Deep Learning aplicado a inspeção visual da presença de um componente de conjunto de eixo
Abstract
A evolução dos processos industriais, guiada pelos conceitos de fábrica inteligente da Indústria 4.0, e a necessidade de tornar as tarefas de tomada de decisão cada vez menos dependentes de humanos deve demandar cada vez mais a aplicação industrial do aprendizado de máquinas. Nesse sentido, esse trabalho propõe a utilização de aprendizado profundo para a identificação da presença ou falta de um anel de retenção montado na ponta de um eixo veicular a partir de imagens. Uma rede neural convolucional foi utilizada para aprender as características das imagens e realizar a classificação. O sistema foi avaliado utilizando uma base de imagens coletada em um ambiente real de uma empresa. Apesar do desbalanceamento do conjunto de dados, o método produziu resultados máximos em sensibilidade, especificidade e F1-score. Além disso, a arquitetura da rede foi otimizada (redução de 90% do número de parâmetros) a fim de aumentar a eficiência computacional.
References
P. K. Muhuri, A. K. Shukla and A. Abraham, “Industry 4.0: A bibliometric analysis and detailed overview,” Engineering Applications of Artificial Intelligence, vol. 78, pp. 218-235, 2019.
N. Rahmatov, A. Paul, F. Saeed, W.-H. Hong, H. Seo and J. Kim, “Machine learning–based automated image processing for quality management in industrial Internet of Things,” International Journal of Distributed Sensor Networks, vol. 15, no 10, pp. 1-12, 2019.
V. Chauhan and B. Surgenor, “Fault detection and classification in automated assembly machines using machine vision,” The International Journal of Advanced Manufacturing Technology, vol. 90, no 9-12, pp. 2491-2512, 2017.
L. S. Dalenogare, G. B. Benitez, N. F. Ayala and A. G. Frank, “The expected contribution of Industry 4.0 technologies for industrial performance,” International Journal of Production Economics, vol. 204, pp. 383-394, 2018.
S. Wang, J. Wan, D. Li and C. Zhang, “Implementing smart factory of industrie 4.0: an outlook,” International Journal of Distributed Sensor Networks, vol. 12, no 1, p. 3159805, 2016.
J. A. Carvajal Soto, F. Tavakolizadeh and D. Gyulai, “An online machine learning framework for early detection of product failures in an Industry 4.0 context,” International Journal of Computer Integrated Manufacturing, vol. 32, no 4-5, pp. 452-465, 2019.
Z. Ge, Z. Song, S. X. Ding and B. Huang, “Data Mining and Analytics in the Process Industry: The Role of Machine Learning,” IEEE Access, vol. 5, pp. 20590-20616, 2017.
M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives and prospects,” Science, vol. 349, no 6245, pp. 255-260, 2015.
V. Sze, Y. H. Chen, T. J. Yang and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” Proceedings of the IEEE, vol. 105, no 12, pp. 2295-2329, 2017.
Y. LeCun, Y. Bengio and G. Hinton, “Deep Learning,” Nature, vol. 521, no 7553, pp. 436-444, 2015.
I. Goodfellow, Y. Bengio and A. Courville, Deep learning, Cambridge: MIT press, 2016.
D. Dechow, “The engine in engineering: David Dechow at Integro Technologies, discusses vision system integration, a discipline that remains as strong as ever as new imaging technologies become more readily avaliable.,” Imaging and Machine Vision Europe, pp. 34-36, 2018.
A. Mayr, D. Kißkalt, M. Meiners, B. Lutz, F. Schäfler, R. Seidel, A. Selmaier, J. Fuchs, M. Metzner, A. Blank and J. Franke, “Machine Learning in Production - Potentials, Challenges and Exemplary Applications,” Procedia CIRP, vol. 86, pp. 49-54, 2019.
G. Acciani, G. Brunetti, E. Chiarantoni and G. Fornarelli, “An Automatic Method to Detect Missing Components in Manufactured Products,” Proceedings. 2005 IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2324-2330, 2005.
J. Killing, B. W. Surgenor and C. K. Mechefske, “A machine vision system for the detection of missing fasteners on steel stampings,” The International Journal of Advanced Manufacturing Technology, vol. 41, no 7-8, pp. 808-819, 2009.
Z. Liu and H. Wang, “Automatic Detection of Transformer Components in Inspection Images Based on Improved Faster R-CNN,” Energies, vol. 11, no 12, p. 3496, 2018.
