Camera-based PHM method in rotating machinery equipment micro-action scenarios

• Autorzy: Junfeng An , Liu Jiqiang , Zhen Hao , Mengmeng Lu

Identyfikatory

DOI

10.17531/ein.2023.1.10

• Języki publikacji: EN

Abstrakty

EN

The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

Słowa kluczowe

EN

deep learning; condition monitoring; Rmcad; anomaly detection; defect early warning

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2023
• Tom: Vol. 25, no. 1
• Strony: art. no. 10
• Opis fizyczny: Bibliogr. 27 poz., rys., tab., wykr.

Twórcy

autor

Junfeng An

• Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China
• Jinan Rail Transit Group Co., LTD, Jinan, 250000, China

• https://orcid.org/0000-0002-6303-131X

autor

Liu Jiqiang

• Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China

• https://orcid.org/0000-0003-1147-4327

autor

Zhen Hao

• Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing, 100044, China

• https://orcid.org/0000-0002-1462-1700

autor

Mengmeng Lu

• Shandong Labor Vocational and Technical College, Jinan, 250103, China

Bibliografia

• 1. Al-Kahwati, K., Birk, W., Nilsfors, E. F., and Nilsen, R. Experiences of a digital twin based predictive maintenance solution for belt conveyor systems. In PHM Society European Conference, volume 7, pages 1–8, 2022. http://dx.doi.org/10.36001/phme.2022.v7i1.3355.
• 2. Bielecki, A., Bielecka, M., Jablonski, A., and Staszewski, W. Simple method of failure detection of rotary machines. Diagnostyka, 22, 2021. http://dx.doi.org/10.29354/diag/142862.
• 3. Blair, J., Stephen, B., Brown, B., Forbes, A., and Mcarthur, S. Hybrid fault prognostics for nuclear applications: addressing rotating plant model uncertainty. In PHM Society European Conference, volume 7, pages 58–67, 2022. http://dx.doi.org/10.36001/phme.2022.v7i1.3321.
• 4. Borucka A. Three-state Markov model of using transport means. Business Logistics In Modern Management 2018:. 3-19.
• 5. Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pages 1597–1607. PMLR, 202.https://doi.org/10.48550/arXiv.2002.05709
• 6. Dyer, C. Notes on noise contrastive estimation and negative sampling. arXiv preprint arXiv:1410.8251, 2014, https://doi.org/10.48550/arXiv.1410.8251
• 7. Grzelak M, Borucka A, Guzanek P. Application of linear regression for evaluation of production processes effectiveness. In International Conference Innovation in Engineering 2021: 36-47, Springer, Cham, https://doi.org/10.1007/978-3-030-78170-5_4
• 8. He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016. http://dx.doi.org/ 10.1109/CVPR.2016.90.
• 9. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700–4708, 2017. http://dx.doi.org/10.1109/CVPR.2017.243.
• 10. Klosowski, G., Rymarczyk, T., Kania, K., Swic, A., and Cieplak, T. Maintenance of industrial reactors supported by deep learning driven ultrasound tomography. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 22(1):138–147, 2020. http://dx.doi.org/10.17531/ein.2020.1.16.
• 11. Li, R., Chu, Z., Jin, W., Wang, Y., and Hu, X. Temporal convolutional network based regression approach for estimation of remaining useful life. In 2021 IEEE International Conference on Prognostics and Health Management, pages 1–10. IEEE, 2021. http://dx.doi.org/10.1109/ICPHM51084.2021.9486528.
• 12. Mahboub, M. A., Rouabah, B., Kafi, M. R., and Toubakh, H. Health management using fault detection and fault tolerant control of multicellular converter applied in more electric aircraft system. Diagnostyka, 23, 2022. http://dx.doi.org/10.29354/diag/151039.
• 13. Nowakowski, T. and Komorski, P. Diagnostics of the drive shaft bearing based on vibrations in the high- frequency range as a part of the vehicle’s self-diagnostic system. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 24(1):70–79, 2022. http://dx.doi.org/10.17531/ein.2022.1.9.
• 14. Ochella, S., Shafiee, M., and Dinmohammadi, F. Artificial intelligence in prognostics and health man- agement of engineering systems. Engineering Applications of Artificial Intelligence, 108:104552, 2022. http://dx.doi.org/10.1016/j.engappai.2021.104552.
• 15. Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Doll´ar, P. Designing network design spaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10428– 10436, 2020. http://dx.doi.org/10.1109/CVPR42600.2020.01044.
• 16. Rodriguez-Picon, L. A., Mendez-Gonzailez, L. C., Perez-Olguin, I. J., and Hernandez-Hernandez, J. I. A study of the inverse gaussian process with hazard rate functions-based drifts applied to degradation modelling. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 24(3):590–602, 2022.http://dx. doi.org/10.17531/ein.2022.3.20.
• 17. Ruan, Y., Yuan, L., He, Y., Li, Z., Yuan, W., and Lu, L. Prognostics and health management for piezoresistive pressure sensor based on improved gated recurrent unit networks. Measurement Science and Technology, 33(11):115112, 2022. http://dx.doi.org/10.1088/1361-6501/ac81a0.
• 18. Sawczuk, W., Merkisz-Guranowska, A., and Rilo Canas, A.-M. Assessment of disc brake vibration in rail vehicle operation on the basis of brake stand. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 23(2):221–230, 2021. http://dx.doi.org/10.17531/ein.2021.2.2.
• 19. Taghiyarrenani, Z. and Berenji, A. An analysis of vibrations and currents for broken rotor bar detection in three-phase induction motors. In PHM Society European Conference, volume 7, pages 43–48, 2022. http://dx.doi.org/10.36001/phme.2022.v7i1.3332.
• 20. Vaiciunas, G., Bureika, G., and Steisunas, S. Rail vehicle axle-box bearing damage detection considering the intensity of heating alteration. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 22(4): 724–729, 2020. http://dx.doi.org/10.17531/ein.2020.4.16.
• 21. Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. Eca-net: Efficient channel attention for deep convolutional neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11534–11542, 2020. http://dx.doi.org/10.1109/CVPR42600.2020.01155.
• 22. Wen, X., Lu, G., Liu, J., and Yan, P. Graph modeling of singular values for early fault detection and diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 145:106956, 2020. http://dx.doi.org/10.1016/j.ymssp.2020.106956.
• 23. Xue, B., Xu, Z.-b., Huang, X., and Nie, P.-c. Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network. Journal of Mechanical Science and Technology, 35(12):5371–5387, 2021. https://doi.org/10.1007/s12206-021-1109-8.
• 24. Yao, Z., He, D., Chen, Y., Liu, B., Miao, J., Deng, J., and Shan, S. Inspection of exterior substance on high-speed train bottombased on improved deep learning method. Measurement, 163:108013, 2020. https://doi.org/10.1016/j.measurement.2020.108013.
• 25. Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310–12320. PMLR, 2021.https://doi.org/10.48550/arXiv.2103.03230
• 26. Zhao, L., Zhu, Y., and Zhao, T. Deep learning-based remaining useful life prediction method with transformer module and random forest. Mathematics, 10(16):2921, 2022. http://dx.doi.org/10.3390/ math10162921.
• 27. Zhou, F., Xia, L., Dong, W., Sun, X., Yan, X., and Zhao, Q. Fault diagnosis of high-speed railway turnout based on support vector machine. In 2016 IEEE International Conference on Industrial Technology, pages 1539–1544. IEEE, 2016. http://dx.doi.org/10.3390/math10162921

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).