پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 417 |
| تعداد مقالات | 5,494 |
| تعداد مشاهده مقاله | 6,135,932 |
| تعداد دریافت فایل اصل مقاله | 5,380,606 |
Compound Fault Detection of Rotating Machinery in Unobserved Conditions Using Missing Data | ||
| AUT Journal of Mechanical Engineering | ||
| دوره 8، شماره 2، 2024، صفحه 199-208 اصل مقاله (1.92 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2024.22767.6073 | ||
| نویسندگان | ||
| Somaye Mohammadi* 1؛ Mehdi Behzad1؛ Hesam Addin Arghand2 | ||
| 1Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran | ||
| 2Department of Mechanical Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran | ||
| چکیده | ||
| In recent years, there has been a rise in the popularity of using data-driven artificial intelligence models for detecting faults in rotating machinery. The challenge lies in creating a model that can be used even when sensor data is not available and the operating conditions differ from those observed during development. This article addresses the issue of potential failures in gear, bearing, and shaft components and suggests two strategies - adjusting entry and cost functions - to address these challenges in developing a one-dimensional convolutional neural network model. These strategies enable the model to extract features from the input signal with minimal dependency on operating conditions. By analyzing the 2009 PHM (Prognostics and Health Management Society) challenge competition dataset, the model achieved its highest accuracy by using the frequency spectrum of velocity and acceleration from vibrational signals. The model’s average accuracy for signals recorded by any arbitrary sensor is 79.6%, even if some operating speeds were not observed during training. Incorporating a suggested penalty function based on p-value into the cost function increased accuracy by up to 13.6%. Consequently, implementing the proposed strategies in similar cases is highly recommended, as demonstrated by successful application in two industrial cases. | ||
| کلیدواژه‌ها | ||
| Condition Monitoring؛ Fault Detection؛ Convolutional Neural Network؛ Gear Fault؛ Rolling Bearing؛ Rotor Faults؛ Missing Data | ||
| مراجع | ||
|
[1] X. Lyu, Z. Hu, H. Zhou, Q. Wang, Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis, Measurement, 139 (2019) 236-248.
[2] H. Zhiyi, S. Haidong, Z. Xiang, Y. Yu, C. Junsheng, An intelligent fault diagnosis method for rotor-bearing system using small labeled infrared thermal images and enhanced CNN transferred from CAE, Advanced Engineering Informatics, 46 (2020) 101150.
[3] L. Xin, S. Haidong, J. Hongkai, X. Jiawei, Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds, Structural Health Monitoring, 21(2) (2022) 339-353.
[4] Y. Xue, D. Dou, J. Yang, Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine, Measurement, 156 (2020) 107571.
[5] P.J. García-Laencina, J.L. Sancho-Gómez, A.R. Figueiras-Vidal, Machine learning techniques for solving classification problems with missing input data, in: Proceedings of the 12th World Multi-Conference on Systems, Cybernetics and Informatics, 2008, pp. 1-6.
[6] Z. Zhang, F. Dong, Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach, Chemometrics and Intelligent Laboratory Systems, 138 (2014) 30-40.
[7] K. Zhang, R. Gonzalez, B. Huang, G. Ji, Expectation–maximization approach to fault diagnosis with missing data, IEEE Transactions on Industrial Electronics, 62(2) (2014) 1231-1240.
[8] W. Liu, D. Wei, F. Zhou, Fault diagnosis based on deep learning subject to missing data, in: 2018 Chinese control and decision conference (CCDC), IEEE, 2018, pp. 3972-3977.
[9] S. Venkatasubramanian, S. Raja, V. Sumanth, J.N. Dwivedi, J. Sathiaparkavi, S. Modak, M.L. Kejela, Fault diagnosis using data fusion with ensemble deep learning technique in IIoT, Mathematical Problems in Engineering, 2022(1) (2022) 1682874.
[10] B. Zhang, W. Li, X.-L. Li, S.-K. Ng, Intelligent fault diagnosis under varying working conditions based on domain adaptive convolutional neural networks, Ieee Access, 6 (2018) 66367-66384.
[11] T. Han, C. Liu, W. Yang, D. Jiang, A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults, Knowledge-based systems, 165 (2019) 474-487.
[12] J. Shao, Z. Huang, Y. Zhu, J. Zhu, D. Fang, Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation, Advances in Mechanical Engineering, 13(8) (2021) 16878140211040226.
[13] T. Han, C. Liu, W. Yang, D. Jiang, Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions, ISA transactions, 93 (2019) 341-353.
[14] X. Li, Y. Hu, M. Li, J. Zheng, Fault diagnostics between different type of components: A transfer learning approach, Applied Soft Computing, 86 (2020) 105950.
[15] https://phmsociety.org/data-analysis-competition/, in, 2024.
[16] M. Ozeki, T. Okatani, Understanding convolutional neural networks in terms of category-level attributes, in: Computer Vision--ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part II 12, Springer, 2015, pp. 362-375.
[17] C.M. Bishop, Pattern recognition and machine learning, Springer google schola, 2 (2006) 1122-1128. | ||
|
آمار تعداد مشاهده مقاله: 263 تعداد دریافت فایل اصل مقاله: 225 |
||