Интеллектуальное обнаружение и изоляция неисправностей на основе нейронных сетей NARX
Ключевые слова:
обнаружение и изоляция неисправностей, инкубатор, многослойный перцептрон, моделирование NARX, генерация и оценка остаточных величин, рекуррентные нейронные сети, классификаторы машинного обучения, интеллектуальные системыАннотация
Интеллектуальные системы стали неотъемлемым компонентом современных технологических ландшафтов. Вопрос их надежности является крайне важным, поскольку возникновение неисправностей способно катастрофически сказаться на функционировании и итоговой эффективности системы, вследствие чего обнаружение и изоляция неисправностей (FDI) становится критически значимой задачей. Реализация этой задачи осложняется присущей таким системам сложной нелинейной динамикой. В данной работе в рамках решения указанной проблемы предлагается методология интеллектуального обнаружения и изоляции неисправностей; в качестве репрезентативного примера рассматривается система инкубатора. Предлагаемый метод использует нелинейную авторегрессионную экзогенную (NARX) нейронную сеть в параллельной структуре для моделирования сложной нелинейной динамики системы. Были сравнены различные реализации модели NARX, основанные на многослойном перцептроне (MLP), сети долгой краткосрочной памяти (LSTM), вентильном рекуррентном блоке (GRU) и сети Элмана, для оценки их эффективности моделирования и определения лучшей модели. Полученные расхождения между прогнозами модели и фактическими значениями называются остаточными величинами. Для классификации неисправностей было проведено сравнительное исследование пяти методов машинного обучения (ML): многослойного перцептрона, экстремального градиентного бустинга (XGBoost), метода опорных векторов (SVM), метода k-ближайших соседей (KNN) и линейного дискриминантного анализа (LDA). Данные методы анализируют остаточные величины, чтобы идентифицировать конкретную неисправность из заранее определенного множества возможных, включая неисправности исполнительных механизмов и датчиков. Полученные результаты демонстрируют эффективность интеллектуальной методологии FDI, представленной в данной работе. Модели NARX продемонстрировали высокую эффективность моделирования, причем гибридная модель MLP-NARX показала наилучшие результаты, превзойдя остальные архитектуры. Сравнительный анализ классификаторов выявил различия в эффективности пяти рассматриваемых методов, при этом классификатор на основе многослойного перцептрона достиг наивысших показателей по всем оценочным метрикам. Это свидетельствует о его пригодности для практического применения в задачах диагностики неисправностей, что обусловлено его высокой способностью улавливать сложные нелинейные взаимосвязи в данных.
Литература
1. Mujcic E., Drakulic U. Design and Implementation of Fuzzy Control System for Egg Incubator based on IoT Technology. IOP Conference Series: Materials Science and Engineering. 2021. vol. 1208. no. 1. DOI: 10.1088/1757-899X/1208/1/012038.
2. Mitchaothai J., Lertpatarakomol R., Trairatapiwan T., Lukkananukool A. Influence of Incubation Temperature and Relative Humidity on the Egg Hatchability Pattern of Two-Spotted (Gryllus bimaculatus) and House (Acheta domesticus) Crickets. Animals. 2024. vol. 14. no. 15. DOI: 10.3390/ani14152176.
3. Noiva R.M., Menezes A.C., Peleteiro M.C. Influence of Temperature and Humidity Manipulation on Chicken Embryonic Development. BMC Veterinary Research. 2014. vol. 10. pp. 1–10. DOI: 10.1186/s12917-014-0234-3.
4. Yakimov V.L., Maltsev G.N. Hybrid Network Structures and Their Use in Diagnosing Complex Technical Systems. Informatics and Automation. 2022. vol. 21. no. 1. pp. 126–160. DOI: 10.15622/ia.2022.21.5.
5. Isermann R. Model-Based Fault-Detection and Diagnosis – Status and Applications. Annual Reviews in Control. 2005. vol. 29. no. 1. pp. 71–85. DOI: 10.1016/j.arcontrol.2004.12.002.
6. Patton R.J. Fault Detection and Diagnosis in Aerospace Systems Using Analytical Redundancy. Computer Control Engineering Journal. 1991. vol. 2. no. 3. pp. 127–136. DOI: 10.1049/cce:19910031.
7. Bernardi E., Adam E.J. Observer-Based Fault Detection and Diagnosis Strategy for Industrial Processes. Journal of the Franklin Institute. 2020. vol. 357. no. 14. pp. 10054–10081. DOI: 10.1016/j.jfranklin.2020.07.046.
8. Mo J., Qin D., Liu Y. Unknown Input Observer-Based Fault Diagnosis of Speed Sensors in Dual Clutch Transmission. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 2022. vol. 237. no. 7. pp. 1710–1720. DOI: 10.1177/09544070221095286.
9. Khentout N., Salhi H., Magrotti G., Merrouche D. Fault Monitoring and Accommodation of the Heat Exchanger Parameters of Triga-Mark II Nuclear Research Reactor Using Model-Based Analytical Redundancy. Progress in Nuclear Energy. 2018. vol. 109. pp. 97–112. DOI: 10.1016/j.pnucene.2018.02.019.
10. Jihani N., Kabbaj M.N., Benbrahim M. Kalman Filter Based Sensor Fault Detection in Wireless Sensor Network for Smart Irrigation. Results in Engineering. 2023. vol. 20. DOI: 10.1016/j.rineng.2023.101395.
11. Patan K. Artificial Neural Networks for the Modeling and Fault Diagnosis of Technical Processes. Springer, 2008. 206 p.. DOI: 10.1007/978-3-540-79872-9.
12. Patan K., Korbicz J. Artificial Neural Networks in Fault Diagnosis. Berlin, Heidelberg: Springer Berlin Heidelberg. 2004. pp. 333–379. DOI: 10.1007/978-3-642-18615-8_9.
13. Yu D.L., Hamad A., Gomm J.B., Sangha M.S. Dynamic Fault Detection and Isolation for Automotive Engine Air Path by Independent Neural Network Model. International Journal of Engine Research. 2014. vol. 15. no. 1. pp. 87–100. DOI: 10.1177/1468087412461267.
14. Mohd Amiruddin A.A., Zabiri H., Taqvi S.A.A., Tufa L.D. Neural Network Applications in Fault Diagnosis and Detection: An Overview of Implementations in Engineering-Related Systems. Neural Computing and Applications. 2020. vol. 32. no. 2. pp. 447–472. DOI: 10.1007/s00521-018-3911-5.
15. Atmane M.A., Boudebouda O., Zatla H., Nouar S.F., Tolbi B., Djeriri Y., Bouhamama M. Fault Diagnosis: An Integrated Methodology Based on Neural Networks. Proceedings of 3rd International Conference on Advanced Electrical Engineering (ICAEE’2024). IEEE, 2024. pp. 1–6. DOI: 10.1109/ICAEE61760.2024.10783413.
16. Nasser R., Azar A.T., Humaidi A.J., Al-Mhdawi A.K., Ibraheem I.K. Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. Electronics. 2021. vol. 10. no. 23. DOI: 10.3390/electronics10232888.
17. Yang L., Gao L., Luo X., Hao Y., Zhang Z., Jin Y., Zhang J. Improved Method for Fault Diagnosis of Oil-Immersed Transformers Based on Simulation Test Platform. IEEE Transactions on Dielectrics and Electrical Insulation. 2025. vol. 32. no. 1. pp. 571–580. DOI: 10.1109/TDEI.2024.3418388.
18. Venkata P., Pandya V., Vala K., Sant A.V. Support Vector Machine for Fast Fault Detection and Classification in Modern Power Systems Using Quarter Cycle Data. Energy Reports. 2022. vol. 8. pp. 92–98. DOI: 10.1016/j.egyr.2022.10.279.
19. Tuerxun W., Chang X., Hongyu G., Zhijie J., Huajian Z. Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm. IEEE Access. 2021. vol. 9. pp. 69307–69315. DOI: 10.1109/ACCESS.2021.3075547.
20. Cho S., Choi M., Gao Z., Moan T. Fault Detection and Diagnosis of a Blade Pitch System in a Floating Wind Turbine Based on Kalman Filters and Artificial Neural Networks. Renewable Energy. 2021. vol. 169. pp. 1–13. DOI: 10.1016/j.renene.2020.12.116.
21. Wang Y., Wen C., Wu X. Fault Detection and Isolation of Floating Wind Turbine Pitch System Based on Kalman Filter and Multi-Attention 1DCNN. Systems Science & Control Engineering. 2024. vol. 12. no. 1. DOI: 10.1080/21642583.2024.2362169.
22. Yu D.L., Gomm J.B., Williams D. Sensor Fault Diagnosis in a Chemical Process via RBF Neural Networks. Control Engineering Practice. 1999. vol. 7. no. 1. pp. 49–55. DOI: 10.1016/S0967-0661(98)00167-1.
23. Liu X., He H. Fault Diagnosis for TE Process Using RBF Neural Network. IEEE Access. 2021. vol. 9. pp. 118453–118460. DOI: 10.1109/ACCESS.2021.3107360.
24. Rahimilarki R., Gao Z., Jin N., Zhang A. Convolutional Neural Network Fault Classification Based on Time-Series Analysis for Benchmark Wind Turbine Machine. Renewable Energy. 2022. vol. 185. pp. 916–931. DOI: 10.1016/j.renene.2021.12.056.
25. Choudhary A., Mian T., Fatima S. Convolutional Neural Network Based Bearing Fault Diagnosis of Rotating Machine Using Thermal Images. Measurement. 2021. vol. 176. DOI: 10.1016/j.measurement.2021.109196.
26. Zhang Y., Zhou T., Huang X., Cao L., Zhou Q. Fault Diagnosis of Rotating Machinery Based on Recurrent Neural Networks. Measurement. 2021. vol. 171. DOI: 10.1016/j.measurement.2020.108774.
27. Zhu J., Jiang Q., Shen Y., Qian C., Xu F., Zhu Q. Application of Recurrent Neural Network to Mechanical Fault Diagnosis: A Review. Journal of Mechanical Science and Technology. 2022. vol. 36. no. 2. pp. 527–542. DOI: 10.1007/s12206-022-0102-1.
28. An Y., Sun X., Ren B., Li H., Zhang M. A Data-driven Method for IGBT Open-circuit Fault Diagnosis for the Modular Multilevel Converter based on a modified Elman Neural Network. Energy Reports. 2022. vol. 8. pp. 80–88. DOI: 10.1016/j.egyr.2022.08.024.
29. Nguyen V.-Q., Vu M.-H., Pham V.-T., Tran T.-T. A Deep Learning Approach Based on MLP-Mixer Models for Bearing Fault Diagnosis. Proceedings of International Conference on System Science and Engineering (ICSSE’2023). IEEE, 2023. pp. 16–21. DOI: 10.1109/ICSSE58758.2023.10227218.
30. Khoualdia T., Lakehal A., Chelli Z., Khoualdia K., Nessaib K. Optimized Multi Layer Perceptron Artificial Neural Network Based Fault Diagnosis of Induction Motor Using Vibration Signals. Diagnostyka. 2021. vol. 22. no. 1. pp. 65–74. DOI: 10.29354/diag/133091.
