| Title |
Investigation of deep learning models on identification of minimum signal length for precise classification of conveyor rubber belt loads / |
| Authors |
Žvirblis, Tadas ; Petkevičius, Linas ; Bzinkowski, Damian ; Vaitkus, Dominykas ; Vaitkus, Pranas ; Rucki, Mirosław ; Kilikevičius, Artūras |
| DOI |
10.1177/16878132221102776 |
| Full Text |
|
| Is Part of |
Advances in mechanical engineering.. London : SAGE Publishing. 2022, vol. 14, no. 6, p. [1-13].. ISSN 1687-8132. eISSN 1687-8140 |
| Keywords [eng] |
conveyor rubber belt ; classification ; machine learning ; logistic regression ; support vector machine ; random forest ; long short-term memory neural networks ; transformer neural networks |
| Abstract [eng] |
In this paper, long short-term memory (LSTM) and Transformer neural network models were developed for classification of different conveyor belt conditions (loaded and unloaded). Comparative shallow models such as logistic regression, support vector machine and random forest were also developed and summarized. Six different-length belt pressure signals were analyzed: 0.2, 0.4, 0.8, 1.6, 3.2, and 5.0 s. Both LSTM and Transformer models achieved 100% accuracy using pressure raw signal. Furthermore, LSTM model reached the highest classification level with the shortest signals. Accuracy and F1-score of 98% and 100% were reached using only 0.8 and 1.6 s-length signals, respectively. Also, LSTM model performed training and testing procedures faster than Transformer. Random forest model demonstrated the best classification level using aggregated signal data with accuracy of 85% and F1-score for loaded and unloaded conditions of 85% and 69%, respectively. Loaded conveyor belt condition was significantly easier to classify than the unloaded one in all models. Only LSTM showed better classification recall for unloaded conveyor belt condition using short signal. Experimental research dataset CORBEL (Conveyor belt pressure signal dataset) and models are open-sourced and accessible on GitHub https://github.com/TadasZvirblis/CORBEL. |
| Published |
London : SAGE Publishing |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
