automatic inspection
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Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 327
Author(s):  
Jarosław Szrek ◽  
Janusz Jakubiak ◽  
Radoslaw Zimroz

Mechanical systems (as belt conveyors) used in the mining industry, especially in deep underground mines, must be supervised on a regular basis. Unfortunately, they require high power and are spatially distributed over a large area. Till now, some elements of the conveyor (drive units) have been monitored 24 h/day using SCADA systems. The rest of the conveyor is inspected by maintenance staff. To minimize the presence of humans in harsh environments, we propose a mobile inspection platform based on autonomous UGV. It is equipped with various sensors, and in practice it is capable of collecting almost the same information as maintenance inspectors (RGB image, sound, gas sensor, etc.). Till now such experiments have been performed in the lab or in the mine, but the robot was controlled by the operator. In such a scenario the robot is able to record data, process them and detect, for example, an overheated idler. In this paper we will introduce the general concept of an automatic robot-based inspection for underground mining applications. A framework of how to deploy the inspection robot for automatic inspection (3D model of the tunnel, path planing, etc.) are defined and some first results from automatic inspection tested in lab conditions are presented. Differences between the planned and actual path are evaluated. We also point out some challenges for further research.


Author(s):  
Yunjie Zhou ◽  
Xiaodi Wang ◽  
Yanling Chen ◽  
Xiaojuan Jiang ◽  
Cheng Wang ◽  
...  

Author(s):  
Lorenzo Comba ◽  
Alessandro Biglia ◽  
Davide Ricauda Aimonino ◽  
Paolo Barge ◽  
Cristina Tortia ◽  
...  

2021 ◽  
Vol 131 ◽  
pp. 103881 ◽  
Author(s):  
Yi Tan ◽  
Silin Li ◽  
Hailong Liu ◽  
Penglu Chen ◽  
Zhixiang Zhou

2021 ◽  
Vol 2085 (1) ◽  
pp. 012016
Author(s):  
Shuo Pan ◽  
Xinjie Shao

Abstract Aiming at the problem of 3D measurement of the inner surface of pipe, this paper develops a new structure of pipe inspection device inside the pipe based on the principle of laser triangulation. The device is composed of motion mechanism and image acquisition system. The three-dimensional shape of the inner surface is reconstructed the image pixel offset. The detection device driven by the motion mechanism can realize the automatic detection of different positions. The experiment shows the steps of the three-dimensional measurement of the inner surface of the pipe, verifies the feasibility of this method.


2021 ◽  
Vol 33 (1) ◽  
Author(s):  
Petra Gospodnetić ◽  
Dennis Mosbach ◽  
Markus Rauhut ◽  
Hans Hagen

AbstractInspection planning approaches so far have focused on automatically obtaining an optimal set of viewpoints required to cover a given object. While research has provided interesting results, the automatic inspection planning has still not been made a part of the everyday inspection system development process. This is mostly because the plans are difficult to verify and it is impossible to compare them to laboratory-developed plans. In this work, we give an overview of available generate-and-test approaches, evaluate their results for various objects and finally compare them to plans created by inspection system development experts. The comparison emphasizes both benefits and downsides of automated approaches and highlights problems which need to be tackled in the future in order to make the automated inspection planning more applicable.


Author(s):  
Alessandro Pignatelli ◽  
Francesca D’Ajello Caracciolo ◽  
Rodolfo Console

AbstractAnalyzing seismic data to get information about earthquakes has always been a major task for seismologists and, more in general, for geophysicists. Recently, thanks to the technological development of observation systems, more and more data are available to perform such tasks. However, this data “grow up” makes “human possibility” of data processing more complex in terms of required efforts and time demanding. That is why new technological approaches such as artificial intelligence are becoming very popular and more and more exploited. In this paper, we explore the possibility of interpreting seismic waveform segments by means of pre-trained deep learning. More specifically, we apply convolutional networks to seismological waveforms recorded at local or regional distances without any pre-elaboration or filtering. We show that such an approach can be very successful in determining if an earthquake is “included” in the seismic wave image and in estimating the distance between the earthquake epicenter and the recording station.


Author(s):  
Katherine Shu-Min Li ◽  
Leon Li-Yang Chen ◽  
Ken Chau-Cheung Cheng ◽  
Peter Yi-Yu Liao ◽  
Sying-Jyan Wang ◽  
...  

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