Определение технического состояния и прогнозирование остаточного ресурса электропривода в предсказательном обслуживании: обзор зарубежных источников
DOI:
https://doi.org/10.22213/2410-9304-2025-1-82-93Ключевые слова:
методы классификации и регрессии, глубокое обучение, электропривод, диагностика, определение технического состояния, предсказательное обслуживаниеАннотация
Предсказательное обслуживание электропривода в составе технологического оборудования позволяет снизить вероятность внепланового простоя производства и минимизировать затраты на его ремонт путем непрерывного мониторинга состояния и прогнозирования остаточного ресурса электропривода. В статье на основе анализа зарубежных источников и стандартов ИСО представлены аспекты систем предсказательного обслуживания электропривода: рассмотрены этапы их построения и методы, применяющиеся на каждом этапе. Сделан акцент на наиболее трудоемкие и сочетающие в себе различные методы этапы - диагностику состояния электропривода и прогнозирование его остаточного ресурса. Выделены методы определения технического состояния электропривода, рассмотрены их преимущества и недостатки, а также типы неисправностей, которые возможно диагностировать с помощью каждого из методов. Показано, что наибольшую эффективность и широкое применение демонстрируют методы, основанные на анализе вибрационных и электрических сигналов. Они позволяют выявлять широкий спектр неисправностей и применимы для оценки технического состояния практически всех типов электродвигателей. Проведен сравнительный анализ методов машинного обучения, применяемых для прогнозирования неисправностей и остаточного ресурса электропривода. Отмечены такие методы машинного обучения, как метод случайного леса, нейронные сети долгой краткосрочной памяти, метод опорных векторов, метод k-ближайших соседей. Анализ показал, что выбор алгоритма зависит от множества факторов и не может основываться на универсальных подходах.Библиографические ссылки
Singh S., Gill R., Kumar R. et al. A Comparative Analysis of Machine Learning Algorithms for Predictive Maintenance in Electrical Systems // 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM). 2024. Pp. 1-6.DOI: 10.1109/ICIPTM59628.2024.10563826.
Limprasert N., Thimtheang T., Wattanakul K. et al. Predictive Maintenance Using Machine Learning // 2023 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C). 2023. pp. 235-240. DOI: 10.1109/RI2C60382.2023.10356007.
Bundasak S., Wittayasirikul P. Predictive maintenance using AI for Motor health prediction system // 2022 International Electrical Engineering Congress (iEECON). 2022. Pp. 1-4. DOI: 10.1109/iEECON53204.2022.9741620.
Floresca F., Kyle Tobias C., Ostia C. Naïve Bayes Classification Technique for Brushless DC Motor Fault Diagnosis with Discrete Wavelet Transform Feature Extraction // 2022 14th International Conference on Computer and Automation Engineering (ICCAE). 2022. Pp. 140-144. DOI: 10.1109/ICCAE 55086.2022.9762447.
Zainol A. Burhani N. Detecting Most Influential Parameters in High Voltage Induction Motor Failure using Logistic Regression Analysis // 2022 2nd International Seminar on Machine Learning, Optimization, and Data Science (ISMODE). 2022. Pp. 82-86. DOI: 10.1109/ISMODE56940.2022.10180980.
Silva R., Giesbrecht M. Detection of Broken Rotor Bars in Induction Motors through the k-NN Algorithm Combined with a Deterministic-Stochastic Subspace Method for System Identification // IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. 2021. Pp. 1-6. DOI: 10.1109/IECON48115.2021.9589128.
Kammoun J., Lajnef H., Ghariani M., Hamed B., Fakhfakh M. Diagnostic of Induction Motor Eccentricity Defaults in Electrical Vehicles using SVM // 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). 2024. Pp. 01-05. DOI: 10.1109/IRASET60544. 2024.10549645.
Amihai I., Gitzel R., Kotriwala A. et al. An industrial case study using vibration data and machine learning to predict asset health. // 2018 IEEE 20th Conference on Business Informatics (CBI). 2018. Vol. 1, pp. 178-185.
Yaqub R., Ali H., Bin M. Electrical Motor Fault Detection System using AI's Random Forest Classifier Technique // 2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET). 2023. Pp. 1-5. DOI: 10.1109/IC_ASET58101.2023.10150924.
Kandukuri S., Van Khang H., Robbsersmyr K. Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning // 2018 21st International Conference on Electrical Machines and Systems (ICEMS). 2018. Pp. 1002-1007. DOI: 10.23919/ ICEMS.2018.8549293.
Jiang G., He H., Xie P. Tang Y., Stacked Multilevel-DenoisingAutoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis // IEEE Transactions on Instrumentation and Measurement. 2017. Vol. 66 (9). Pp. 2391-2402. DOI: 10.1109/TIM.2017.2698738.
Han J., Choi D., Park S., Hong S. Diagnosis of motor aging through CNN model using signal correlation // 2020 20th International Conference on Control, Automation and Systems (ICCAS). 2020. Pp. 571-575. DOI: 10.23919/ICCAS50221.2020.9268420.
