Experimental Study on RFID Performance Factors of Conveyor Belt System Using DOE Methodology

Author(s):  
Xing Wang ◽  
Dong Wang
2021 ◽  
Vol 8 (12) ◽  
pp. 328-332
Author(s):  
Vishnu Dutta Tiwari ◽  
Gouraw Beohar

A conveyor belt system essentially consists of an endless belt of elastic material connected between two flat pulleys and driven by the rotation of one of the pulleys by a direct current motor. Usually, the material is fed to the belt near the other end of the pulley. The moving belt that carries the material to the drive pulley tends to sag between the two end pulleys due to its own weight. Rubber conveyor belts are commonly used to transport items with uneven bottom surfaces, small items that fall between rolls, or product bags that sag between rolls.


2017 ◽  
Vol 201 ◽  
pp. 17-25 ◽  
Author(s):  
Tesfaye F. Bedane ◽  
Long Chen ◽  
Francesco Marra ◽  
Shaojin Wang

Author(s):  
Olutayo Opeyemi Ogunmilua

Abstract: The conveyor belt is one of the most operational critical equipment’s in the mining industry, they are mostly used in the transportation of crushed materials from the crushing station to where there’ll be further processed. Due to the increasing complexity of belt conveyor systems, managing their integrity has become even more difficult, as they are now used across various industries, environments and carry materials of different weight variations, leaving them susceptible to failures (1). This paper provides an industry specific knowledge on how Weibull analysis can be used to predict the failure rate of a conveyor belt system, using parameters such as the time to failure (TTF), installation and failure dates, as determinant parameters for the predictions. Several Weibull failure distributions and functions have been used to establish accuracy of results and to create comparisons on the different ways in which risk, unreliability and availability are quantified, using calculated values such as the Shape and scale parameter. The paper utilizes real world case studies in the area of mining, which sheds light on key component failures and their cut sets within the conveyor belt system (2) Keywords: TTF, TTR, Threshold parameter, Repair date, Shape parameter, B10, B15, B20, Scale parameter, ECA, CDF, PDF


Machine learning techniques plays an important role in knowledge discovery and assists humans in decision making. They help to detect patterns and predict the actions/outcome. In a complex industrial environment mode of operations of a machine depends on various internal and external parameters which are often done using expert judgement method which is not accurate and results in machine breakdown thereby resulting in unplanned outage. In this paper, we discussed and demonstrated how machine learning algorithms can help to handle conveyor systems autonomously in an optimum way without any human intervention. A conveyor belt system operational data is used to select the appropriate classification technique for the selected dataset. The details of the dataset collected, algorithms used and the test results are discussed in this paper.


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