scholarly journals Conveyor Belt Damage Detection with the Use of a Two-Layer Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5480
Author(s):  
Agata Kirjanów-Błażej ◽  
Aleksandra Rzeszowska
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Conveyor Belt ◽  
Belt Conveyor ◽  
Wear Process ◽  
Non Invasive ◽  
Changes Over Time ◽  
Transported Material ◽  
Matlab Programming

Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%.

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

Mathematical Model for Determining the Lifetime of Conveyor Belts Depending on some Selected Parameters

2014 ◽  
Vol 683 ◽  
pp. 147-152
Author(s):  
Miriam Andrejiová ◽  
Anna Grinčová ◽  
Anna Pavlisková
Keyword(s):  
Mathematical Model ◽  
Statistical Methods ◽  
Paint Layer ◽  
Conveyor Belt ◽  
Belt Conveyor ◽  
Layer Width ◽  
Conveyor Belts ◽  
Belt Conveyors ◽  
Transported Material ◽  
Optimal Construction

In the last years, belt conveyors belong to the most frequently used means of transport in various industries. The most important component of the belt conveyor is the conveyor belt. Therefore, it is necessary to pay more attention also to optimal lifetime of conveyor belts. Conveyor belt lifetime is a very complicated issue. It is affected by plenty of factors, including above all the quality structure of the belt conveyor, optimal construction, production, and properties of the conveyor belt as such, adequate solution of conveyance route shifting, reasonable maintenance, and quality repairs of conveyor belts. The paper deals with the exploring the lifetime of conveyor belts depending from on some selected parameters obtained from the operating records of practice (thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material) with using appropriate mathematical - statistical methods.

scholarly journals A Siamese Neural Network for Non-Invasive Baggage Re-Identification

2020 ◽  
Vol 6 (11) ◽  
pp. 126
Author(s):  
Pier Luigi Mazzeo ◽  
Christian Libetta ◽  
Paolo Spagnolo ◽  
Cosimo Distante
Keyword(s):  
Neural Network ◽  
State Of The Art ◽  
Conveyor Belt ◽  
Handling System ◽  
Non Invasive ◽  
Training Images ◽  
Traffic Jams ◽  
Baggage Handling

Baggage travelling on a conveyor belt in the sterile area (the rear collector located after the check-in counters) often gets stuck due to traffic jams, mainly caused by incorrect entries from the check-in counters on the collector belt. Using suitcase appearance captured on the Baggage Handling System (BHS) and airport checkpoints and their re-identification allows for us to handle baggage safer and faster. In this paper, we propose a Siamese Neural Network-based model that is able to estimate the baggage similarity: given a set of training images of the same suitcase (taken in different conditions), the network predicts whether the two input images belong to the same baggage identity. The proposed network learns discriminative features in order to measure the similarity among two different images of the same baggage identity. It can be easily applied on different pre-trained backbones. We demonstrate our model in a publicly available suitcase dataset that outperforms the leading latest state-of-the-art architecture in terms of accuracy.

scholarly journals Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3081
Author(s):  
Dominika Olchówka ◽  
Aleksandra Rzeszowska ◽  
Leszek Jurdziak ◽  
Ryszard Błażej
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Conveyor Belt ◽  
Data Sets ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Second Stage ◽  
Conveyor Belts ◽  
Steel Cord

This paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images, obtained for numerous parameters settings of the device, were the base for statistical analysis. This analysis makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach it appropriate identification of damage. In both methods, the same data sets were used, which made it possible to compare methods.

scholarly journals Analysis of Reasons for Reduced Strength of Multiply Conveyor Belt Splices

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1512
Author(s):  
Mirosław Bajda ◽  
Monika Hardygóra
Keyword(s):  
Closed Loop ◽  
Conveyor Belt ◽  
Strength Parameter ◽  
Belt Conveyor ◽  
Reliable Operation ◽  
Synthetic Fibers ◽  
Conveyor Belts ◽  
Belt Conveyors ◽  
Main Component ◽  
Transported Material

Belt conveyors are used for the transportation of bulk materials in a number of different branches of industry, especially in mining and power industries or in shipping ports. The main component of a belt conveyor is its belt, which serves both as a support for the transported material along the conveyor route and as an element in the drive transmission system. Being crucial to the effective and reliable operation of the conveyor, the belt is also its most expensive and the least durable element. A conveyor belt comprises a core, covers and edges. A multiply textile belt, in which the core is constructed of synthetic fibers such as polyamide, polyester or aramid, is the oldest and still the most commonly used conveyor belt type. The plies are joined with a thin layer of rubber or another material (usually the material is the same as the material used in the covers), which provides the required delamination strength to the belt and allows the plies to move relative to each other as the belt is bent. Belts are installed on the conveyors in a closed loop in order to join belt sections, whose number and length depend on the length and type of the belt conveyor. Belts are joined with each other in a splicing procedure. The cutting of the belt core causes belt splices to be prone to concentrated stresses. The discontinued core also causes the belt to be the weakest element in a conveyor belt loop. The article presents the results of strength parameter tests that were performed on laboratory and industrial splices and indicated the reasons for the reduced strength of conveyor belt splices. Splice strength is reduced mainly due to incorrect preparation of the spliced surfaces and to different mechanical parameters of the spliced belts.

scholarly journals Statistical Analysis and Neural Network in Detecting Steel Cord Failures in Conveyor Belts

2021 ◽  
Author(s):  
Dominika Olchówka ◽  
Aleksandra Rzeszowska ◽  
Leszek Jurdziak ◽  
Ryszard Błażej
Keyword(s):  
Neural Network ◽  
Statistical Analysis ◽  
Conveyor Belt ◽  
Data Sets ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Second Stage ◽  
Conveyor Belts ◽  
Steel Cord

The paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images obtained for numerous devices parameters settings were the base for statistical analysis. It makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach its appropriate identification of damage. In both methods were used the same data sets, which made it possible to compare methods.

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