Der Einsatz unbemannter Flugsysteme zur Charakterisierung von gesprengtem Haufwerk

Die erreichte Zerkleinerung und die Form des Haufwerks sind die beiden wichtigsten Ergebnisse einer Tagebausprengung. Schnelle Informationen über die Eigenschaften des gesprengten Haufwerks ermöglichen eine zielgerichtete und effiziente Produktionsplanung und Kenntnisse über die erreichte Zerkleinerung ermöglichen außerdem Anpassungen in der weiteren Zerkleinerungskette. Durch den Einsatz von UAVs (unmanned aerial vehicles) gemeinsam mit modernen Algorithmen aus dem Bereich Computer Vision und des maschinellen Lernens soll eine schnelle Erfassung und Interpretation der Daten bei gleichzeitiger Integration in die herkömmlichen betrieblichen Abläufe ermöglicht werden, und außerdem können Schwächen bodengebundener Systeme hinsichtlich Vollständigkeit und Repräsentativität umgangen werden. Im vorliegenden Beitrag wird einerseits auf den relevanten Stand des Wissens und der Technik eingegangen und andererseits wird die verfolgte Stoßrichtung bei der Systementwicklung dargelegt sowie erste Arbeiten präsentiert.The fragmentation and the shape of the muck pile are the two major outcomes of open pit mine and quarry blasts. Fast information about the muck pile properties will help to improve the production scheduling and furthermore this information could be used to optimize the blasting patterns of future production blasts. The combined use of unmanned aerial vehicles (UAVs) and modern machine learning and computer vision systems offers a new way of acquiring spatial data to determine on-site fragment size distribution, while at the same time enabling integration into common work flows and mitigating the weaknesses of ground-based systems with special regard to completeness and representativeness. In the present paper, we will discuss the relevant related work, present the planned path for system development and give examples of first work

High speed videometric monitoring of rock breakage

Estimation of rock breakage characteristics plays an important role in optimising various industrial and mining processes used for rock comminution. Although little research has been undertaken into 3D photogrammetric measurement of the progeny kinematics, there is promising potential to improve the efficacy of rock breakage characterisation. In this study, the observation of progeny kinematics was conducted using a high speed, stereo videometric system based on laboratory experiments with a drop weight impact testing system. By manually tracking individual progeny through the captured video sequences, observed progeny coordinates can be used to determine 3D trajectories and velocities, supporting the idea that high speed video can be used for rock breakage characterisation purposes. An analysis of the results showed that the high speed videometric system successfully observed progeny trajectories and showed clear projection of the progeny away from the impact location. Velocities of the progeny could also be determined based on the trajectories and the video frame rate. These results were obtained despite the limitations of the photogrammetric system and experiment processes observed in this study. Accordingly there is sufficient evidence to conclude that high speed videometric systems are capable of observing progeny kinematics from drop weight impact tests. With further optimisation of the systems and processes used, there is potential for improving the efficacy of rock breakage characterisation from measurements with high speed videometric systems

Estimating size fraction categories of coal particles on conveyor belts using image texture modeling methods

Motivation: Physical properties of coal such as particle size distribution have a large influence on the stability and operational behavior of fluidized bed reactors and metallurgical furnaces. In particular, the presence of large amounts of ‘‘fine’’ particles invariably has a drastic effect on plant performance as a result of impaired gas permeability characteristics of the coal or ore burden. Therefore, monitoring and control of particle size distribution profiles of such aggregate material on reactor feed streams, such as moving conveyor belts, is critical for predictable operation of these processes. Traditionally, the method of sieve analysis using stock or belt cut samples has been widely used in industry. Unfortunately, the reliability and usefulness of belt cut techniques are constrained by frequency of sampling as well as laboratory analysis turnaround times. For real-time monitoring and control purposes, automated sampling and analysis methods are more desirable. Methods: In this study, the problem of estimating the particle size distribution profile of material on a moving conveyor belt is formulated within a texture classification framework, which has its basis in machine vision and incorporates elements from statistical pattern recognition. Using exemplar images of coal particles taken on a process stream, a set of local features that compactly describes the textural properties of each image are expressed in terms of localized nonlinear features called textons.Representation of image information using textons is primarily motivated by insights from neuroscience research on the optimality of linear oriented basis functions as models of perception in early processing of visual information in the cortex regions of the human brain. Using these representations for different textures, nearest neighbor and support vector machine classification models are subsequently used to classify test images. Results: Using a comprehensive evaluation, it is shown that the use of texton representation obtained from decomposing images with linear oriented basis functions can be sufficiently discriminative compared to the use of the widely used second-order statistical features or features from other baseline models. In particular, model performance obtained with appropriately tuned filters suggest the importance of including shape and spatial structure information in an image representation for texture classification of coal particles. Furthermore, using nonlinear support vector machines rather than nearest neighbor classifiers significantly improved classification performance. A texture classification approach to particle size profile estimation has potential applications in the online monitoring of the proportion of ‘‘fines’’ in coal material on moving conveyor belts

Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches