Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

Jakubowski, Jakub; Stanisz, Przemysław; Bobek, Szymon; Nalepa, Grzegorz J.

doi:10.3390/s22010291

Cited by 56Machine Fault Diagnosis TechniquesFault Detection and Control SystemsAnomaly Detection Techniques and Applications

Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

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Jakubowski, Jakub, Stanisz, Przemysław, Bobek, Szymon, Nalepa, Grzegorz J.·2021·Sensors

DOI 10.3390/s22010291PMC PMC8749861

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Abstract

Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a steel-making site. As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. The results show that the variational autoencoder slightly outperforms the base autoencoder architecture in anomaly detection tasks. However, its performance on the real use-case does not make it a production-ready solution for industry and should be a matter of further research. Furthermore, the information obtained from the explainability model can increase the reliability of the proposed artificial intelligence-based solution.

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Shapley values-based techniques such as SHAP are typically used to obtain feature contributions for explaining autoencoders.
“First of all, Shapley values-based techniques such as SHAP are typically used to obtain feature contributions for explaining AEs.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [91]
The Introduction and Section 5 describe using SHAP to identify feature contributions to anomalies detected by a variational autoencoder, via a random-forest surrogate model. The paper demonstrates this use, but does not establish that Shapley-value techniques are typically used to explain autoencoders. (LLM, conf=0.85)
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Isolation Forest
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.17 · Cited by 6,601 · see citing works
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
Adadi, A., & Berrada, M. (2018) · IEEE Access · doi:10.1109/access.2018.2870052 · Cited by 6,294 · see citing works
Untitled work
(2017) · International Conference on Learning Representations · Cited by 3,248 · see citing works
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634 · see citing works
Remaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162 · see citing works
Damage propagation modeling for aircraft engine run-to-failure simulation
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008) · 2008 International Conference on Prognostics and Health Management · doi:10.1109/phm.2008.4711414 · Cited by 1,954 · see citing works
Consistent Individualized Feature Attribution for Tree Ensembles
Lundberg, S. M., Erion, G. G., & Lee, S.-I. (2018) · arXiv · doi:10.48550/arxiv.1802.03888 · Cited by 1,430 · see citing works
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., & Cox, D. D. (2015) · Computational Science & Discovery · doi:10.1088/1749-4699/8/1/014008 · Cited by 1,002 · see citing works
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
Zhang, W., Yang, D., & Wang, H. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2905565 · Cited by 732 · see citing works
Recurrent neural networks for remaining useful life estimation
Heimes, F. O. (2008) · International Conference on Prognostics and Health Management (PHM) · doi:10.1109/phm.2008.4711422 · Cited by 655 · see citing works
Industrial internet of things: Recent advances, enabling technologies and open challenges
Khan, W. Z., Rehman, M. H. U., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106522 · Cited by 618 · see citing works
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Ellefsen, A. L., Bjørlykhaug, E., Æsøy, V., Ushakov, S., & Zhang, H. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.11.027 · Cited by 526 · see citing works
Direct Remaining Useful Life Estimation Based on Support Vector Regression
Khelif, R., Chebel‐Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Noureddine Zerhouni, A. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2623260 · Cited by 440 · see citing works
Remaining useful lifetime prediction via deep domain adaptation
da Costa, P. R. D. O., Akçay, A., Zhang, Y., & Kaymak, U. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2019.106682 · Cited by 390 · see citing works
Cyber-physical Systems Architecture for Self-Aware Machines in Industry 4.0 Environment
Bagheri, B., Yang, S., Kao, H.-A., & Lee, J. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.318 · Cited by 361 · see citing works
Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction
Mosallam, A., Medjaher, K., & Zerhouni, N. (2014) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-014-0933-4 · Cited by 329 · see citing works
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
Essien, A. E., & Giannetti, C. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2967556 · Cited by 318 · see citing works
A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance
Amruthnath, N., & Gupta, T. (2018) · IEEE International Conference on Industrial Engineering and Applications (ICIEA) · doi:10.1109/iea.2018.8387124 · Cited by 289 · see citing works
Recent advances in prognostics and health management for advanced manufacturing paradigms
Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., & Xi, L. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.06.021 · Cited by 270 · see citing works
Anomaly detection in temperature data using DBSCAN algorithm
Çelik, M., Dadaşer-Çelik, F., & Dokuz, A. Ş. (2011) · International Symposium on Innovations in Intelligent SysTems and Applications · doi:10.1109/inista.2011.5946052 · Cited by 205 · see citing works
Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
Zhang, A., Wang, H., Li, S., Cui, Y., Liu, Z., Yang, G., & Hu, J. (2018) · Applied Sciences · doi:10.3390/app8122416 · Cited by 184 · see citing works
A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study
Ramotsoela, D. T., Abu-Mahfouz, A. M. I., & Hancke, G. P. (2018) · Sensors · doi:10.3390/s18082491 · Cited by 158 · see citing works
A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine
DeCastro, J., Litt, J. S., & Frederick, D. K. (2008) · AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit (JPC) · doi:10.2514/6.2008-4579 · Cited by 111 · see citing works
System-wide anomaly detection in wind turbines using deep autoencoders
Renström, N., Bangalore, P., & Highcock, E. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.04.148 · Cited by 106 · see citing works
Anomaly detection in discrete manufacturing using self-learning approaches
Lindemann, B., Fesenmayr, F., Jazdi, N., & Weyrich, M. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.02.073 · Cited by 85 · see citing works
Data-driven learning framework for associating weather conditions and wind turbine failures
Reder, M., Yürüşen, N. Y., & Melero, J. J. (2017) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.10.004 · Cited by 83 · see citing works
Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach
Reddy, K. K., Sarkar, S., Venugopalan, V., & Giering, M. J. (2016) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2016.v8i1.2549 · Cited by 72 · see citing works
General overview of maintenance strategies – concepts and approaches
Gackowiec, P. (2019) · Multidisciplinary Aspects of Production Engineering · doi:10.2478/mape-2019-0013 · Cited by 67 · see citing works
Remaining Useful Life Prediction of Turbofan Engine Using Hybrid Model Based on Autoencoder and Bidirectional Long Short-Term Memory
Song, Y. J., Guo, S., Chen, L., Huang, X., & Xia, T. (2018) · Journal of Shanghai Jiaotong University (Science) · doi:10.1007/s12204-018-2027-5 · Cited by 51 · see citing works
Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance
Shukla, B., Fan, I., & Jennions, I. (2020) · PHM Society European Conference · doi:10.36001/phme.2020.v5i1.1231 · Cited by 44 · see citing works
Predictive and Explainable Machine Learning for Industrial Internet of Things Applications
Christou, I. T., Kefalakis, N., Zalonis, A., & Soldatos, J. K. (2020) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss49796.2020.00043 · Cited by 39 · see citing works
Explainable Artificial Intelligence for Predictive Maintenance Applications using a Local Surrogate Model
Torcianti, A., & Matzka, S. (2021) · International Conference on Artificial Intelligence for Industries (AI4I) · doi:10.1109/ai4i51902.2021.00029 · Cited by 36 · see citing works
An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals
Chen, G., Chen, J., Zi, Y., Pan, J. H., & Han, W. (2017) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2017.12.009 · Cited by 31 · see citing works
Explainable anomaly detection for Hot-rolling industrial process
Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G. J. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9564228 · Cited by 31 · see citing works
Self-citation
Explainable AI Framework for Imaging-Based Predictive Maintenance for Automotive Applications and Beyond
Krishnamurthy, V., Nezafati, K., Stayton, E., & Singh, V. (2020) · Data-Enabled Discovery and Applications · doi:10.1007/s41688-020-00042-2 · Cited by 27 · see citing works
Autoencoder based Semi-Supervised Anomaly Detection in Turbofan Engines
Bataineh, A. A., Mairaj, A., & Kaur, D. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111105 · Cited by 17 · see citing works
An unsupervised degradation estimation framework for diagnostics and prognostics in cyber-physical system
Wu, Z., Luo, H., Yang, Y., Zhu, X., & Qiu, X. (2018) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2018.8355191 · Cited by 15 · see citing works

Flagged first, then by citations

Research works printed but not linked: 2

[32]Kingma D.P. Welling M. Auto-Encoding Variational Bayes arXiv 2014 1312.6114
[42]Statistics L.B. Breiman L. Random Forests Machine Learning Springer Berlin/Heidelberg, Germany 2001 5 32

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Self-citation
Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot
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Authors: Jakubowski, J., Stanisz, P., Bobek, S., & Nalepa, G. J.
Affiliations: ArcelorMittal (Poland) (PL)
Jagiellonian University (PL)
Corresponding author: Jakubowski, Jakub
Bobek, Szymon
ORCID: Jakubowski, Jakub
Stanisz, Przemysław
Bobek, Szymon
Nalepa, Grzegorz J.
Venue: Sensors
Publisher: MDPI AG
Published: 2021-12-31
Volume: 22
Issue: 1
Pages: 291
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s22010291
PMC: PMC8749861
OpenAlex: W4205525990
Cited by: 56
References: 40
Citations by year: 2026: 11 · 2025: 12 · 2024: 14 · 2023: 15 · 2022: 4
FWCI: 3.67
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by-nc-nd)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: National Science Center (857925 (NCN UMO-2020/02/Y/ST6/00070))
Topics: Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Keywords: anomaly detection, asset degradation, variational autoencoder, unsupervised learning, predictive maintenance, hot strip mill, turbofan engine, explainability, model interpretability
MeSH terms: Machine Learning, Artificial Intelligence, Industry, Reproducibility of Results

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Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations

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