31. Naimi A., Deng J., Shimjith S.R., Arul A.J. Fault Detection and Isolation of a Pressurized Water Reactor based on Neural Network and K-Nearest Neighbor. IEEE Access. 2022. vol. 10. pp. 17113–17121. DOI: 10.1109/ACCESS.2022.3149772.
32. Ma Y., Liu H., Zhu Y., Wang F., Luo Z. The NARX Model-Based System Identification on Nonlinear, Rotor-Bearing Systems. Applied Sciences. 2017. vol. 7. no. 9. pp. 911. DOI: 10.3390/app7090911.
33. Choe H.O., Lee M.H. Artificial Intelligence-based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment. Agriculture. 2023. vol. 13. no. 11. DOI: 10.3390/agriculture13112124.
34. Taqvi S.A., Tufa L.D., Zabiri H., Maulud A.S., Uddin F. Fault Detection in Distillation Column Using NARX Neural Network. Neural Computing and Applications. 2020. vol. 32. no. 8. pp. 3503–3519. DOI: 10.1007/s00521-018-3658-z.
35. Mustafaraj G., Lowry G., Chen J. Prediction of Room Temperature and Relative Humidity by Autoregressive Linear and Nonlinear Neural Network Models for an Open Office. Energy and Buildings. 2011. vol. 43. no. 6. pp. 1452–1460. DOI: 10.1016/j.enbuild.2011.02.007.
36. Sani M.G., Abdul Wahab N., Sam Y.M., Samsudin S.I., Jamaludin I.W. Comparison of NARX Neural Network and Classical Modelling Approaches. Applied mechanics and Materials. 2014. vol. 554. pp. 360–365. DOI: 10.4028/www.scientific.net/AMM.554.360.
37. Lin Y.P. ARX and NARX Modeling and Control of a Continuous UV/H2O2 Photoreactor for the Aqueous PVA Degradation. Master's Thesis. Toronto Metropolitan University, 2024. 171 p. DOI: 10.32920/25761546.
38. Ling T.G., Rahmat M.F., Husain A.R. A Comparative Study of Linear ARX and Nonlinear ANFIS Modeling of an Electro-hydraulic Actuator System. Jurnal Teknologi (Sciences & Engineering). 2014. vol. 67. no. 5. pp. 1–8. DOI: 10.11113/jt.v67.2833.
39. Renteria-Mena J.B., Plaza D., Giraldo E. Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting. Engineering Proceedings. 2023. vol. 39. no. 1. DOI: 10.3390/engproc2023039060.
40. Delcroix B., Ny J.L., Bernier M., Azam M., Qu B., Venne J.S. Autoregressive Neural Networks with Exogenous Variables for Indoor Temperature Prediction in Buildings. Building simulation. 2021. vol. 14. no. 1. pp. 165–178. DOI: 10.1007/s12273-019-0597-2.
41. Ramirez C., Acuna G. Forecasting Cash Demand in ATM using Neural Networks and Least Square Support Vector Machine. Proceedings of 16th Iberoamerican Congress on Pattern Recognition (CIARP). Springer Berlin Heidelberg, 2011. pp. 515–522. DOI: 10.1007/978-3-642-25085-9_61.
42. Shao Y., Zhao L. NARX-GA-Elman Method for Mach Number Prediction of Wind Tunnel Flow Field. Instrumentation. 2023. vol. 10. no. 4. DOI: 10.15878/j.cnki.instrumentation.2023.04.004.
43. Nikentari N., Wei H.L. Tide Level Prediction Using NARX-based Recurrent Neural Networks. Proceeding of 27th International Conference on Automation and Computing (ICAC). IEEE, 2022. pp. 1–6. DOI: 10.1109/ICAC55051.2022.9911163.
44. Freitas V.C.G.D., Araujo V.G.D., Crisostomo D.C.D.C., Lima G.F.D., Neto A.D.D., Salazar A.O. Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning. Sensors. 2022. vol. 22. no. 23. DOI: 10.3390/s22239162.
45. Ghate V.N., Dudul S.V. Optimal MLP Neural Network Classifier for Fault Detection of Three Phase Induction Motor. Expert Systems with Applications. 2010. vol. 37. no. 4. pp. 3468–3481. DOI: 10.1016/j.eswa.2009.10.041.
46. Teler K., Skowron M., Orłowska-Kowalska T. Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive. IEEE Transactions on Industrial Informatics. 2024. vol. 20. no. 4. pp. 5702–5713. DOI: 10.1109/TII.2023.3336348.
47. Lin W., Yang C., Lin J., Tsay M.A. Fault Classification Method by RBF Neural Network with OLS Learning Procedure. IEEE Power Engineering Review. 2001. vol. 21. no. 8. DOI: 10.1109/MPER.2001.4311561.
48. Yang P., Wang T., Yang H., Meng C., Zhang H., Cheng L. The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network. Electronics. 2023. vol. 12. no. 4. DOI: 10.3390/electronics12041066.
49. Jeong K., Choi S.B., Choi H. Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems. IEEE Transactions on Vehicular Technology. 2020. vol. 69. no. 4. pp. 3852–3863. DOI: 10.1109/TVT.2020.2977353.
50. Ramdani O., Beddek K., Haddouche R., Zerrougui M., Chouider N. SVM-Based Approach Fault Detection for PMSG-Wind Energy Conversion System. Journal of Engineering Research. 2025. vol. 13. no. 4. pp. 3378–3393. DOI: 10.1016/j.jer.2025.01.001.
