Research on the Monitoring System of Belt Conveyor Based on Suspension Inspection Robot

The paper discusses problems that occur in robotic arm control. The specific problems arise due to the wear of various types of gears (in the presented case, belt gear and worm gear). It is important to note that such errors need to be diagnosed in time, and the way of their elimination has to be determined, which should be resource-intensive and cost-effective. This article describes the basic types of robotic manipulators (robotic arm, telpher and Cartesian), presenting a review and study of the possibilities of errors in the movement of a robot, adjustment of a mechanical system, and determination of a strategy for solving the emerging problems. A comparison between various types of gear faults is also provided. Different ways of diagnosing faults are discussed, based on the advantages and disadvantages of the methods. The main objective of this study is to provide a complete overview of the mechanical areas where disturbances occur, their diagnosing, and methods of their elimination.

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“…However, the belt gear is also used to transmit tractive effort over long distances, e.g. conveyor-type machines [28,29].…”

Section: Belt Gearmentioning

confidence: 99%

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Autsou¹,

Kudelina²,

Rassõlkin³

et al. 2022

PEAS

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“…Cao et al [ 17 ] proposed the usage of suspended robots to evaluate belt conveyor rollers’ thermal conditions. The robot contains sensors and a thermal camera to perform the required tasks.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Approach at the Edge to Detect Iron Ore Type

Klippel

Bianchi

Delabrida

et al. 2021

Sensors

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There is a constant risk of iron ore collapsing during its transfer between processing stages in beneficiation plants. Existing instrumentation is not only expensive but also complex and challenging to maintain. In this research, we propose using edge artificial intelligence for early detection of landslide risk based on images of iron ore transported on conveyor belts. During this work, we defined the device edge and the deep neural network model. Then, we built a prototype will to collect images that will be used for training the model. This model will be compressed for use in the device edge. This same prototype will be used for field tests of the model under operational conditions. In building the prototype, a real-time clock was used to ensure the synchronization of image records with the plant’s process information, ensuring the correct classification of images by the process specialist. The results obtained in the field tests of the prototype with an accuracy of 91% and a recall of 96% indicate the feasibility of using deep learning at the edge to detect the type of iron ore and prevent its risk of avalanche.

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“…As mentioned, due to the number of idlers located along the conveyor, there is a need to have a method for quick and automatic acoustic measurement and analysis. Thus, there are intensive works on inspection robots equipped with various sensors and data acquisition systems, including sound recording [5,6,8,9,[49][50][51]. In the paper, we propose a combination of robotics inspection, acoustic data measurement, and finally signal processing for fault detection in idlers.…”

Section: Introductionmentioning

confidence: 99%

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

et al. 2021

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Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.

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Research on the Monitoring System of Belt Conveyor Based on Suspension Inspection Robot

Cited by 18 publications

References 1 publication

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Analysis of possible faults and diagnostic methods of the Cartesian industrial robot

Deep Learning Approach at the Edge to Detect Iron Ore Type

Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler

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