Operational state detection in hydrocyclones with convolutional neural networks and transfer learning

Abstract

In the mineral processing industries, the performance of hydrocyclones used in solids classification tasks influences the efficiency of downstream processing. Typical operation involves a fan shaped underflow profile, while undesirable roping and underflow blockage conditions result in excessive coarse particles in the overflow and have visually different underflow characteristics. Different approaches were investigated previously as potential sensing options for undesirable state detection, and while some have been commercialised, they currently have not found widespread implementation in the industry. Analysis of hydrocyclone underflow by use of image analysis has been one method investigated, in which it has been shown that meaningful features can be extracted for operational state detection.Deep convolutional neural networks have recently led to significant advances in computer vision tasks. In addition, the use of transfer learning making it possible to leverage this improved performance on smaller datasets. Video frames of the underflow of a laboratory hydrocyclone were used to produce a three-state (fan/rope/blocked) classifier, via transfer learning with a pretrained convolutional neural network feature extractor, which was able to produce meaningful state detection with the ability to handle considerable noise in the images.A dual state (fan/rope) classifier was also developed using largely industrial hydrocyclone underflow video frames, again via transfer learning with a pretrained convolutional neural network with both support vector machine and neural network based classification investigated. This study demonstrates the robust ability of deep convolutional neural networks to extract meaningful features from hydrocyclone underflow images without requiring significant image preprocessing. This is a major advantage over previous methods that rely on engineered features, and which may be more susceptible to changing environmental conditions during operation.