51. Zhou N., Shao Q., Zhou J., Changjie H. Fault Classification of Proton Exchange Membrane Fuel Cells for Vehicles based on XGBoost. Proceedings of 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2021. pp. 1054–1058. DOI: 10.1109/ICBAIE52039.2021.9389943.
52. Hasan S., Toufikuzzaman M. Fault Occurrence Detection and Classification of Fault Type in Electrical Power Transmission Line with Machine Learning Algorithms. International Journal on Electrical Engineering and Informatics. 2022. vol. 14. no. 3. DOI: 10.15676/ijeei.2022.14.3.9.
53. Mehta A., Goyal D., Choudhary A., Pabla B.S., Belghith S. Machine Learning‐Based Fault Diagnosis of Self‐Aligning Bearings for Rotating Machinery Using Infrared Thermography. Mathematical Problems in Engineering. 2021. vol. 2021. no. 1. DOI: 10.1155/2021/9947300.
54. Shaikh F.A., Kamboh M.Z., Alvi B.A., Khan S., Khan F.M. Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier. Sir Syed University Research Journal of Engineering and Technology. 2022. vol. 12. no.2. pp. 95–100. DOI: 10.33317/ssurj.513.
55. Altayef E., Anayi F., Packianather M., Benmahamed Y., Kherif O. Detection and Classification of Lamination Faults in A 15 kVA Three-Phase Transformer Core using SVM, KNN and DT Algorithms. IEEE Access. 2022. vol. 10. pp. 50925–50932. DOI: 10.1109/ACCESS.2022.3174359.
56. Shinde P.V., Desavale R.G., Jadhav P.M., Sawant S.H. A Multi Fault Classification in a Rotor-Bearing System using Machine Learning Approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2023. vol. 45. no. 2. DOI: 10.1007/s40430-023-04015-1.
57. Rakhmawati R., Murdianto F.D., Luthfi A., Rahman A.Y. Thermal Optimization on Incubator using Fuzzy Inference System based IoT. Proceedings of International Conference of Artificial Intelligence and Information Technology (ICAIIT). IEEE, 2019. pp. 464–468. DOI: 10.1109/ICAIIT.2019.8834530.
58. Febriani A., Wahyuni R., Mardeni M., Irawan Y., Melyanti R. Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator. Journal of Applied Data Sciences. 2024. vol. 5. no. 3. pp. 1052–1068. DOI: 10.47738/jads.v5i3.304.
59. Easwaran A., Arvindan P., Dhanyasree E., Surya R., Selvakumar S. Internet of Things Enabled Smart Animal Farm Prototype. Journal of Physics: Conference Series. 2021. vol. 2070. no. 1. DOI: 10.1088/1742-6596/2070/1/012115.
60. Guzman-Zabala J.V., Castro-Martin A.P. Smart and Semi-industrial Egg Incubator with Remote Monitoring Using LoRa Technology. Proceedings of International Conference on Computer Science, Electronics and Industrial Engineering (CSEI). Springer Nature Switzerland, 2024. pp. 522–540. DOI: 10.1007/978-3-031-70981-4_35.
61. Roshanghiyasi H., Ahmad A.H., Hosseinpour S., Jafari A., Mousazadeh H., Asadollahzadeh A. Monitoring and Controlling the Chicken Incubation Process Using the Internet of Things System. Journal of Agricultural Mechanization. 2022. vol. 7. no. 1. pp. 73–77. DOI: 10.22034/jam.2022.15710.
62. Radhakrishnan K., Jose N., Sanjay S.G., Cherian T., Vishnu K.R. Design and Implementation of a Fully Automated Egg Incubator. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2014. vol. 3. no. 2. pp. 7686–7690.
63. Uzoigwe L.O., Ekezie J.C. Egg Incubator Control System with Short Message Service (sms) Fault Analysis Alert. Journal of Agriculture and Food Sciences. 2013. vol. 11. no. 2. pp. 45–68. DOI: 10.4314/jafs.v11i2.5.
64. Stull R. Practical Meteorology: An Algebra-based Survey of Atmospheric Science. University of British Columbia, 2017. 940 p.
65. Rumelhart D.E., Hinton G.E., Williams R.J. Learning Representations by Back-Propagating Errors. Nature. 1986. vol. 323. no. 6088. pp. 533–536. DOI: 10.1038/323533a0.
66. Elman J.L. Finding Structure in Time. Cognitive science. 1990. vol. 14. no. 2. pp. 179–211. DOI: 10.1207/s15516709cog1402_1.
67. Hochreiter S., Schmidhuber J. Long Short-Term Memory. Neural computation. 1997. vol. 9. no. 8. pp. 1735–1780. DOI: 10.1162/neco.1997.9.8.1735.
68. Cho K., Van Merrienboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2014. pp. 1724–1734. DOI: 10.3115/v1/D14-1179.
69. MacKay D.J.C. Bayesian Interpolation. Neural Computation. 1992. vol. 4. no. 3. pp. 415–447. DOI: 10.1162/neco.1992.4.3.415.
70. Londhe A.S., Ingale A.S., Khadse C.B. Bayesian Regularization Neural Network-based Fault Detection System in HVDC Transmission System. Proceedings of International Conference on Smart Technologies for Energy, Environment, and Sustainable Development (ICSTEESD). Springer Nature, 2022. pp. 601–607. DOI: 10.1007/978-981-16-6875-3_48.
71. Hassan M.S., Kamal K., Ratlamwala T.A.H. Fault Classification of Power Plants using Artificial Neural Network. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2022. vol. 44. no. 3. pp. 7665–7680. DOI: 10.1080/15567036.2022.2113936.