Husari F., Seshadrinath J. Sensitive Inter-Tum Fault Identifcation in Induction Motors Using Deep Learning Based Methods // 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020). 2020. Pp. 1-6. DOI: 10.1109/PESGRE45664.2020.9070334.
Samanta S., Bera J., Biswas A. Induction Motor Inter-turn Short Circuit Fault Classification by Extracting Auto Features Using LSTM Neural Network // 2024 IEEE 3rd International Conference on Control, Instrumentation, Energy & Communication (CIEC). 2024. Pp. 180-185.DOI: 10.1109/CIEC59440.2024.10468522.
Khaniki M., Mirzaeibonehkhater M., Manthouri M. Enhancing Fault Detection in Induction Motors using LSTM-Attention Neural Networks // 2023 9th International Conference on Control, Instrumentation and Automation (ICCIA). 2023. Pp. 1-5.DOI: 10.1109/ICCIA61416.2023.10506369.
Sameh M., Tarek A.Yassine K. Bearing and Rotor Faults detection and diagnosis of Induction Motors using Statistical Neural Networks // 2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA). 2020. Pp. 77-81. DOI: 10.1109/STA50679.2020.9329334.
Su Y., Gao L., Lu Y. et al.Intelligent Diagnosis Research of Motor Drive Systems Based on Neural Networks // 2024 3rd International Conference on Energy and Electrical Power Systems (ICEEPS). 2024. Pp. 1101-1105. DOI: 10.1109/ICEEPS62542.2024.10693252.
Briza A., Piedad E., Peramo E. Simpler Machine Learning Methods Outperform Deep Learning in Motor Fault Detection // 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). 2024. Pp. 675-680. DOI: 10.1109/ ICAIIC60209.2024.10463445.
Liu L., Guo Y., Lei G., Zhu J. Review of Data-Driven Artificial Intelligence Applications in Electric Machines and Drive Systems // 2023 IEEE International Future Energy Electronics Conference (IFEEC). 2023. Pp. 93-97. DOI: 10.1109/IFEEC58486.2023.10458448.
Zhang Y., Guo R. Fault Diagnosis for Rolling Bearings Based on the Quadrature Particle Filter // 2020 Chinese Control And Decision Conference (CCDC). 2020. Pp. 2148-2155.DOI: 10.1109/CCDC49329.2020.9164797.
Moon C., Han J., Kwon Y. Square-root unscented Kalman filter for state estimation of permanent magnet synchronous motor // 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). 2016. Pp. 460-464. DOI: 10.1109/SICE.2016.7749203.
Behzad M., Arghan H., Bastami A., Zuo M.Prognostics of rolling element bearings with the combination of paris law and reliability method // 2017 Prognostics and System Health Management Conference (PHM-Harbin). 2017. pp. 1-6.DOI: 10.1109/PHM.2017. 8079187.
Huang W., Huang Y., Luo L., Zhou L. A Mixed Logical Dynamic Model-Based Open-Circuit Fault Diagnosis Method for Five-Phase PMSM Drives // 2023 26th International Conference on Electrical Machines and Systems (ICEMS). 2023, pp. 3641-3646.DOI: 10.1109/ICEMS59686.2023.10344412.
Atamuradov V., Medjaher K., DersinP. et al. Prognostics and Health Management for Maintenance Practitioners-Review, Implementation and Tools Evaluation // International Journal of Prognostics and Health Management. 2018. Vol. 8. DOI: 10.36001/ijphm. 2017.v8i3.2667.
Shifat T., Jang-Wook H. Remaining Useful Life Estimation of BLDC Motor Considering Voltage Degradation and Attention-Based Neural Network // IEEE Access. 2020. Vol. 8. Pp. 168414-168428. DOI: 10.1109/ACCESS.2020.3023335.
Maciejewski N., Treml A. Flauzino R. A Systematic Review of Fault Detection and Diagnosis Methods for Induction Motors // 2020 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE). 2020. Pp. 86-90. DOI: 10.1109/FORTEI-ICEE50915.2020.9249890.
Drif M., Cardoso A. Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses // IEEE Transactions on Industrial Informatics. 2014. Vol. 10 (2). Pp. 1348-1360, DOI: 10.1109/TII.2014.2307013.
Bianchini C., Torreggiani A., DavoliM. et al. Stator fault diagnosis by reactive power in dual three-phase reluctance motors // 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). 2019. Pp. 251-256. DOI: 10.1109/DEMPED.2019.8864909.
Furse C., Kafal M., Razzaghi R., Shin Y. Fault Diagnosis for Electrical Systems and Power Networks: A Review //IEEE Sensors Journal. 2021.Vol. 21 (2). Pp. 888-906. DOI: 10.1109/JSEN.2020.2987321.
Niu G., Dong X., Chen Y. Motor Fault Diagnostics Based on Current Signatures: A Review // IEEE Transactions on Instrumentation and Measurement. 2023.Vol. 72. Pp. 1-19. DOI: 10.1109/TIM.2023.3285999.
