ДЕРЕКТЕРДІ ІРІКТЕУДІҢ ӨНДІРІСТІК ЖАБДЫҚТАРДЫ ДИАГНОСТИКАЛАУДАҒЫ ҮЛГІЛЕРДІ ТАНУ МІНДЕТІН ШЕШУГЕ ӘСЕРІ

Мақалада өнеркәсіптік жабдықты диагностикалау кезінде классификатордың болжамдық қабілетіне деректерді іріктеу әдістерінің әсері зерттеледі. Қарастырылған деректерді іріктеу әдістеріне қарапайым кездейсоқ таңдау, кластерлік таңдау және жүйелі таңдау жатады. Іріктеу әдістерінің нәтижелеріне сәйкес, бөлшектер тобын оңтайландыру және ансамбльдік үлгілерге (қаптау және дауыс беру түрлері) негізделген классификаторлар әзірленді. Нейрондық желі, градиентті күшейтілген ағаштар және Бейс үлгілері негізіндегі болжауды біріктіретін дауыс беру ансамбльдік модельдеу стратегиясы ең үздік нәтижені көрсетті. Ең жоғары дәлдікке нейрондық желі, градиентті күшейтілген ағаштар және аңғал Бейс үлгілері негізіндегі болжауды біріктіретін дауыс беру ансамбльдік модельдеу стратегиясы мен деректерді жүйелі таңдау әдісін қолдану арқылы қол жеткізілді: дәлдік (accuracy) 93,6%; классификация қателігі (classification error) 8%; еске түсіру (recall) 94,32%; дәлдік (precision) 93,87%. Соңғы кезеңде, деректерді іріктеу негізінде жабдықты диагностикалаудың ең тиімді стратегиясы мен ансамбльдік модель үлкен деректермен жұмыс істеуге бейімделген жақсартылған нұсқаны әзірлеу мақсатында FMEA (Failure Mode and Effects Analysis) технологиясына енгізілді.

1. Samigulina G., Samigulina Z. Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems. Journal of Intelligent Manufacturing, Springer, 2022, vol. 33, pp.1433–1450.

2. Samigulina G., Samigulina Z. Biologically Inspired Unified Artificial Immune System for Industrial Equipment Diagnostic. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, 2023, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_7.

3. Elton P De Souza, Lis Moura, Thiago Barroso Costa, João Lucas Lobato Soares. Convolutional neural networks for pattern-based fault diagnosis in low-rotation equipment. International congress of mechanical engineering, 2023.

4. Leandro Ventricci, Ronny Francis Ribeiro Junior, Guilherme Ferreira Gomes. Motor fault classification using hybrid short-time Fourier transform and wavelet transform with vibration signal and convolutional neural network. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2024, vol. 6, p. 46.

5. Qiushi Wang, Zhicheng Sun, Yueming Zhu, Chunhe Song. Intelligent fault diagnosis algorithm of rolling bearing based on optimization algorithm fusion convolutional neural network. Mathematical Biosciences & Engineering, 2023, vol. 11, pp. 63–82.

6. Renjie Wang, Ningyuan Yu, Bin An. Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm. Journal of Electrical Systems, 2024, vol. 5, pp. 112–125.

7. Tian Y., Liu X. A Deep Adaptive Learning Method for Rolling Bearing Fault Diagnosis Using Immunity. Tsinghua Science and Technology, 2019, vol. 24, no. 6, pp. 1–14.

8. Sahu S., Kumar P.B., Parhi D.R. Analysis of hybrid CSA-DEA method for fault detection of cracked structures. Journal of Theoretical and Applied Mechanics, 2019, vol. 57, no. 2, pp. 369–382.

9. Pinto C., Pinto R., Gonçalves G. Towards. Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm. Algorithms, 2022, vol. 15, no. 1, pp. 1–28.

10. Xiaochen Zhang, Chen Wang, Wei Zhou, Jiajia Xu. Trustworthy Diagnostics With Out-of-Distribution Detection: A Novel Max-Consistency and Min-Similarity Guided Deep Ensembles for Uncertainty Estimation. IEEE Internet of Things Journal, 2024, vol. 1.1, pp. 99–120.

11. Meng Wang, Jiong Yu, Hongyong Leng, Xusheng Du. Bearing fault detection by using graph autoencoder and ensemble learningю Scientific Reports, 2024, vol.14, no. 1.

12. Weihua Li, Jingke He, Huibin Lin, Ruyi Huang. A LightGBM-based Multi-scale Weighted Ensemble Model for Few-shot Fault Diagnosis. IEEE Transactions on Instrumentation and Measurementm, 2023, vol. 1, p. 99.

13. Arnaud Nanfak, Charles Hubert Kom, Samuel Eke. Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios. International Journal of Intelligent Engineering and Systems, 2022, vol. 5, pp. 12–24.

14. Zhiyuan Chen, Olugbenro. O. Selere, Nicholas Lu Chee Seng. Equipment Failure Analysis for Oil and Gas Industry with an Ensemble Predictive Model. Proceedings of the 9th International Conference on Computational Science and Technology, 2023, pp. 569–581.

