Minimizing Unplanned Downtime in Rotary Vacuum Drum Filters for Iron Ore Mining through Image-based Analysis

In iron mining the processing phase broadly consists of sorting, concentrating, and pelletizing of the iron ore, this is to increase the iron content in the final product. In pelletizing, the filtering stage which controls the moisture content in the iron cake plays a crucial role. A Rotatory Vacuum Drum Filter (RVDF) is one of the mining equipment for removing excessive moisture by separating solid iron cake from slurry. A supporting wire which holds the cloth mounted on the frame of the RVDF is one of the critical components. During operation, recursive compression and stretching due to variation in pressure may lead to wire failure. This failure significantly impacts the integrity and efficiency of filter cloth that affects the filtration performance. If the wire failure is not detected promptly, it can lead to prolonged maintenance time, substantial maintenance cost and unplanned downtime, consequently affecting system availability. This work demonstrates health monitoring of filtering system in mining, designed to alert the operators about the emerging failure, to take appropriate maintenance action and minimize further damage, and unplanned downtime. This paper introduces a computer-vision based monitoring approach that leverages image data of the drum filter during operation. The proposed approach identifies wire-induced degradation pattern on the filter cloth. Extracted video frames from the drum filter are processed to isolate the region of interest. Using Hough transform horizontal sections of the drum are detected followed by a sliding window analysis to evaluate the variations in pixel intensity. For normal surface, the average intensity variations remain low, typically ranging from 5 to 10. However, it spikes up to around 40 when irregular patterns are detected. The focus of this work is on detection and diagnostics, a transition towards prognostics is envisioned by incorporating pressure sensor data. Integrating multi-modal data may enhance the capability of predicting failure and improve the system availability.