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Operating condition recognition of ball mill load is important to improve product quality, decrease energy consumption and ensure the safety of grinding process. A probabilistic one-against-one (OAO) multi-classification method using partial least square-based extreme learning machine algorithm (PLS-ELM) is proposed to identify the operating state of ball mill. The feature of shell vibration spectrum is extracted using KPCA. PLS-ELM model is applied to enhance the reliability and accuracy of the operating conditions identification of the ball mill load. Posterior probability of each class using Bayesian decision theory is defined as a measure as classification reliability. Classification results of the experimental ball mill shown that the accuracy and stability of the proposed method outperform ELM, PLS-ELM and KPCA-ELM model.

Template matching based on a Hausdorff distance (HD) approach become popular for object recognition. In this paper, we present a newly improved edge structure weighted HD (ESW-HD) algorithm for object recognition. We use edge points as the feature of the model, and construct the structure tensor by edge intensity and edge gradient. Then, the HD is weighted by the structure tensors. This work illustrates the ESW-HD algorithm by template edge matching which uses edge points and its edge adjacent structure information to perform the image matching. The experimental results show that the improved HD matching method can achieve a good performance level in terms of matching accuracy, even in a noisy environment when compared with the conventional approaches for object recognition.