Automatic fault detection system for mining conveyor using distributed acoustic sensor

Condition monitoring of mining conveyor is a highly essential task to ensure minimum disruption to the mining operational system. Failure of one or more conveyor components can result in significant operational downtime, economic loss, and safety risks. The current monitoring method still involves subjective measure from maintenance engineers, where at some cases, fault can be left undetected and leads into site incident. Therefore, there is a high demand for real-time condition monitoring technology to detect early fault on conveyor. In this study, the effective application of distributed optical fibre sensor (DOFS) was explored for long distance real-time condition monitoring of mining conveyor. The fault detection framework was developed by integrating and modifying the Isolation Forest algorithm to analyse optical signals for effective detection of defective idlers. Further, the optical signal was analysed to extract the damage progression of defective idler with time and space. The results were used to classify various levels of damage and to set appropriate damage thresholds. Also, software interface, that can be used to set the sensing parameters, to collect, analyse, and visualise the signal in real-time, was developed. Finally, the developed condition monitoring system was used to monitor a 1.6 km long section of a conveyor structure in Western Australia for a period of 10 months. The results and findings from the field monitoring were presented together with automated fault detection framework for condition monitoring of mining conveyor.

Automated summary

The study focused on utilizing distributed optical fibre sensors for real-time condition monitoring of mining conveyor, where a fault detection framework was developed to analyze optical signals for defective idlers. The system was successfully tested on a 1.6 km section of conveyor in Western Australia, demonstrating efficient fault detection and damage progression tracking.