A Mixed-Feed Identification Method for Sizer Crusher Based on Squeeze-and-Excitation Residual Network

In mineral engineering, harmful foreign objects mixed in the feed can cause faults in the sizer crusher (Sizer). The traditional feed identification methods are inefficient and manual, which fail to adapt to the present production conditions of high belt speed, thick material layer and large throughput. In previous studies, Sizer feed identification focused on single-component rather than mixed feeds. Therefore, we proposed a new Sizer mixed-feed identification method based on continuous wavelet transform (CWT) and squeeze-and-excitation residual network (SE-ResNet). Firstly, the three-channel sensor signals acquired during the Sizer feeding process were subjected to CWT, and the resulting scalograms were fused as the image dataset. Secondly, a Sizer feed identification model based on SE-ResNet was designed for single-component and mixed-feed classification. Ablation studies and method comparisons were conducted to validate the performance of the proposed model. Finally, the method of this paper was tested with industrial datasets to demonstrate its effectiveness and generalizability. The results show that the proposed method can achieve an accuracy of 85.99±0.16% for Sizer mixed-feed identification. This method also significantly improves the identification ability of wood, which solves the problem of distinguishing between coal and wood in the feed. Besides, industrial tests suggest that our method has good Sizer mixed-feed identification performance with an average accuracy of 95.64±1.77%, which can meet the demand of industrial production for the safe and smooth operation of the Sizer.