72. Liu D.C., Nocedal J. On the Limited Memory BFGS Method for Large Scale Optimization. Mathematical Programming. 1989. vol. 45. no. 1. pp. 503–528. DOI: 10.1007/BF01589116
73. Ljung L. System Identification: Theory for the User. Prentice Hall, 1999. pp. 640.
2. Mitchaothai J., Lertpatarakomol R., Trairatapiwan T., Lukkananukool A. Influence of Incubation Temperature and Relative Humidity on the Egg Hatchability Pattern of Two-Spotted (Gryllus bimaculatus) and House (Acheta domesticus) Crickets. Animals. 2024. vol. 14. no. 15. DOI: 10.3390/ani14152176.
3. Noiva R.M., Menezes A.C., Peleteiro M.C. Influence of Temperature and Humidity Manipulation on Chicken Embryonic Development. BMC Veterinary Research. 2014. vol. 10. pp. 1–10. DOI: 10.1186/s12917-014-0234-3.
4. Yakimov V.L., Maltsev G.N. Hybrid Network Structures and Their Use in Diagnosing Complex Technical Systems. Informatics and Automation. 2022. vol. 21. no. 1. pp. 126–160. DOI: 10.15622/ia.2022.21.5.
5. Isermann R. Model-Based Fault-Detection and Diagnosis – Status and Applications. Annual Reviews in Control. 2005. vol. 29. no. 1. pp. 71–85. DOI: 10.1016/j.arcontrol.2004.12.002.
6. Patton R.J. Fault Detection and Diagnosis in Aerospace Systems Using Analytical Redundancy. Computer Control Engineering Journal. 1991. vol. 2. no. 3. pp. 127–136. DOI: 10.1049/cce:19910031.
7. Bernardi E., Adam E.J. Observer-Based Fault Detection and Diagnosis Strategy for Industrial Processes. Journal of the Franklin Institute. 2020. vol. 357. no. 14. pp. 10054–10081. DOI: 10.1016/j.jfranklin.2020.07.046.
8. Mo J., Qin D., Liu Y. Unknown Input Observer-Based Fault Diagnosis of Speed Sensors in Dual Clutch Transmission. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 2022. vol. 237. no. 7. pp. 1710–1720. DOI: 10.1177/09544070221095286.
9. Khentout N., Salhi H., Magrotti G., Merrouche D. Fault Monitoring and Accommodation of the Heat Exchanger Parameters of Triga-Mark II Nuclear Research Reactor Using Model-Based Analytical Redundancy. Progress in Nuclear Energy. 2018. vol. 109. pp. 97–112. DOI: 10.1016/j.pnucene.2018.02.019.
10. Jihani N., Kabbaj M.N., Benbrahim M. Kalman Filter Based Sensor Fault Detection in Wireless Sensor Network for Smart Irrigation. Results in Engineering. 2023. vol. 20. DOI: 10.1016/j.rineng.2023.101395.
11. Patan K. Artificial Neural Networks for the Modeling and Fault Diagnosis of Technical Processes. Springer, 2008. 206 p.. DOI: 10.1007/978-3-540-79872-9.
12. Patan K., Korbicz J. Artificial Neural Networks in Fault Diagnosis. Berlin, Heidelberg: Springer Berlin Heidelberg. 2004. pp. 333–379. DOI: 10.1007/978-3-642-18615-8_9.
13. Yu D.L., Hamad A., Gomm J.B., Sangha M.S. Dynamic Fault Detection and Isolation for Automotive Engine Air Path by Independent Neural Network Model. International Journal of Engine Research. 2014. vol. 15. no. 1. pp. 87–100. DOI: 10.1177/1468087412461267.
14. Mohd Amiruddin A.A., Zabiri H., Taqvi S.A.A., Tufa L.D. Neural Network Applications in Fault Diagnosis and Detection: An Overview of Implementations in Engineering-Related Systems. Neural Computing and Applications. 2020. vol. 32. no. 2. pp. 447–472. DOI: 10.1007/s00521-018-3911-5.
15. Atmane M.A., Boudebouda O., Zatla H., Nouar S.F., Tolbi B., Djeriri Y., Bouhamama M. Fault Diagnosis: An Integrated Methodology Based on Neural Networks. Proceedings of 3rd International Conference on Advanced Electrical Engineering (ICAEE’2024). IEEE, 2024. pp. 1–6. DOI: 10.1109/ICAEE61760.2024.10783413.
16. Nasser R., Azar A.T., Humaidi A.J., Al-Mhdawi A.K., Ibraheem I.K. Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier. Electronics. 2021. vol. 10. no. 23. DOI: 10.3390/electronics10232888.
17. Yang L., Gao L., Luo X., Hao Y., Zhang Z., Jin Y., Zhang J. Improved Method for Fault Diagnosis of Oil-Immersed Transformers Based on Simulation Test Platform. IEEE Transactions on Dielectrics and Electrical Insulation. 2025. vol. 32. no. 1. pp. 571–580. DOI: 10.1109/TDEI.2024.3418388.
18. Venkata P., Pandya V., Vala K., Sant A.V. Support Vector Machine for Fast Fault Detection and Classification in Modern Power Systems Using Quarter Cycle Data. Energy Reports. 2022. vol. 8. pp. 92–98. DOI: 10.1016/j.egyr.2022.10.279.
19. Tuerxun W., Chang X., Hongyu G., Zhijie J., Huajian Z. Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm. IEEE Access. 2021. vol. 9. pp. 69307–69315. DOI: 10.1109/ACCESS.2021.3075547.
