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Data Augmentation for Classification of Multi-Domain Tension Signals
Pub. online: 19 November 2024      Type: Research Article      Open accessOpen Access
Received
1 August 2024
Accepted
1 November 2024
Published
19 November 2024

Abstract

There are different deep neural network (DNN) architectures and methods for performing augmentation on time series data, but not all the methods can be adapted for specific datasets. This article explores the development of deep learning models for time series, applies data augmentation methods to conveyor belt (CB) tension signal data and investigates the influence of these methods on the accuracy of CB state classification. CB systems are one of the essential elements of production processes, enabling smooth transportation of various industrial items, therefore its analysis is highly important. For the purpose of this work, multi-domain tension data signals from five different CB load weight conditions (0.5 kg, 1 kg, 2 kg, 3 kg, 5 kg) and one damaged belt condition were collected and analysed. Four DNN models based on fully convolutional network (FCN), convolutional neural network combined with long short-term memory (CNN-LSTM) model, residual network (ResNet), and InceptionTime architectures were developed and applied to classification of CB states. Different time series augmentations, such as random Laplace noise, drifted Gaussian noise, uniform noise, and magnitude warping, were applied to collected data during the study. Furthermore, new CB tension signals were generated using a TimeVAE model. The study has shown that DNN models based on FCN, ResNet, and InceptionTime architectures are able to classify CB states accurately. The research has also shown that various data augmentation methods can improve the accuracy of the above-mentioned models, for example, the combined addition of random Laplace and drifted Gaussian noise improved FCN model’s baseline (without augmentation) classification accuracy with 2.0 s-length signals by 4.5% to 92.6% ± 1.54%. FCN model demonstrated the best accuracy and classification performance despite its lowest amount of trainable parameters, thus demonstrating the importance of selecting and optimizing the right architecture when developing models for specific tasks.

References

 
Andrejiova, M., Grincova, A., Marasova, D. (2021). Identification with machine learning techniques of a classification model for the degree of damage to rubber-textile conveyor belts with the aim to achieve sustainability. Engineering Failure Analysis, 127, 105564.
 
Bortnowski, P., Król, R., Ozdoba, M. (2022a). Roller damage detection method based on the measurement of transverse vibrations of the conveyor belt. Eksploatacja i Niezawodność, 24(3), 510–521.
 
Bortnowski, P., Kawalec, W., Król, R., Ozdoba, M. (2022b). Types and causes of damage to the conveyor belt–review, classification and mutual relations. Engineering Failure Analysis, 140, 106520.
 
Chlap, P., Min, H., Vandenberg, N., Dowling, J.A., Holloway, L., Haworth, A. (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5), 545–563.
 
Dąbek, P., Wróblewski, A., Wodecki, J., Bortnowski, P., Ozdoba, M., Król, R., Zimroz, R. (2023). Application of the methods of monitoring and detecting the belt mistracking in laboratory conditions. Applied Sciences, 13(4), 2111.
 
Desai, A., Freeman, C., Wang, Z., Beaver, I. (2021). Timevae: a variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095.
 
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27.
 
Goubeaud, M., Joußen, P., Gmyrek, N., Ghorban, F., Schelkes, L., Kummert, A. (2021). Using Variational Autoencoder to augment Sparse Time series Datasets. In: 2021 7th International Conference on Optimization and Applications (ICOA), pp. 1–6.
 
Gregor Hartmann, K., Tibor Schirrmeister, R., Ball, T. (2018). EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv e-prints, arXiv:1806.01875v1.
 
Hsiao, T.-Y., Chang, Y.-C., Chou, H.-H., Chiu, C.-T. (2019). Filter-based deep-compression with global average pooling for convolutional networks. Journal of Systems Architecture, 95, 9–18.
 
Huang, T., Chakraborty, P., Sharma, A. (2023). Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images. International Journal of Transportation Science and Technology, 12(1), 1–18.
 
Iglesias, G., Talavera, E., González-Prieto, Á., Mozo, A., Gómez-Canaval, S. (2023). Data augmentation techniques in time series domain: a survey and taxonomy. Neural Computing and Applications, 35(14), 10123–10145.
 
Iwana, B.K., Uchida, S. (2021). Time series data augmentation for neural networks by time warping with a discriminative teacher. In: 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, pp. 3558–3565.
 
Kingma, D.P., Welling, M. (2019). An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4), 307–392.
 
Klištincová, N., Pin, L., Puškárová, A., Giannino, D., Bučková, M., Lambreva, M.D., Manfredini, A., Canfora, L., Pangallo, D., Pinzari, F. (2024). From farm to fork: fungal and bacterial contaminants and their diagnostics in the production steps of ready-to-eat salads. Trends in Food Science & Technology, 150, 104573.
 
