Abstract
Within the context of Industry 4.0, quality assessment procedures using data-driven techniques are becoming more critical due to the generation of massive amounts of production data. In this paper, we address the detection of abnormal screw tightening processes, which is a relevant industrial task. Since labeling is costly, requiring a manual effort, we focus on unsupervised approaches. In particular, we assume a low-dimensional input screw fastening approach that is based only on angle-torque pairs. Using such pairs, we explore three main unsupervised Machine Learning (ML) algorithms: Local Outlier Factor (LOF), Isolation Forest (iForest) and a deep learning Autoencoder (AE). For benchmarking purposes, we also explore a supervised Random Forest (RF) algorithm. Several computational experiments were held by using recent industrial data with 2.8 million angle-torque pair records and a realistic and robust rolling window evaluation. Overall, high quality anomaly discrimination results were achieved by the iForest (99%) and AE (95% and 96%) unsupervised methods, which compared well against the supervised RF (99% and 91%). When compared with iForest, the AE requires less computation effort and provides faster anomaly detection response times.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alla, S., Adari, S.K.: Beginning Anomaly Detection Using Python-Based Deep Learning. Apress, Berkeley (2019). https://doi.org/10.1007/978-1-4842-5177-5_8
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Breunig, M.M., Kriegel, H., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Chen, W., Naughton, J.F., Bernstein, P.A. (eds.) Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16–18, 2000, Dallas, Texas, USA, pp. 93–104. ACM (2000). https://doi.org/10.1145/342009.335388
Cao, N., Lin, Y.R., Gotz, D., Du, F.: Z-glyph: visualizing outliers in multivariate data. Inf. Vis. 17(1), 22–40 (2018). https://doi.org/10.1177/1473871616686635
Cao, X., Liu, J., Meng, F., Yan, B., Zheng, H., Su, H.: Anomaly detection for screw tightening timing data with LSTM recurrent neural network. In: 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019, Shenzhen, China, December 11–13, 2019, pp. 348–352. IEEE (2019). https://doi.org/10.1109/MSN48538.2019.00072
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009). https://doi.org/10.1145/1541880.1541882
Diez-Olivan, A., Penalva, M., Veiga, F., Deitert, L., Sanz, R., Sierra, B.: Kernel density-based pattern classification in blind fasteners installation. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 195–206. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_17
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010
Gulli, A., Pal, S.: Deep learning with Keras. Packt Publishing Ltd, Birmingham (2017)
Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647
Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley, Hoboken (2013)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Bach, F.R., Blei, D.M. (eds.) Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6–11 July 2015. JMLR Workshop and Conference Proceedings, vol. 37, pp. 448–456. JMLR.org (2015)
Liu, F.T., Ting, K.M., Zhou, Z.: Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15–19, 2008, Pisa, Italy, pp. 413–422. IEEE Computer Society (2008). https://doi.org/10.1109/ICDM.2008.17
Matos, L.M., Cortez, P., Mendes, R., Moreau, A.: Using deep learning for mobile marketing user conversion prediction. In: International Joint Conference on Neural Networks, IJCNN 2019 Budapest, Hungary, July 14–19, 2019. pp. 1–8. IEEE (2019). https://doi.org/10.1109/IJCNN.2019.8851888
Matsuno, T., Huang, J., Fukuda, T.: Fault detection algorithm for external thread fastening by robotic manipulator using linear support vector machine classifier. In: 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, May 6–10, 2013, pp. 3443–3450. IEEE (2013). https://doi.org/10.1109/ICRA.2013.6631058
Pedregosa, F., et al.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). http://dl.acm.org/citation.cfm?id=2078195
Pereira, P.J., Cortez, P., Mendes, R.: Multi-objective grammatical evolution of decision trees for mobile marketing user conversion prediction. Expert Syst. Appl. 168, 114287 (2021). https://doi.org/10.1016/j.eswa.2020.114287
Ponpitakchai, S.: Monitoring screw fastening process: an application of SVM classification. Naresuan Univ. Eng. J. (NUEJ) 11(2), 1–5 (2016). https://doi.org/10.14456/nuej.2016.11
Ruff, L., et al.: Deep one-class classification. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10–15, 2018. Proceedings of Machine Learning Research, vol. 80, pp. 4390–4399. PMLR (2018). http://proceedings.mlr.press/v80/ruff18a.html
Tashman, L.J.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. J. Forecast. 16(4), 437–450 (2000). https://doi.org/10.1016/S0169-2070(00)00065-0
Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13–17, 2017. pp. 665–674. ACM (2017). https://doi.org/10.1145/3097983.3098052
Acknowledgments
This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n 39479; Funding Reference: POCI-01-0247-FEDER-39479]. The authors also wish to thank the automotive electronics company staff involved with this project for providing the data and the valuable domain feedback.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Ribeiro, D., Matos, L.M., Cortez, P., Moreira, G., Pilastri, A. (2021). A Comparison of Anomaly Detection Methods for Industrial Screw Tightening. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12950. Springer, Cham. https://doi.org/10.1007/978-3-030-86960-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-86960-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86959-5
Online ISBN: 978-3-030-86960-1
eBook Packages: Computer ScienceComputer Science (R0)