20. Cho S., Choi M., Gao Z., Moan T. Fault Detection and Diagnosis of a Blade Pitch System in a Floating Wind Turbine Based on Kalman Filters and Artificial Neural Networks. Renewable Energy. 2021. vol. 169. pp. 1–13. DOI: 10.1016/j.renene.2020.12.116.
21. Wang Y., Wen C., Wu X. Fault Detection and Isolation of Floating Wind Turbine Pitch System Based on Kalman Filter and Multi-Attention 1DCNN. Systems Science & Control Engineering. 2024. vol. 12. no. 1. DOI: 10.1080/21642583.2024.2362169.
22. Yu D.L., Gomm J.B., Williams D. Sensor Fault Diagnosis in a Chemical Process via RBF Neural Networks. Control Engineering Practice. 1999. vol. 7. no. 1. pp. 49–55. DOI: 10.1016/S0967-0661(98)00167-1.
23. Liu X., He H. Fault Diagnosis for TE Process Using RBF Neural Network. IEEE Access. 2021. vol. 9. pp. 118453–118460. DOI: 10.1109/ACCESS.2021.3107360.
24. Rahimilarki R., Gao Z., Jin N., Zhang A. Convolutional Neural Network Fault Classification Based on Time-Series Analysis for Benchmark Wind Turbine Machine. Renewable Energy. 2022. vol. 185. pp. 916–931. DOI: 10.1016/j.renene.2021.12.056.
25. Choudhary A., Mian T., Fatima S. Convolutional Neural Network Based Bearing Fault Diagnosis of Rotating Machine Using Thermal Images. Measurement. 2021. vol. 176. DOI: 10.1016/j.measurement.2021.109196.
26. Zhang Y., Zhou T., Huang X., Cao L., Zhou Q. Fault Diagnosis of Rotating Machinery Based on Recurrent Neural Networks. Measurement. 2021. vol. 171. DOI: 10.1016/j.measurement.2020.108774.
27. Zhu J., Jiang Q., Shen Y., Qian C., Xu F., Zhu Q. Application of Recurrent Neural Network to Mechanical Fault Diagnosis: A Review. Journal of Mechanical Science and Technology. 2022. vol. 36. no. 2. pp. 527–542. DOI: 10.1007/s12206-022-0102-1.
28. An Y., Sun X., Ren B., Li H., Zhang M. A Data-driven Method for IGBT Open-circuit Fault Diagnosis for the Modular Multilevel Converter based on a modified Elman Neural Network. Energy Reports. 2022. vol. 8. pp. 80–88. DOI: 10.1016/j.egyr.2022.08.024.
29. Nguyen V.-Q., Vu M.-H., Pham V.-T., Tran T.-T. A Deep Learning Approach Based on MLP-Mixer Models for Bearing Fault Diagnosis. Proceedings of International Conference on System Science and Engineering (ICSSE’2023). IEEE, 2023. pp. 16–21. DOI: 10.1109/ICSSE58758.2023.10227218.
30. Khoualdia T., Lakehal A., Chelli Z., Khoualdia K., Nessaib K. Optimized Multi Layer Perceptron Artificial Neural Network Based Fault Diagnosis of Induction Motor Using Vibration Signals. Diagnostyka. 2021. vol. 22. no. 1. pp. 65–74. DOI: 10.29354/diag/133091.
31. Naimi A., Deng J., Shimjith S.R., Arul A.J. Fault Detection and Isolation of a Pressurized Water Reactor based on Neural Network and K-Nearest Neighbor. IEEE Access. 2022. vol. 10. pp. 17113–17121. DOI: 10.1109/ACCESS.2022.3149772.
32. Ma Y., Liu H., Zhu Y., Wang F., Luo Z. The NARX Model-Based System Identification on Nonlinear, Rotor-Bearing Systems. Applied Sciences. 2017. vol. 7. no. 9. pp. 911. DOI: 10.3390/app7090911.
33. Choe H.O., Lee M.H. Artificial Intelligence-based Fault Diagnosis and Prediction for Smart Farm Information and Communication Technology Equipment. Agriculture. 2023. vol. 13. no. 11. DOI: 10.3390/agriculture13112124.
34. Taqvi S.A., Tufa L.D., Zabiri H., Maulud A.S., Uddin F. Fault Detection in Distillation Column Using NARX Neural Network. Neural Computing and Applications. 2020. vol. 32. no. 8. pp. 3503–3519. DOI: 10.1007/s00521-018-3658-z.
35. Mustafaraj G., Lowry G., Chen J. Prediction of Room Temperature and Relative Humidity by Autoregressive Linear and Nonlinear Neural Network Models for an Open Office. Energy and Buildings. 2011. vol. 43. no. 6. pp. 1452–1460. DOI: 10.1016/j.enbuild.2011.02.007.
36. Sani M.G., Abdul Wahab N., Sam Y.M., Samsudin S.I., Jamaludin I.W. Comparison of NARX Neural Network and Classical Modelling Approaches. Applied mechanics and Materials. 2014. vol. 554. pp. 360–365. DOI: 10.4028/www.scientific.net/AMM.554.360.
37. Lin Y.P. ARX and NARX Modeling and Control of a Continuous UV/H2O2 Photoreactor for the Aqueous PVA Degradation. Master's Thesis. Toronto Metropolitan University, 2024. 171 p. DOI: 10.32920/25761546.
