Fault condition detection for a copper flotation process based on a wavelet multi-scale binary froth image

Abstract

Considering the difficulty of detecting the fault condition of copper flotation in real-time, a new fault condition detection method based on the wavelet multi-scale binary image is proposed. Firstly, the froth gray image is decomposed into approximation sub-images and detailed sub-images by wavelet transformation, whereby the approximation sub-images of different scales are restructured and binarized. Then a new feature that is directly related to froth morphology, namely the equivalent size feature, is obtained by calculating the white area of each binary image according to the space-frequency relationship of a two-dimensional wavelet transformation. After this, the equivalent size distribution of the froth image can be obtained through the equivalent size feature. At last, the equivalent size distributions of different froth images are compared in order to classify the froth images under different flotation conditions. Experiment results, together with the industrial field data, show that this method can simply and effectively detect fault conditions in the copper flotation process.