or adapt the query for additional data.  
Figure 3: Architecture of custom SW tool, with main components: QT-based (customised) pyserver (orange box) and VTK-enabled pvbrowser (blue box), fetching data from a database (grey box).  
The core functionalities of this SW tool are:  
* Initialisation, with connection to the database, and auto-populating of the dropdown menus for all wind turbines and sensor data available in the database.
* Selection of a wind turbine to present in 2D and 3D views.
* Selection of parameters, such as attributes and sensors to visualise and time period of interest.
* Modularity and expandability, for future functionality (as described in following section).  
Any change in the parameters above, triggers the visualisation of:
* The 3D visualisation of the sensor values at each sensor position on the wind turbine, as shown in Figure 5a,b.
* The 2D or 3D visualisation of derived the wall thickness loss/defect features or relative thickness.
* The 2D visualisation of a time series of a selected attribute, at a selected position, over the selected time period, shown in Figure 5c.  
The user-friendliness and responsiveness of the tool is further enhanced by enabling 3D-panning, rotating and zooming, sensor highlighting, 2D-point picking and informational toolti  
Figure 4: Example of the browser window of our SW-tool. It consists of three areas: user input (red box), a 3D visualisation area (green box) and a 2D time series visualisation (blue box). All widgets are interactive and responsive, as explained in Section 3.3.2.