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首页> 外文期刊>Minerals Engineering >Process working condition recognition based on the fusion of morphological and pixel set features of froth for froth flotation
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Process working condition recognition based on the fusion of morphological and pixel set features of froth for froth flotation

机译：基于泡沫浮选泡沫形态学和像素集特征的融合的过程工作条件识别

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摘要

Process condition recognition is an effective way to improve the froth process performance. In previous condition recognition algorithms based on machine vision in flotation process, the used features including gray value, bubble size distribution, load, etc., are essentially statistical results of gray level images and there is local bubble structure information loss in their extraction procedure. Meanwhile, the large number of image data are not adequately utilized. Thus, in this paper, deep neural network are used to extract the pix set features, and a two-step condition recognition method based on bubble image morphology and pixel set features is proposed, which utilizes the large number of image data. First, froth images are segmented into single bubble images. Next, the morphological feature vector of a single bubble image is extracted and classification labels are assigned to the single bubble images via K-means clustering. A large quantity of historical images is analyzed and labeled to train a convolutional neural network (CNN) by which the pixel set features of each bubble image are extracted. The morphological feature vector and pixel set features of the bubble images are then fused for bubble image clustering using the weighted mean-shift algorithm. The frequencies of various types of bubbles in a froth image are calculated to form a bubble frequency set for the froth image. A two-step working condition recognition strategy based on image sequence over a time period is then proposed. In this strategy, the bubble frequency sets of all froth images and those of the bubble images segmented from the froth images over the corresponding time period are matched with those of the images of typical flotation conditions in two main steps to determine the current working condition. Test results using industrial data demonstrate the high accuracy and calculation speed of the proposed method.
机译：过程条件识别是提高泡沫过程性能的有效方法。在基于浮选过程中的机器视觉的先前条件识别算法中，包括灰度值，气泡尺寸分布，负载等的使用特征是灰度图像的统计结果，并且在其提取过程中存在局部气泡结构信息丢失。同时，不充分利用大量的图像数据。因此，在本文中，建议使用深神经网络来提取PIX集特征，并且提出了一种基于气泡图像形态和像素集特征的两步条件识别方法，其利用了大量的图像数据。首先，泡沫图像被分段为单个泡沫图像。接下来，提取单个气泡图像的形态学特征向量，并通过K均值聚类将分类标签分配给单个泡沫图像。分析大量历史图像并标记以训练卷积神经网络（CNN），通过该卷积神经网络（CNN）提取每个气泡图像的像素集特征。然后使用加权平均换档算法将气泡图像的形态特征向量和像素集特征融合用于气泡图像聚类。计算泡沫图像中各种类型的气泡的频率以形成用于泡沫图像的气泡频率。然后提出了一种基于图像序列的两步工作条件识别策略。在该策略中，所有泡沫图像的气泡频率集和从相应的时间段从泡沫图像分段的气泡图像与两个主要步骤中的典型浮选条件的图像匹配以确定当前的工作状态。使用工业数据的测试结果证明了所提出的方法的高精度和计算速度。

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