An inspection robot using infrared thermography for belt conveyor

2016 ◽  
Author(s):  
Wenjuan Yang ◽  
Xuhui Zhang ◽  
Hongwei Ma
Keyword(s):  
Infrared Thermography ◽  
Belt Conveyor ◽  
Inspection Robot

Slip recognition and fuzzy control of belt conveyor inspection robot

2021 ◽  
Author(s):  
Jidai Wang ◽  
Yongchao Li ◽  
Aiqin Sun ◽  
Yunxia Wang ◽  
Xiaoluan Lv ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Belt Conveyor ◽  
Inspection Robot

scholarly journals Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yi Liu ◽  
Changyun Miao ◽  
Xianguo Li ◽  
Guowei Xu
Keyword(s):  
Deep Learning ◽  
Template Matching ◽  
Region Of Interest ◽  
Conveyor Belt ◽  
Detection Algorithm ◽  
Outer Edge ◽  
Detection Accuracy ◽  
Belt Conveyor ◽  
Inspection Robot ◽  
Edge Points

The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest (ROI) containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD (OM-SSD). Secondly, Hough line transform algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree (DD) of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor.

scholarly journals Belt Conveyors Rollers Diagnostics Based on Acoustic Signal Collected Using Autonomous Legged Inspection Robot

2021 ◽  
Vol 11 (5) ◽  
pp. 2299
Author(s):  
Artur Skoczylas ◽  
Paweł Stefaniak ◽  
Sergii Anufriiev ◽  
Bartosz Jachnik
Keyword(s):  
Acoustic Signal ◽  
Raw Materials ◽  
Point Of View ◽  
Belt Conveyor ◽  
Filtering Method ◽  
Inspection Robot ◽  
Belt Conveyors ◽  
Difficult Challenge ◽  
Autonomous Inspection

Growing demand for raw materials forces mining companies to reach deeper deposits. Difficult environmental conditions, especially high temperature and the presence of toxic/explosives gases, as well as high seismic activity in deeply located areas, pose serious threats to humans. In such conditions, running an exploration strategy of machinery parks becomes a difficult challenge, especially from the point of view of technical facilities inspections performed by mining staff. Therefore, there is a growing need for new, reliable, and autonomous inspection solutions for mining infrastructure, which will limit the role of people in these areas. In this article, a method for detection of conveyor rollers failure based on an acoustic signal is described. The data were collected using an ANYmal autonomous legged robot inspecting conveyors operating at the Polish Ore Enrichment Plant of KGHM Polska Miedź S.A., a global producer of copper and silver. As a part of an experiment, about 100 m of operating belt conveyor were inspected. The sound-based fault detection in the plant conditions is not a trivial task, given a considerable level of sonic disturbance produced by a plurality of sources. Additionally, some disturbances partially coincide with the studied phenomenon. Therefore, a suitable filtering method was proposed. Developed diagnostic algorithms, as well as ANYmal robot inspection functionalities and resistance to underground conditions, are developed as a part of the “THING–subTerranean Haptic INvestiGator” project.

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

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7646
Author(s):  
Hamid Shiri ◽  
Jacek Wodecki ◽  
Bartłomiej Ziętek ◽  
Radosław Zimroz
Keyword(s):  
Signal Processing ◽  
Good Condition ◽  
Design Feature ◽  
Acoustic Signals ◽  
Rolling Element Bearing ◽  
Belt Conveyor ◽  
Inspection Robot ◽  
Vibration Sensors ◽  
Belt Conveyors ◽  
Monitoring And Diagnostics

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.

scholarly journals Procedures of Detecting Damage to a Conveyor Belt with Use of an Inspection Legged Robot for Deep Mine Infrastructure

Minerals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1040
Author(s):  
Maria Stachowiak ◽  
Wioletta Koperska ◽  
Paweł Stefaniak ◽  
Artur Skoczylas ◽  
Sergii Anufriiev
Keyword(s):  
Conveyor Belt ◽  
Underground Mines ◽  
Belt Conveyor ◽  
Deep Mine ◽  
Inspection Robot ◽  
Video Recordings ◽  
Route Length ◽  
Conveyor Belts ◽  
Potential Damage ◽  
Edge Damage