38. Ling T.G., Rahmat M.F., Husain A.R. A Comparative Study of Linear ARX and Nonlinear ANFIS Modeling of an Electro-hydraulic Actuator System. Jurnal Teknologi (Sciences & Engineering). 2014. vol. 67. no. 5. pp. 1–8. DOI: 10.11113/jt.v67.2833.
39. Renteria-Mena J.B., Plaza D., Giraldo E. Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting. Engineering Proceedings. 2023. vol. 39. no. 1. DOI: 10.3390/engproc2023039060.
40. Delcroix B., Ny J.L., Bernier M., Azam M., Qu B., Venne J.S. Autoregressive Neural Networks with Exogenous Variables for Indoor Temperature Prediction in Buildings. Building simulation. 2021. vol. 14. no. 1. pp. 165–178. DOI: 10.1007/s12273-019-0597-2.
41. Ramirez C., Acuna G. Forecasting Cash Demand in ATM using Neural Networks and Least Square Support Vector Machine. Proceedings of 16th Iberoamerican Congress on Pattern Recognition (CIARP). Springer Berlin Heidelberg, 2011. pp. 515–522. DOI: 10.1007/978-3-642-25085-9_61.
42. Shao Y., Zhao L. NARX-GA-Elman Method for Mach Number Prediction of Wind Tunnel Flow Field. Instrumentation. 2023. vol. 10. no. 4. DOI: 10.15878/j.cnki.instrumentation.2023.04.004.
43. Nikentari N., Wei H.L. Tide Level Prediction Using NARX-based Recurrent Neural Networks. Proceeding of 27th International Conference on Automation and Computing (ICAC). IEEE, 2022. pp. 1–6. DOI: 10.1109/ICAC55051.2022.9911163.
44. Freitas V.C.G.D., Araujo V.G.D., Crisostomo D.C.D.C., Lima G.F.D., Neto A.D.D., Salazar A.O. Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning. Sensors. 2022. vol. 22. no. 23. DOI: 10.3390/s22239162.
45. Ghate V.N., Dudul S.V. Optimal MLP Neural Network Classifier for Fault Detection of Three Phase Induction Motor. Expert Systems with Applications. 2010. vol. 37. no. 4. pp. 3468–3481. DOI: 10.1016/j.eswa.2009.10.041.
46. Teler K., Skowron M., Orłowska-Kowalska T. Implementation of MLP-Based Classifier of Current Sensor Faults in Vector-Controlled Induction Motor Drive. IEEE Transactions on Industrial Informatics. 2024. vol. 20. no. 4. pp. 5702–5713. DOI: 10.1109/TII.2023.3336348.
47. Lin W., Yang C., Lin J., Tsay M.A. Fault Classification Method by RBF Neural Network with OLS Learning Procedure. IEEE Power Engineering Review. 2001. vol. 21. no. 8. DOI: 10.1109/MPER.2001.4311561.
48. Yang P., Wang T., Yang H., Meng C., Zhang H., Cheng L. The Performance of Electronic Current Transformer Fault Diagnosis Model: Using an Improved Whale Optimization Algorithm and RBF Neural Network. Electronics. 2023. vol. 12. no. 4. DOI: 10.3390/electronics12041066.
49. Jeong K., Choi S.B., Choi H. Sensor Fault Detection and Isolation Using a Support Vector Machine for Vehicle Suspension Systems. IEEE Transactions on Vehicular Technology. 2020. vol. 69. no. 4. pp. 3852–3863. DOI: 10.1109/TVT.2020.2977353.
50. Ramdani O., Beddek K., Haddouche R., Zerrougui M., Chouider N. SVM-Based Approach Fault Detection for PMSG-Wind Energy Conversion System. Journal of Engineering Research. 2025. vol. 13. no. 4. pp. 3378–3393. DOI: 10.1016/j.jer.2025.01.001.
51. Zhou N., Shao Q., Zhou J., Changjie H. Fault Classification of Proton Exchange Membrane Fuel Cells for Vehicles based on XGBoost. Proceedings of 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). IEEE, 2021. pp. 1054–1058. DOI: 10.1109/ICBAIE52039.2021.9389943.
52. Hasan S., Toufikuzzaman M. Fault Occurrence Detection and Classification of Fault Type in Electrical Power Transmission Line with Machine Learning Algorithms. International Journal on Electrical Engineering and Informatics. 2022. vol. 14. no. 3. DOI: 10.15676/ijeei.2022.14.3.9.
53. Mehta A., Goyal D., Choudhary A., Pabla B.S., Belghith S. Machine Learning‐Based Fault Diagnosis of Self‐Aligning Bearings for Rotating Machinery Using Infrared Thermography. Mathematical Problems in Engineering. 2021. vol. 2021. no. 1. DOI: 10.1155/2021/9947300.
54. Shaikh F.A., Kamboh M.Z., Alvi B.A., Khan S., Khan F.M. Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier. Sir Syed University Research Journal of Engineering and Technology. 2022. vol. 12. no.2. pp. 95–100. DOI: 10.33317/ssurj.513.
55. Altayef E., Anayi F., Packianather M., Benmahamed Y., Kherif O. Detection and Classification of Lamination Faults in A 15 kVA Three-Phase Transformer Core using SVM, KNN and DT Algorithms. IEEE Access. 2022. vol. 10. pp. 50925–50932. DOI: 10.1109/ACCESS.2022.3174359.
56. Shinde P.V., Desavale R.G., Jadhav P.M., Sawant S.H. A Multi Fault Classification in a Rotor-Bearing System using Machine Learning Approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering. 2023. vol. 45. no. 2. DOI: 10.1007/s40430-023-04015-1.
