Fault diagnosis method of mining vibrating screen mesh based on an improved algorithm

Abstract

Artificial intelligence fault diagnosis technology based on machine vision, due to its low cost and high efficiency, has become an indispensable part of production processes across various industries. Compared to traditional fault diagnosis methods, artificial intelligence diagnosis of common mechanical failures, such as ‘clogging’, ‘wear’, and ‘breakage’ in vibrating screen meshes within the mining screening sector, improves detection efficiency, accuracy, and sustainability. Since small target faults in large screening areas are challenging to detect through manual diagnosis, it reduces screening efficiency and shorter equipment lifespan, negatively impacting mining enterprises' safe and efficient production. A fault diagnosis model with a better speed-precision trade-off is proposed to improve detection precision based on the You Only Look Once version 5 single-stage object detection algorithm. This model is optimized in feature extraction and fusion by integrating autocode masking, re-parameterization, and omni-dimensional attention. The model's performance is primarily evaluated using precision, recall, balanced score, and mean average precision. The improved algorithm achieves a precision of 97.2%, a recall of 93.3%, a balanced score of 95.21%, and a mean average precision of 97.0%. Experimental results demonstrate that the improved algorithm increases the mean average precision by 3.1% compared to the original model. The results show that the improved algorithm is more effective than the original in fault diagnosis, with enhanced screen mesh detection precision. Thus, it ensures production safety and stable screening efficiency. Moreover, the proposed algorithm provides a reference for advancing intelligent and efficient fault diagnosis technology in the mining screening field.