Explainable Models for Multivariate Time-series Defect Classification of Arc Stud Welding
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Published
Feb 13, 2023
Sadra Naddaf Shargh
Mahdi Naddaf-Sh Maxim Dalton Soodabeh Ramezani Amir R. Kashani Hassan Zargarzadeh
https://orcid.org/0000-0003-1879-901X
Mahdi Naddaf-Sh Maxim Dalton Soodabeh Ramezani Amir R. Kashani Hassan Zargarzadeh
Abstract
Arc Stud Welding (ASW) is widely used in many industries such as automotive and shipbuilding and is employed in building and jointing large-scale structures. While defective or imperfect welds rarely occur in production, even a single low-quality stud weld is the reason for scrapping the entire structure, financial loss and wasting time. Preventive machine learning-based solutions can be leveraged to minimize the loss. However, these approaches only provide predictions rather than demonstrating insights for characterizing defects and root cause analysis. In this work, an investigation on defect detection and classification to diagnose the possible leading causes of low-quality defects is proposed. Moreover, an explainable model to describe network predictions is explored. Initially, a dataset of multi-variate time-series of ASW utilizing measurement sensors in an experimental environment is generated. Next, a set of pre-possessing techniques are assessed. Finally, classification models are optimized by Bayesian black-box optimization methods to maximize their performance. Our best approach reaches an F1-score of 0.84 on the test set. Furthermore, an explainable model is employed to provide interpretations on per class feature attention of the model to extract sensor measurement contribution in detecting defects as well as its time attention.
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Keywords
Time Series, Multivariate, Arc Stud Welding, Machine Learning, Defect Classification, weld quality assessment, ICA, Explainable AI, Bayesian Optimization
References
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Stud welding products systems. (2021). Retrieved from http://www.emhart.eu/eu-en/products-services/products-by-category/tucker-stud-welding/stud-welding-systems/energy-units.php (Accessed: 05-26-21)
Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022.
Wang, Z., Yan, W., & Oates, T. (2017). Time series classification from scratch with deep neural networks: A strong baseline. In 2017 international joint conference on neural networks (ijcnn) (pp. 1578–1585).
Zhang, W., Jha, D. K., Laftchiev, E., & Nikovski, D. (2019). Multi-label prediction in time series data using deep neural networks. International Journal of Prognostics and Health Management, 10(4).
Assaf, R., Giurgiu, I., Bagehorn, F., & Schumann, A. (2019). Mtex-cnn: Multivariate time series explanations for predictions with convolutional neural networks. In 2019 ieee international conference on data mining (icdm) (p. 952-957). doi: 10.1109/ICDM.2019.00106
Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Baydogan, M. G., Runger, G., & Tuv, E. (2013). A bag-of-features framework to classify time series. IEEE transactions on pattern analysis and machine intelligence, 35(11), 2796–2802.
Chambers, H. A. (2001). Principles and practices of stud welding. Pci Journal, 46(5), 46–59.
Chen, K.-H., & Khashanah, K. (2015). The reconstruction of financial signals using fast ica for systemic risk. In 2015 ieee symposium series on computational intelligence (p. 885-889). doi: 10.1109/SSCI.2015.130
Dong, J., Xu, G., Yu, H., Fan, G., Wei, L., & Gu, X. (2019). Connection status evaluation in arc stud weld joints by ultrasonic detection. The International Journal of Advanced Manufacturing Technology, 100(1), 663–672.
Ducoffe, M., Haloui, I., & Gupta, J. S. (2019). Anomaly detection on time series with wasserstein gan applied to phm. International Journal of Prognostics and Health Management, 10(4).
Fauvel, K., Lin, T., Masson, V., Fromont, É., & Termier, A. (2020). Xcm: An explainable convolutional neural network for multivariate time series classification. arXiv preprint arXiv:2009.04796.
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019). Deep learning for time series classification: a review. Data Mining and Knowledge Discovery, 33(4), 917–963.
Goldin, D. Q., & Kanellakis, P. C. (1995). On similarity queries for time-series data: constraint specification and implementation. In International conference on principles and practice of constraint programming (pp. 137–153).
Gòrecki, T., & Łuczak, M. (2015). Multivariate time series classification with parametric derivative dynamic time warping. Expert Systems with Applications, 42(5), 2305-2312. Retrieved from https://www.sciencedirect.com/science/article/pii/S0957417414006927 doi: https://doi.org/10.1016/j.eswa.2014.11.007
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the ieee international conference on computer vision (pp. 1026–1034).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 770–778).
Heidarydashtarjandi, R., Prasad-Rao, J., & Groth, K. M. (2022). Optimal maintenance policy for corroded oil and gas pipelines using markov decision processes. International Journal of Prognostics and Health Management, 13(1).
Hildebrand, J., & Soltanzadeh, H. (2014). A review on assessment of fatigue strength in welded studs. International Journal of Steel Structures, 14(2), 421–438.
Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural networks, 13(4-5), 411–430.
Keogh, E., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and information systems, 7(3), 358–386.
Naddaf-Sh, M.-M., Naddaf-Sh, S., Zargarzadeh, H., Zahiri, S. M., Dalton, M., Elpers, G., & Kashani, A. R. (2021). Defect detection and classification in welding using deep learning and digital radiography. In Fault diagnosis and prognosis techniques for complex engineering systems (pp. 327–352). Elsevier.
O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., et al. (2019). Kerastuner. https://github.com/keras-team/keras-tuner.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., . . . others (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12, 2825–2830.
Ramasamy, S., Gould, J., & Workman, D. (2002). Design-of-experiments study to examine the effect of polarity on stud welding. WELDING JOURNAL-NEW YORK-, 81(2), 19–S.
Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49. doi: 10.1109/TASSP.1978.1163055
Samardzic, I., Klaric, S., & Siewert, T. (2007, 2007-07-15). Analysis of welding parameter distribution in stud arc welding. Proceedings of the Conference: Welding and Materials, Dubrovnik, Croatia, YU. Retrieved from https://tsapps.nist.gov/publication/get_pdf.cfm?pub id=50536
Schäfer, P. (2015). The boss is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery, 29(6), 1505–1530.
Serrà, J., Pascual, S., & Karatzoglou, A. (2018). Towards a universal neural network encoder for time series. In Ccia (pp. 120–129).
Stud welding products systems. (2021). Retrieved from http://www.emhart.eu/eu-en/products-services/products-by-category/tucker-stud-welding/stud-welding-systems/energy-units.php (Accessed: 05-26-21)
Ulyanov, D., Vedaldi, A., & Lempitsky, V. (2016). Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022.
Wang, Z., Yan, W., & Oates, T. (2017). Time series classification from scratch with deep neural networks: A strong baseline. In 2017 international joint conference on neural networks (ijcnn) (pp. 1578–1585).
Zhang, W., Jha, D. K., Laftchiev, E., & Nikovski, D. (2019). Multi-label prediction in time series data using deep neural networks. International Journal of Prognostics and Health Management, 10(4).
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