Fault Detection and Isolation Intelligent Data Analysis
Biography
Conveyor belt systems are widely used in many industrial sectors, such as mining, manufacturing, logistics, and material handling. Their unexpected failures cause production downtimes, possible damages of machines, maintenance costs, and even safety issues. The traditional approaches to maintenance management usually imply a manual inspection, a plan of maintenance works, and alarms triggering when some thresholds are exceeded that, generally, are not able to detect possible faults timely. In this project, the Smart Conveyor Belt Monitoring System, which allows realizing a software-based continuous monitoring of the conveyor’s operating conditions through the simulation of sensor data, is designed. The parameters, such as the conveyor belt’s speed and temperature, the level of its vibration, and the electric current of its motors, are simulated and analyzed to identify abnormal working conditions of the conveyor. The system’s fault detection and notification component allows classifying the conveyor state and even recommending some maintenance works. The web interface displays information about the current sensor data, machine state, warnings, historic and analytical information. The described system is suitable as an example of how artificial intelligence and data analytics help to realize a predictive maintenance without involving any physical conveyor in the development process.