C.-C. Huang and X.-P. Lin, “Study on Machine Learning Based Intelligent Defect Detection System,” MATEC Web of Conferences, vol. 201, pp. 1-10, 2018.
L. B. Canêdo, A revolução industrial, São Paulo: Atual, 1994.
A. M. Nicolaci-da-Costa, “Revoluções tecnológicas e transformações subjetivas,” Psicologia: teoria e pesquisa, vol. 18, pp. 193-202, 2002.
A. T. Rauen, “Ciência, Tecnologia e Economia: Características frente à primeira e segunda Revoluções Industriais,” Espaço Acadêmico, pp. 1-3, 2006.
M. F. F. Júnior, “A terceira revolução industrial e o novo paradigma produtivo: algumas considerações sobre o desenvolvimento industrial brasileiro nos anos 90,” Revista da FAE, vol. 3, nº 2, 2000.
J. Qin, Y. Liu and R. Grosvenor, “A Categorical Framework of Manufacturing for Industry 4.0 and Beyond,” Procedia CIRP, vol. 52, pp. 173-178, 2016.
M. Hermann, T. Pentek and B. Otto, “Design Principles for Industrie 4.0 Scenarios: A Literature Review,” 2016 49th Hawaii international conference on system sciences (HICSS), pp. 3928-3937, 2016.
E. Oztemel and S. Gursev, “Literature review of Industry 4.0 and related technologies,” Journal of Intelligent Manufacturing, vol. 31, no 1, pp. 127-182, 2020.
R. Y. Zhong, X. Xu, E. Klotz and S. T. Newman, “Intelligent Manufacturing in the Context of Industry 4.0: A Review,” Engineering, vol. 3, no 5, pp. 616-630, 2017.
S. J. Qin and L. H. Chiang, “Advances and opportunities in machine learning for process data analytics,” Computers and Chemical Engineering, vol. 126, pp. 465-473, 2019.
Z. Ghahramani, “Probabilistic machine learning and artificial intelligence,” Nature, vol. 521, no 7553, pp. 452-459, 2015.
C. M. Bishop, Pattern recognition and machine learning, Springer, 2006.
S. Zerdoumi, A. Q. M. Sabri, A. Kamsin, I. A. T. Hashem, A. Gani, S. Hakak, M. A. Al-garadi and V. Chang, “Image pattern recognition in big data: taxonomy and open challenges: survey,” Multimedia Tools and Applications, vol. 77, no 8, pp. 10091-10121, 2018.
O. Chapelle, B. Scholkopf and A. Zien, “Semi-supervised Learning,” IEEE Transactions on Neural Networks, vol. 20, no 3, p. 542, 2006.
R. Nian, J. Liu and B. Huang, “A review on reinforcement learning: Introduction and applications in industrial process control,” Computers & Chemical Engineering, vol. 139, p. 106886, 2020.
T. Hastie, R. Tibshirani and J. Friedman, The elements of statistical learning: data mining, inference, and prediction, Springer Science & Business Media, 2009.
S.-T. Bow, Pattern Recognition and Image Preprocessing, Boca Raton: CRC press, 2002.
J. T. Tou and R. C. Gonzalez, Pattern recognition principles, Massachusetts: Addison-Wesley Publishing Company, 1974.
J. Sun and Q. Sun, “Further development of adaptable automated visual inspection—part I: concept and scheme,” The International Journal of Advanced Manufacturing Technology, vol. 81, no 5-8, pp. 1067-1076, 2015.
H. Liu, C. Zhang and D. Huang, “Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection,” Journal of Electtrical and Coputer Engineering, vol. 2017, pp. 1-10, 2017.
A. A. Moghaddam and L. Rangarajan, “Classification of Welding Defects in Radiographic Images,” Pattern Recognition and Image Analysis, vol. 26, no 1, pp. 54-60, 2016.
R. E. Davies, Computer and machine vision: theory, algorithms, practicalities, Academic Press, 2012.
D. A. Forsyth and J. Ponce, Computer vision: a modern aproach, Prentice Hall Professional Technical Reference, 2003.
R. S. Pahwa, J. Chao, J. Paul, Y. Li, M. T. L. Nwe, S. Xie, A. James, A. Ambikapathi, Z. Zeng and V. R. Chandrasekhar, “FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision,” 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 559-566, 2019.
