Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders
This paper provides a novel rock-coal interface recognition method based on stacked sparse autoencoders (SSAE). Given their different size and hardness, coal and rock generate different tail beam vibrations. Therefore, the rock-coal interface in top coal caving can be identified using an acceleratio...
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doaj-e12186d345a64227a52780f48bcd0ffb2020-11-24T23:56:50ZengJVE InternationalJournal of Vibroengineering1392-87162538-84602016-11-011874261427510.21595/jve.2016.1738617386Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencodersGuoxin Zhang0Zengcai Wang1Lei Zhao2School of Mechanical Engineering, Shandong University, Jinan, ChinaSchool of Mechanical Engineering, Shandong University, Jinan, ChinaSchool of Mechanical Engineering, Shandong University, Jinan, ChinaThis paper provides a novel rock-coal interface recognition method based on stacked sparse autoencoders (SSAE). Given their different size and hardness, coal and rock generate different tail beam vibrations. Therefore, the rock-coal interface in top coal caving can be identified using an acceleration sensor to measure such vibrations. The end of the hydraulic support beam is an ideal location for installing the sensor, as proven by many experiments. To improve recognition accuracy, the following steps are performed. First, ensemble empirical mode decomposition method (EEMD) is used to decompose the vibration signals of the tail beam into several intrinsic mode functions to complete feature extraction. Second, the features extracted are preprocessed as the inputs of SSAE. Third, a greedy, layer-wise approach is employed to pretrain the weights of the entire deep network. Finally, fine tuning is employed to search the global optima by simultaneously altering the parameters of all layers. Test results indicate that the average recognition accuracy of coal and rock is 98.79 % under ideal caving conditions. The superiority of the proposed method is verified by comparing its performance with those of four other algorithms.https://www.jvejournals.com/article/17386recognition of rock-coal interfacestacked sparse autoencoderspattern recognitionfeature extraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guoxin Zhang Zengcai Wang Lei Zhao |
spellingShingle |
Guoxin Zhang Zengcai Wang Lei Zhao Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders Journal of Vibroengineering recognition of rock-coal interface stacked sparse autoencoders pattern recognition feature extraction |
author_facet |
Guoxin Zhang Zengcai Wang Lei Zhao |
author_sort |
Guoxin Zhang |
title |
Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
title_short |
Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
title_full |
Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
title_fullStr |
Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
title_full_unstemmed |
Recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
title_sort |
recognition of rock–coal interface in top coal caving through tail beam vibrations by using stacked sparse autoencoders |
publisher |
JVE International |
series |
Journal of Vibroengineering |
issn |
1392-8716 2538-8460 |
publishDate |
2016-11-01 |
description |
This paper provides a novel rock-coal interface recognition method based on stacked sparse autoencoders (SSAE). Given their different size and hardness, coal and rock generate different tail beam vibrations. Therefore, the rock-coal interface in top coal caving can be identified using an acceleration sensor to measure such vibrations. The end of the hydraulic support beam is an ideal location for installing the sensor, as proven by many experiments. To improve recognition accuracy, the following steps are performed. First, ensemble empirical mode decomposition method (EEMD) is used to decompose the vibration signals of the tail beam into several intrinsic mode functions to complete feature extraction. Second, the features extracted are preprocessed as the inputs of SSAE. Third, a greedy, layer-wise approach is employed to pretrain the weights of the entire deep network. Finally, fine tuning is employed to search the global optima by simultaneously altering the parameters of all layers. Test results indicate that the average recognition accuracy of coal and rock is 98.79 % under ideal caving conditions. The superiority of the proposed method is verified by comparing its performance with those of four other algorithms. |
topic |
recognition of rock-coal interface stacked sparse autoencoders pattern recognition feature extraction |
url |
https://www.jvejournals.com/article/17386 |
work_keys_str_mv |
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1725456384451936256 |