Monitoring the Performance of Smoker Machines
We implemented a comprehensive monitoring system to evaluate the performance of smoking machines within a laboratory setting. These smoking machines analyze critical cigarette parameters, including puff count, carbon monoxide (CO) levels, nicotine content, sugar content, and tar content.
Machine activity is tracked via log files containing various sensor readings related to cigarette parameters. However, since not all log file data is relevant for analysis, we introduced an IoT Box featuring a Raspberry Pi. This IoT Box is programmed specifically to filter essential data and transmit it directly to a local database server. Additionally, the IoT Box includes a 7-inch touchscreen interface, enabling operators to conveniently adjust parameters and receive error notifications in real-time.
For effective messaging and data streaming, RabbitMQ was utilized as the messaging broker. Node-RED facilitated seamless integration within the IoT infrastructure, connecting hardware devices, APIs, and online services. Given the customer’s preference for an on-premise solution, we deployed a local Microsoft SQL Server database to store and manage data securely. The robust and efficient Node.js framework was chosen for its capability to handle high volumes of incoming requests from IoT sensors. To provide intuitive and insightful data visualization, Grafana was employed, offering clear performance insights into machine operations.
Tools used:
Hardware Development: IoT Box
Database Development: Microsoft SQL Server
Server: Linux Ubuntu
Back End Application Development: Node JS
Front End and Mobile Applications: Grafana
IoT Integration: RabbitMQ and Node-Red
Monitoring Water Pump
A local water company faced challenges in effectively controlling and monitoring their water pumps with their existing system. This project aimed to upgrade their systems, equipping the company with advanced tools to control and monitor their pumps more efficiently. Figure 1 illustrates the upgraded system.
Data such as voltage, current, frequency, and RPM were obtained directly from the pump’s built-in sensors. Additionally, temperature and vibration data were collected using custom-designed sensors attached to the pumps.
Each pump is managed by a PLC controller connected via a Variable Frequency Drive (VFD). The PLC controller, featuring integrated IoT capabilities, transmits sensor data directly to an MQTT server. Furthermore, we established a database and web application server to enable real-time visualization of sensor data through a mobile application, as shown in Figure 2, or via a desktop interface accessible over the internet. Historical data from the previous reporting system was also integrated into the new reporting platform.
Tools used:
Hardware Development: IoT PLC Controller
Database Development: SQL Server
Server: Linux Ubuntu
Back End Application Development: Node JS
Front End and Mobile Applications: Quasar
IoT Integration: RabbitMQ and Node-Red