S. Ravikumar, K. I. Ramachandran and V. Sugumaran, “Machine learning approach for automated visual inspection of machine components,” Expert Systems with Applications, vol. 38, no 4, pp. 3260-3266, 2011.
P. Malaca, L. F. Rocha, D. D. M. Gomes, J. Silva and G. Veiga, “Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry,” Journal of Intelligent Manufacturing, vol. 30, no 1, pp. 351-361, 2019.
J. Sun and Q. Sun, “Further development of adaptable automated visual inspection—part II: implementation and evaluation,” The International Journal of Advanced Manufacturing Technology, vol. 81, no 5-8, pp. 1077-1096, 2015.
L. Liu, F. Zhou and Y. He, “Vision-based fault inspection of small mechanical components for train safety,” IET Intelligent transport Systems, vol. 10, no 2, pp. 130-139, 2016.
C. M. Bishop, Neural Networks for pattern recognition, Oxford university press, 1995.
Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no 11, pp. 2278-2324, 1998.
F. Chollet, Deep learning with Phyton, Manning publications, 2017.
H.-H. Kim, J.-K. Park, J.-H. Oh and D. J. Kang, “Multi-task Convolutional Neural Network System for License Plate Recognition,” International Journal of Control, Automation and Systems, vol. 15, no 6, pp. 2942-2949, 2017.
J. Villalba-Diez, D. Schmidt, R. Gevers, J. Ordieres-Meré, M. Buchwitz and W. Wellbrock, “Deep Learning for Industrial Computer Vision quality Control in the printing Industry 4.0,” Sensors, vol. 19, no 18, p. 3987, 2019.
J. Wang, P. Fu and R. X. Gao, “Machine vision intelligence for product defect inspection based on deep learning and Hough transform,” Journal of Manufacturing Systems, vol. 51, pp. 52-60, 2019.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1506, 2015.
G. Sambasivam and G. D. Opiyo, “A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks,” Egyptian Informatics Journal, 2020.
J. M. Johnson and T. M. Khoshgoftaar, “Survey on deep learning with class imbalance,” Journal of Big Data, vol. 6, no 1, p. 27, 2019.
J. Han, J. Pei and M. Kamber, Data mining: Concepts and techniques, Elsevier, 2011.
W. Ding, D.-Y. Huang, Z. Chen, X. Yu and W. Lin, “Facial action recognition using very deep networks for highly imbalanced class distribution,” 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1368-1372, 2017.
H. Hamledari, B. McCabe and S. Davari, “Automated computer vision-based detection of components of under-construction indoor partitions,” Automation in Construction, vol. 74, pp. 78-92, 2017.
Y. Wu, D. Guo, H. Liu and Y. Huang, “An end-to-end learning method for industrial defect detection,” Assembly Automation, vol. 40, pp. 31-39, 2019.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu and X. Zheng, “TensorFlow: A System for Large-Scale Machine Learning,” 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), pp. 265-283, 2016.
Y. Wu, D. Guo, H. Liu and Y. Huang, “An end-to-end learning method for industrial defect detection,” Assembly Automation, pp. 31-39, 2019.
D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” International Conference on Learning Representations (ICLR), 2015.
Y. Dauphin, H. De Vries, Y. Bengio, “RMSprop and equilibrated adaptive learning rates for non-convex optimization,” Advances in neural information processing systems, pp. 1504-1512, 2015.
Downloads
Published
How to Cite
Issue
Section
License
Declaração de originalidade e cessão de direitos autorais
Declaro que o presente artigo é original, não está sendo tendo sido submetido à publicação em qualquer outro periódico nacional ou internacional durante o processo de revisão. Através deste instrumento, em meu nome e em nome dos demais co-autores, porventura existentes, cedo os direitos autorais do referido artigo à revista SCIENTIA CUM INDUSTRIA. Contudo, a reprodução total ou parcial impressa ou eletrônica pode ser feita desde que o autor comunique oficialmente à revista. Declaro estar ciente de que a não observância deste compromisso submeterá o infrator a sanções e penas previstas na Lei de Proteção de Direitos Autorias. Declaro estar ciente de que a não observância deste compromisso submeterá o infrator a sanções e penas previstas na Lei de Proteção de Direitos Autorias (Nº9610, de 19/02/1998).