15. Hezla L., Gurina R., Hezla M., Rezaeian N. The Role of Artificial Intelligence in Improving Failure Mode and Effects Analysis (FMEA) Efficiency in Construction Safety Management. AI Technologies and Virtual Reality, 2024, pp. 397–411.

16. Podoplelova E.S., Knyazev I.I. Modification of the fmea method using machine learning algorithms. Izvestiya SFedU engineering sciences, 2023.

17. Jiao J., Zhao M., Lin J. and Ding C. Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis. IEEE Trans. Ind. Electron, 2019, vol. 6, pp. 92–98.

18. Lei Y., Jia F., Lin J., Xing S. and Ding S.X. An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data. IEEE Trans. Ind. Electron., 2019, p. 78.

19. Dimitar P. Filev, Ratna Babu Chinnam, Finn Tseng and Pundarikaksha Baruah. An Industrial Strength Novelty Detection Framework for Autonomous Equipment Monitoring and Diagnostics. IEEE Transactions on Industrial Informatics, 2010, vol. 4, pp. 61–78.

20. Venkatasubramanian V., Rengaswamy R., Yin K. and Kavuri S.N. A review of process fault detection and diagnosis: Part I: Quantitative model-based methods. Computers & Chemical Engineering, 2003, vol. 27, no. 9, pp. 293–311.

21. Hwang I., Kim S., Kim Y. and Seah C.E. A Survey of Fault Detection, Isolation, and Reconfiguration Methods. IEEE Transactions on Control Systems Technology, 2010, vol.18, no. 3, pp. 636–653.

22. Lei Y., Lin J., He Z. and Zuo M.J. Condition monitoring and fault diagnosis of planetary gearboxes: A review. Measurement journal, 2014, vol.35, pp. 108–126.

23. Henriquez P., Alonso J.B., Ferrer M.A. and Travieso C.M. Automatic Fault Diagnosis Systems Using Audio and Vibration Signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2012, vol. 5, pp. 642–652.

24. Yan R., Gao R.X. and Chen X. Non-stationary signal processing for bearing health monitoring. Signal Processing, 2014, vol. 1.

25. Venkatasubramanian V., Rengaswamy R., Kavuri S.N. and Yin K. A review of process fault detection and diagnosis. Compuers & chemical engineering, Part III: Process history based methods, 2003, pp. 327–346.

26. Yin S., Ding S.X., Xie X. and Luo H. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring. IEEE Transactions on Industrial Electronic, 2014, vol. 11, pp. 6418–6428.

27. Ding S.X. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control., 2014, vol. 24, no. 2, pp. 431–449.

28. Gao Z., Cecati C. and Ding S.X. Fault Diagnosis and Fault-Tolerant Techniques, Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics, Part I, 2015, vol. 62, pp. 3757–3767.

29. Gao Z., Cecati C. and Ding S.X. Fault Diagnosis and Fault-Tolerant Techniques, Part II, IEEE Transactions on Industrial Electronics, 2015.

30. Evstifeev A.A. and Zaeva M.A. A hybrid teaching factory model towards personalized education. Method of Applying Fuzzy Situational Network to Assess the Risk of the Industrial Equipment Failure, 2021.

31. Mourtzis D., Angelopoulos J. and Panopoulos N., 2020, vol. 5, pp. 166–171.

32. Saeed Rajabi, Mehdi Saman Azari, Stefania Santini, and Francesco Flammini. Expert Systems with Applications, 2022.

33. Teerawat Thepmanee, Sawai Pongswatd, Farzin Asadi, and Prapart Ukakimaparn. Implementation of control and scada system: Energy Reports, 2022, vol. 8, pp. 934–941.

34. Dietterich T.G. Ensemble methods in machine learning. Multiple Classifier Systems, 2015, pp. 1–15.

35. Zhou Z.H. Ensemble Methods: Foundations and Algorithms. Chapman and Hall/CRC, 2012.

36. Kuncheva L.I. Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, 2014.

37. Rokach L. Ensemble-based classifiers. Artificial Intelligence Review, 2010, vol. 33, no. 1–2, pp. 1–39.

38. Kadi Mohamed, Amine Naim, Akkouche Naim, AkkoucheSary, Awad Sary and Awad Show., 2010.

39. Imran Rahman Pandian, Vasant Balbir, Singh Mahinder and Abdullah-Al-Wadud. Alexandria Engineering Journal, 2016.

40. Ponni Ponnusamy and Prabha Dhandayudam. Journal of Electrical Engineering and Technology journal, 2023.

41. Ali Aldrees, Hamad Hassan Awan, Arbab Faisal and Abdeliazim Mustafa Mohamed, Process Safety and Environmental Protection journal, 2022.

42. Sinem Bozkurt and Kemal Keskin, 2022.

43. Sriparna Saha and Asif Ekbal. Combining multiple classifiers using vote based classifier ensemble technique for named entity recognition. Data & Knowledge Engineering journal, 2018.