Li, X.-G., Miao, C.-Y., Wang, J., Zhang, Y. (2011). Automatic defect detection method for the steel cord conveyor belt based on its X-ray images. In: 2011 International Conference on Control, Automation and Systems Engineering (CASE), pp. 1–4.
 
Long, J., Shelhamer, E., Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440.
 
Santos, A.A., Rocha, F.A., Reis, A.J.d.R., Guimarães, F.G. (2020). Automatic system for visual detection of dirt buildup on conveyor belts using convolutional neural networks. Sensors, 20(20), 5762.
 
Sarker, I.H. (2021). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2, 420.
 
Smith, K.E., Smith, A.O. (2020). Conditional GAN for timeseries generation. arXiv preprint arXiv:2006.16477.
 
Um, T.T., Pfister, F.M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U., Kulić, D. (2017). Data augmentation of wearable sensor data for Parkinson’s disease monitoring using convolutional neural networks. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 216–220.
 
Wang, J., Perez, L. (2017a). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11(2017), 1–8.
 
Wang, Y., Miao, C., Miao, D., Yang, D., Zheng, Y. (2023). Hazard source detection of longitudinal tearing of conveyor belt based on deep learning. PLoS ONE, 18(4), 0283878.
 
Wang, Z., Yan, W., Oates, T. (2017b). Time series classification from scratch with deep neural networks: a strong baseline. In: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1578–1585.
 
Yoon, J., Jarrett, D., Van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in Neural Information Processing Systems, 32.
 
Zhang, M., Shi, H., Zhang, Y., Yu, Y., Zhou, M. (2021). Deep learning-based damage detection of mining conveyor belt. Measurement, 175, 109130.
 
Žvirblis, T., Petkevičius, L., Bzinkowski, D., Vaitkus, D., Vaitkus, P., Rucki, M., Kilikevičius, A. (2022). Investigation of deep learning models on identification of minimum signal length for precise classification of conveyor rubber belt loads. Advances in Mechanical Engineering, 14(6), 1–13.

Biographies

Žvirblis Tadas
tadas.zvirblis@mf.vu.lt

T. Žvirblis received his PhD in technology sciences from the Faculty of Mechanics, Vilnius Gediminas Technical University (Lithuania), in 2022. He is currently employed as a senior researcher and a postdoctoral fellow at the Institute of Data Science and Digital Technologies, Vilnius University as well as an associate professor at the Faculty of Medicine, Vilnius University (Lithuania). His research interests include applied statistics, artificial intelligence, neural networks, data mining methods, and biostatistics. He is the author of 40 articles published in scientific journals and 25 works in conference proceedings.

Pikšrys Armantas
armantas.piksrys@mif.stud.vu.lt

A. Pikšrys is MSc student at the Faculty of Mathematics and Informatics, Vilnius University (Lithuania). His research focuses on the application of machine and deep neural learning models.

Bzinkowski Damian
damianbzinkowski@gmail.com

D. Bzinkowski is PhD student at the Faculty of Mechanical Engineering at Radom University (Poland). His main research interest is diagnostics and optimization of production processes.

Rucki Mirosław
m.rucki@uthrad.pl

M. Rucki received his PhD degree and habilitation in mechanical engineering at Poznan University of Technology (Poland) and the title of full professor at VSB-Technical University Ostrava (Czech Republic). At present, he is with Casimir Pulaski Radom University (Poland). His main research interest is metrology, especially the measurement systems related to the industrial applications. Apart from that, he has got PhD degrees in humanities (Aramaic literature) and social sciences (family sciences).

Kilikevičius Artūras
arturas.kilikevicius@vilniustech.lt

A. Kilikevičius received his PhD degree in technological sciences (measurement engineering) at Vilnius Gediminas Technical University (Lithuania). Currently, he is a director and a chief research fellow at the Institute of Mechanical Science at Vilnius Gediminas Technical University (Vilnius Tech). Also, he is a teaching professor at the Faculty of Mechanics, as well as a Chairman of PhD defense council in mechanical engineering at Vilnius Tech (Lithuania). A. Kilikevičius is the author of more than 200 scientific articles, co-author of more than 10 technologies (in the fields of environmental protection and precision mechanics), some of which are patented.

Kurasova Olga
olga.kurasova@mif.vu.lt

O. Kurasova received a PhD degree in computer science from the Institute of Mathematics and Informatics, Vytautas Magnus University (Lithuania) in 2005. She is currently employed as a principal researcher and a professor at the Institute of Data Science and Digital Technologies, Vilnius University (Lithuania). Her research interests include data mining methods, optimization theory and applications, artificial intelligence, neural networks, visualization of multidimensional data, multiple criteria decision support, parallel computing, and image processing. She is the author of more than 100 scientific publications.


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Copyright
© 2024 Vilnius University
Open access article under the CC BY license.

Keywords
fully convolutional network convolutional neural network long short-term memory model residual networks inception networks data augmentation sliding window magnitude warping variational autoencoder conveyor belt tension signals

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