Digital Image Processing for Detecting Industrial Machine Work Failure with Quantization Vector Learning Method

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Diterbitkan: Oct 31, 2019
DOI: https://doi.org/10.30818/jpkm.2019.2040203
Kata Kunci:
image processing, Learning Vector Quantization (LVQ), Neural Network
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Muhammad Rafli Rasyid
Universitas Hasanuddin
Zulkifli Tahir
Universitas Hasanuddin
nfn Syafaruddin
Universitas Hasanuddin

Abstrak

Todays, digital image processing is widely used in various fields to facilitate humans in doing work by analyzing videos or images for use in decision making in the industrial world. The use of industrial machine technology is one of the most important factors in efforts to facilitate human work, but an industrial machine is inseparable from work failure that can hinder the production process and cause harm to the industry. This study aims to detect a failure in industrial machinery by using video data of industrial machine movements recorded using a webcam camera. For the preprocessing stage, the image is resized then converted to grayscale imagery and segmented using the thresholding method, then morphological operations are performed with an opening operation, feature extraction is done by changing the binary image into a vector data that is used as input data in the classification process using the Learning Vector Quantization Neural Network Algorithm (LVQ NN) version 1. The results showed the results of the detection of machine working errors can be done well with an accuracy value of 94.24% in training and 92.38% in the testing phase.

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Vol 4 No 2 (2019): October 2019
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Informatika

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Referensi

Anthal, J., Upadhyay, A. and Gupta, A. (2017). Detection of Vitiligo Skin Disease using LVQ Neural Network. 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE, pp. 922–925. doi: 10.1109/CTCEEC.2017.8455029.

Budiharto, W. and Suharto, D. (2014). Artificial Intelligence Konsep Dan Penerapannya. Pertama. Yogyakarta: Andi.

Contreras-medina, L. M. et al. (2010). FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection, 59(1), pp. 63–72.

Gawde, S. S. and Borkar, S. (2017). Condition Monitoring Using Image Processing. ICCMC, pp. 1083–1086.

Hidayatullah, P. (2017). Pengolahan Citra Digital Teori dan Aplikasi Nyata. First Edit. Bandung: Informatika.

Ji, Y. et al. (2018). Apple color automatic grading method based on machine vision. Proceedings of the 30th Chinese Control and Decision Conference, CCDC 2018. IEEE, pp. 5671–5675. doi: 10.1109/CCDC.2018.8408121.

Li, P. et al. (2015). Surface targets recognition method based on LVQ neural network. 2015 IEEE International Conference on Mechatronics and Automation, ICMA 2015, pp. 676–680. doi: 10.1109/ICMA.2015.7237566.

Melin, P. et al. (2014). A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Information Sciences. Elsevier Inc., 279(April), pp. 483–497. doi: 10.1016/j.ins.2014.04.003.

Şahan, O. F. et al. (2018). An image processing system for detecting production errors on circuit boards and enabling error tracking and reporting based on 2-D barcodes. 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, pp. 1–4. doi: 10.1109/SIU.2018.8404589.

Sardogan, M., Tuncer, A. and Ozen, Y. (2018). Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm. UBMK 2018 - 3rd International Conference on Computer Science and Engineering. IEEE, pp. 382–385. doi: 10.1109/UBMK.2018.8566635.

Setiawan, H. and Yuniarno, E. M. (2018). Biometric Recognition Based on Palm Vein Image Using Learning Vector Quantization. Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017. IEEE, (November), pp. 95–99. doi: 10.1109/ICICI-BME.2017.8537770.

Tahir, Z. (2010). A Hybrid Maintenance Management Model in Decision Support System for Small and Medium Food Processing Industries. Universiti Teknikal Malaysia Melaka.

Tahir, Z. (2018) ‘Study Penerapan Industri Cerdas Dengan Komputasi Kabut (Fog Computing)’. Makassar: Universitas Hasanuddin.

Wang, C., Zhang, H. and Yu, C. (2012). Research on color recognition of urine test paper based on learning vector quantization (LVQ). Proceedings of the 2012 2nd International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2012, pp. 850–853. doi: 10.1109/IMCCC.2012.205