Industrial IoT Preventive Maintenance Demo Application
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This application is distributed in its binary form. If you would like to download the source code and build the application on your own, please contact us.
What to expect
How to install
Download for Linux:
Download for Windows 10:
Note that for Windows 10 it might be necessary to download and install the Visual Studio 2015 redistributable packages for your architecture.
Download for MacOS:
How to install the Industrial IoT Preventive Maintenance demo
1. Download and extract the demo application archive for your platform of choice (Linux, Windows, MacOS)
2. On Windows and MacOS extract the IoTVisualDemoApp*.zip. On Linux, expand the tarball
3. Run demo.py (./demo.py on Linux and/or MacOS or point and click on the demo.py on Windows)
• Python must be installed on the system. The program can be downloaded by clicking here.
4. On Windows or MacOS simply click on the IoTVisualDemoApp.exe (IoTVisualDemoApp) application icon
5. If desired, the GUI application can be run. To start the application on Linux please make sure that file has proper permissions:
• chmod +x IoTVisualDemoApp.AppImage
In the pulp and paper industry, preventing problems is infinitely better than fixing them. Over half of all equipment malfunctions on control systems, scanners and other equipment located throughout the plant are caused by the lack of preventative maintenance. As in any industry, equipment repairs are costly and result in production downtime and profit cuts. Therefore, manufactures strive to keep these unscheduled repairs to a minimum or even prevent them entirely.
To achieve this, production line maintenance is carried out regularly — monthly or annually. In addition, ideally, equipment is monitored while operating to detect such things as vibration, incorrect operating pressures and leaks, loose connections, etc., which cannot be assessed easily on stationary equipment. Sensors placed throughout the paper machine and control systems as part of preventive maintenance programs help detect faults early and avoid loss of revenue and profits due to unforeseen machine stoppage.
This Industrial IoT database demo application simulates the process described above. The output from the sensors (Temperature, Humidity and Vibration Frequency,) is collected in eXtremeDB databases and transmitted through the eXtremeDB Active Replication Fabric to a back end server for analysis.
The console interface provides a set of commands to start, stop, add and remove devices and gateways, perform IoT-related operations, modify configuration parameters and display values from the database. The graphic interface displays values generated and updated at the device nodes then propagated from devices to gateways and a server; and it allows the user to modify configuration parameters affecting the system performance.
The eXtremeDB Active Replication Fabric provides APIs for implementing automatic and on-demand data exchange between collection points, gateways and servers. IIoT network nodes’ databases are made available to the application through the eXtremeDB REST API.
Under the hood of this Industrial IoT Database
This demo illustrates a three-level IIoT topology running three types of components:
- A single top-tier server (`iot_demo_server`) maintains a persistent database and collects data from all the devices.
- Two mid-tier routers/gateways (`iot_demo_router`) maintaining in-memory databases and connected to the server. The routers provide a transparent data flow passing all sensor data to the server.
- Eight low tier devices (`iot_demo_sensor`), connected to the gateways (each gateway has 4 devices connected). Each device generates output values for temperature, humidity and vibration and stores these values in its local in-memory database, periodically sending collected data to the connected gateway over the Active Replication Fabric protocol.
The IIoT demo application contains back-end components that implement the server, the gateways and the device applications and a front-end component that implements the graphical interface to the back-end processes. The server components are written in C/C++ and use the eXtremeDB interfaces (API) to store and transmit data between nodes. The back-end application implements a console-based user interface that allows controlling the data flow between all back-end components as well as simulating various back-end events such as connecting and disconnecting devices, gateways and servers, changing various timeouts, etc. The graphical component connects to the back-end processes through HTTP, visualizes the IIoT nodes topology and controls the back-end processes and their data flow through a graphical user interface. The GUI client is implemented as an Electron-based application (see https://electronjs.org).
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