57. Rakhmawati R., Murdianto F.D., Luthfi A., Rahman A.Y. Thermal Optimization on Incubator using Fuzzy Inference System based IoT. Proceedings of International Conference of Artificial Intelligence and Information Technology (ICAIIT). IEEE, 2019. pp. 464–468. DOI: 10.1109/ICAIIT.2019.8834530.
58. Febriani A., Wahyuni R., Mardeni M., Irawan Y., Melyanti R. Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator. Journal of Applied Data Sciences. 2024. vol. 5. no. 3. pp. 1052–1068. DOI: 10.47738/jads.v5i3.304.
59. Easwaran A., Arvindan P., Dhanyasree E., Surya R., Selvakumar S. Internet of Things Enabled Smart Animal Farm Prototype. Journal of Physics: Conference Series. 2021. vol. 2070. no. 1. DOI: 10.1088/1742-6596/2070/1/012115.
60. Guzman-Zabala J.V., Castro-Martin A.P. Smart and Semi-industrial Egg Incubator with Remote Monitoring Using LoRa Technology. Proceedings of International Conference on Computer Science, Electronics and Industrial Engineering (CSEI). Springer Nature Switzerland, 2024. pp. 522–540. DOI: 10.1007/978-3-031-70981-4_35.
61. Roshanghiyasi H., Ahmad A.H., Hosseinpour S., Jafari A., Mousazadeh H., Asadollahzadeh A. Monitoring and Controlling the Chicken Incubation Process Using the Internet of Things System. Journal of Agricultural Mechanization. 2022. vol. 7. no. 1. pp. 73–77. DOI: 10.22034/jam.2022.15710.
62. Radhakrishnan K., Jose N., Sanjay S.G., Cherian T., Vishnu K.R. Design and Implementation of a Fully Automated Egg Incubator. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2014. vol. 3. no. 2. pp. 7686–7690.
63. Uzoigwe L.O., Ekezie J.C. Egg Incubator Control System with Short Message Service (sms) Fault Analysis Alert. Journal of Agriculture and Food Sciences. 2013. vol. 11. no. 2. pp. 45–68. DOI: 10.4314/jafs.v11i2.5.
64. Stull R. Practical Meteorology: An Algebra-based Survey of Atmospheric Science. University of British Columbia, 2017. 940 p.
65. Rumelhart D.E., Hinton G.E., Williams R.J. Learning Representations by Back-Propagating Errors. Nature. 1986. vol. 323. no. 6088. pp. 533–536. DOI: 10.1038/323533a0.
66. Elman J.L. Finding Structure in Time. Cognitive science. 1990. vol. 14. no. 2. pp. 179–211. DOI: 10.1207/s15516709cog1402_1.
67. Hochreiter S., Schmidhuber J. Long Short-Term Memory. Neural computation. 1997. vol. 9. no. 8. pp. 1735–1780. DOI: 10.1162/neco.1997.9.8.1735.
68. Cho K., Van Merrienboer B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2014. pp. 1724–1734. DOI: 10.3115/v1/D14-1179.
69. MacKay D.J.C. Bayesian Interpolation. Neural Computation. 1992. vol. 4. no. 3. pp. 415–447. DOI: 10.1162/neco.1992.4.3.415.
70. Londhe A.S., Ingale A.S., Khadse C.B. Bayesian Regularization Neural Network-based Fault Detection System in HVDC Transmission System. Proceedings of International Conference on Smart Technologies for Energy, Environment, and Sustainable Development (ICSTEESD). Springer Nature, 2022. pp. 601–607. DOI: 10.1007/978-981-16-6875-3_48.
71. Hassan M.S., Kamal K., Ratlamwala T.A.H. Fault Classification of Power Plants using Artificial Neural Network. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2022. vol. 44. no. 3. pp. 7665–7680. DOI: 10.1080/15567036.2022.2113936.
72. Liu D.C., Nocedal J. On the Limited Memory BFGS Method for Large Scale Optimization. Mathematical Programming. 1989. vol. 45. no. 1. pp. 503–528. DOI: 10.1007/BF01589116
73. Ljung L. System Identification: Theory for the User. Prentice Hall, 1999. pp. 640.
Опубликован
2026-04-06
Как цитировать
Атмане, М. А., Затла, Х., Толби, Б., Нуар, С. Ф., & Бухамама, М. (2026). Интеллектуальное обнаружение и изоляция неисправностей на основе нейронных сетей NARX. Информатика и автоматизация, 25(2), 410-444. https://doi.org/10.15622/ia.25.2.6
Раздел
Искусственный интеллект, инженерия данных и знаний
Copyright (c) Мохамед Амин Атмане, Unknown, Била́л Толби, Суа́д Фадила Нуар, Мохамед Бухамама
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Авторы, которые публикуются в данном журнале, соглашаются со следующими условиями: Авторы сохраняют за собой авторские права на работу и передают журналу право первой публикации вместе с работой, одновременно лицензируя ее на условиях Creative Commons Attribution License, которая позволяет другим распространять данную работу с обязательным указанием авторства данной работы и ссылкой на оригинальную публикацию в этом журнале. Авторы сохраняют право заключать отдельные, дополнительные контрактные соглашения на неэксклюзивное распространение версии работы, опубликованной этим журналом (например, разместить ее в университетском хранилище или опубликовать ее в книге), со ссылкой на оригинальную публикацию в этом журнале. Авторам разрешается размещать их работу в сети Интернет (например, в университетском хранилище или на их персональном веб-сайте) до и во время процесса рассмотрения ее данным журналом, так как это может привести к продуктивному обсуждению, а также к большему количеству ссылок на данную опубликованную работу (Смотри The Effect of Open Access).