Conveying systems are responsible for a large part of continuous horizontal transportation in underground mines. The total length of a conveyor network can reach hundreds of kilometers, while a single conveyor usually has a route length of about 0.5–2 km. The belt is a critical and one of the most costly components of the conveyor, and damage to it can result in long unexpected stoppages of production. This is why proper monitoring of conveyor belts is crucial for continuous operation. In this article, algorithms for the detection of potential damage to a conveyor belt are described. The algorithms for analysis used video recordings of a moving belt conveyor, which, in case the of hazardous conditions of deep mines, can be collected, for example, by a legged autonomous inspection robot. The video was then analyzed frame by frame. In this article, algorithms for edge damage detection, belt deviation, and conveyor load estimation are described. The main goal of the research was to find a potential application for image recognition to detect damage to conveyor belts in mines.

scholarly journals An Inspection Robot for Belt Conveyor Maintenance in Underground Mine—Infrared Thermography for Overheated Idlers Detection

2020 ◽  
Vol 10 (14) ◽  
pp. 4984 ◽  
Author(s):  
Jarosław Szrek ◽  
Jacek Wodecki ◽  
Ryszard Błażej ◽  
Radoslaw Zimroz
Keyword(s):  
Infrared Thermography ◽  
Hot Spots ◽  
Underground Mining ◽  
Mechanical Systems ◽  
Conveyor System ◽  
Safe Place ◽  
Inspection Robot ◽  
Spatially Distributed ◽  
Underground Infrastructure ◽  
Belt Conveyors

It is well known that mechanical systems require supervision and maintenance procedures. There are a lot of condition monitoring techniques that are commonly used, and in the era of IoT and predictive maintenance one may find plenty of solutions for various applications. Unfortunately in the case of belt conveyors used in underground mining a list of possible solutions shrinks quickly. The reason is that they are specific mechanical systems—the typical conveyor is located in the mining tunnel and its length may vary between 100 and 1000 m. According to mining regulations, visual inspection of the conveyor route should be done before it will start the operation. On the other hand, since environmental conditions in mining tunnels are extremely harsh and the risk of accidents is high, there is a tendency to minimize human presence in the tunnels. In this paper, we propose a prototype of an inspection robot based on a UGV platform that could support maintenance staff during the inspection. At present, the robot is controlled by an operator using radio however, we plan to make it autonomous. Moreover, its support could be significant—the robot can “see” elements of the conveyor route (RGB camera) and can identify hot spots using infrared thermography. Moreover, the detected hot spots could be localized and its position can be stored together with both types of images. In parallel, it is possible to preview images in a real-time and stored data allow analysing state of conveyor system after the inspection mission. It is also important that due to radio control systems, an operator can stay in a safe place. Such a robot can be classified as a mobile monitoring system for spatially distributed underground infrastructure.

scholarly journals FPGA-Based Linear Detection Algorithm of an Underground Inspection Robot

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 284
Author(s):  
Chuanwei Zhang ◽  
Shirui Chen ◽  
Lu Zhao ◽  
Xianghe Li ◽  
Xiaowen Ma
Keyword(s):  
Bulk Material ◽  
High Reliability ◽  
Conveyor Belt ◽  
Detection Algorithm ◽  
Belt Conveyor ◽  
Material Transport ◽  
Inspection Robot ◽  
Sensor Arrangement ◽  
Linear Detection ◽  
Belt Conveyors

Conveyor belts are key pieces of equipment for bulk material transport, and they are of great significance to ensure safe operation. With the development of belt conveyors in the direction of long distances, large volumes, high speeds, and high reliability, the use of inspection robots to perform full inspections of belt conveyors has not only improved the efficiency and scope of the inspections but has also eliminated the dependence of the traditional method on the density of sensor arrangement. In this paper, relying on the wireless-power-supply orbital inspection robot independently developed by the laboratory, aimed at the problem of the deviation of the belt conveyor, the methods for the diagnosis of the deviation of the conveyor belt and FPGA (field-programmable gate array) parallel computing technology are studied. Based on the traditional LSD (line segment detection) algorithm, a straight-line extraction IP core, suitable for an FPGA computing platform, was constructed. This new hardware linear detection algorithm improves the real-time performance and flexibility of the belt conveyor diagnosis mechanism.

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