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

Mobile Robot Path Tracking Based on Fuzzy Control

2015 ◽  
Vol 727-728 ◽  
pp. 736-739
Author(s):  
Ming Ming Bian ◽  
Jin Lan Zhang
Keyword(s):  
Angular Velocity ◽  
Fuzzy Control ◽  
Mobile Robot ◽  
Control Method ◽  
Driving Behavior ◽  
Path Tracking ◽  
Angle Error ◽  
Robot Controller ◽  
Inspection Robot ◽  
Robot Path

A model for a mobile inspection robot is introduced. Based on human driving behavior, a fuzzy control method for mobile robot path tracking is proposed. Angular velocity controller is established by distance error and angle error. Angular velocity is controlled to implement path tracking of the wheeled mobile robot. Experimental results show that the effectiveness of the inspection robot controller is good and it is effective.

The Structure to a Brachiation Inspection Robot and its Fuzzy Control for Obstacle-Negotiation

2013 ◽  
Vol 278-280 ◽  
pp. 622-628 ◽  
Author(s):  
Yong Xie ◽  
Ai Ping Xiao ◽  
Jian Wu ◽  
Guang Liu
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Inverse Kinematics ◽  
Fuzzy Controller ◽  
The Body ◽  
Virtual Simulation ◽  
Simulation Technology ◽  
Work Requirements ◽  
Inspection Robot ◽  
The Stability

Through the analysis of the structure of the existing Bachiation Inspection Robot, the thesis summed up the existing shortcomings, put forward an improved structure which makes the robot grasp the line accurately in the process of obstacles in mountainous forest areas, in order to ensure the stability of the body, and avoid the additional torque generated in the process; for the improvement of the structure, the thesis established positive and inverse kinematics research on the Bachiation Inspection Robot through the kinematics model established in D-H method; finally, designed a fuzzy controller to achieve the body’s ascension (down) movement, built program diagram using Simulink tool of MATLAB, analyzed the stability of the fuzzy control system. The end of the thesis further verified the feasibility of structured of the robot through virtual simulation technology, proved that the robot can meet the more steady work requirements in crossing the obstacles.

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.

The Study on Soft Starting of Belt Conveyor Using Two-Input-Outlet Fuzzy Control Model

2011 ◽  
Vol 138-139 ◽  
pp. 327-332 ◽  
Author(s):  
Bing Yao Chen
Keyword(s):  
Fuzzy Control ◽  
Pid Control ◽  
System Reliability ◽  
Fast Response ◽  
Control Model ◽  
Constant Current ◽  
Control Methods ◽  
Belt Conveyor ◽  
Current Impact ◽  
Starting Process

Constant–current soft starting on induction motor based on fuzzy control is characterized as low system overshoot, fast response and relative stable starting process, so it can better solve the shortcomings existed in traditional methods and PID control methods, and improve the whole system reliability. In this article a special fuzzy control is adopted to complete the starting process, in which thyristor is the main current components and MCU is the control core. And this makes the entire starting process out of current impact.

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.

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