Guefack F., Kiselev A., Kuznietsov A. Improved Detection of Inter-turn Short Circuit Faults in PMSM Drives using Principal Component Analysis // 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). 2018. pp. 154-159.DOI: 10.1109/SPEEDAM.2018.8445403.
Silva J., Cardoso A. Bearing failures diagnosis in three-phase induction motors by extended Park's vector approach // 31st Annual Conference of IEEE Industrial Electronics Society. 2005. DOI: 10.1109/IECON.2005.1569315.
Messaoudi M., Flah A., Alotaibi A. et al. Diagnosis and fault detection of rotor bars in squirrelcage induction motors using combined park's vector and extended park's vector approaches // Electronics. 2022. Vol. 11 (3). DOI: 10.3390/electronics11030380.
Asad B., Vaimann T., Belahcen A., Kallaste A. Broken Rotor Bar Fault Diagnostic of Inverter Fed Induction Motor Using FFT, Hilbert and Park's Vector Approach // 2018 XIII International Conference on Electrical Machines (ICEM). 2018. Pp. 2352-2358. DOI: 10.1109/ICELMACH.2018.8506957.
Kandukuri S., Huynh V., Robbersmyr K. Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter // IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 2019. Pp. 5992-5997. DOI: 10.1109/IECON.2019.8926983.
Pires V., Foito D., Martins J., Pires A. Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA) // 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). 2015. Pp. 507-512.DOI: 10.1109/PowerEng.2015.7266369.
Becker V., Schwamm T., Urschel S., Antonino-Daviu J. Fault Detection of Circulation Pumps on the Basis of Motor Current Evaluation // IEEE Transactions on Industry Applications. 2021.Vol. 57 (5). Pp. 4617-4624. DOI: 10.1109/TIA.2021.3085697.
Yeolekar S., Mulay G., Helonde J. Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform // 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). 2017. pp. 2011-2015.DOI: 10.1109/RTEICT.2017.8256951.
IshkovaI., Vítek O. Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis // 2015 16th International Scientific Conference on Electric Power Engineering (EPE). 2015. p. 682-686. DOI: 10.1109/EPE.2015.7161130.
Rafaq M., Faizan Shaikh M., Park Y., Lee S. Reliable Airgap Search Coil Based Detection of Induction Motor Rotor Faults Under False Negative Motor Current Signature Analysis Indications // IEEE Transactions on Industrial Informatics. 2022. Vol. 18 (5). Pp. 3276-3285. DOI: 10.1109/TII.2020.3042195.
Pires V., Martins F., Pires A., Rodrigues L. Induction motor broken bar fault detection based on MCSA, MSCSA and PCA: A comparative study // IEEE 2016 10th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). 2016. Pp. 298-303. DOI:10.1109/CPE.2016.7544203.
Dehina W., Boumehraz M., Kratz F., Fantini J. Diagnosis and Comparison between Stator Current Analysis and Vibration Analysis of Static Eccentricity Faults in The Induction Motor. // 4th International Conference on Power Electronics and their Applications (ICPEA). 2019. pp. 1-4, DOI: 10.1109/ICPEA1.2019.8911193.
Bhaumik D., Sadda A., Punekar G. Vibration Signal Analysis of Induction Motor Bearing Faults: Some Aspects // 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES). 2023. Pp. 1-4. DOI: 10.1109/ICSSES58299.2023.10200270.
Noureddine B., Remus P., Raphael R., Salim S. Rolling Bearing Failure Detection in Induction Motors using Stator Current, Vibration and Stray Flux Analysis Techniques // IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. 2020. Pp. 1088-1095. DOI: 10.1109/IECON43393.2020.9254401.
Aniket M., Babasaheb P. A Review: Condition Based Techniques and Predictive Maintenance for Motor. // 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS).2021. Pp. 807-813. DOI:10.1109/ICAIS50930.2021.9395903.
Bhandari M., Silwal B. Development of Machine Learning Model Applied to Industrial Motors for Predictive Maintenance // 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). 2022. Pp. 1632-1635. DOI: 10.1109/IIHC55949.2022.10060358.
Panov S., Nikolov A., Panova S. Review of standarts and systems for predictive maintenance // Science, Engineering and Education. 2021. Vol. 6 (1). DOI: 10.59957/see.v6.i1.2020.1
Kumar S., Mukherjee D., Kumar. P. etal. A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor // IEEE Access. 2019. Vol. 7, pp. 90690-90704. DOI: 10.1109/ACCESS.2019.2926527.
ГОСТ РИСО 13372-2013 Контроль состояния и диагностика машин. Термины и определения: Национальный стандарт Российской Федерации: дата введения 2013-11-22 / Федеральное агентство по техническому регулированию. Изд. официальное. М.: Стандартинформ, 2019. 20 с.
Atamuradov V., Camci F. Segmentation based feature evaluation and fusion for prognostics feature selection based on segment evaluation // The International Journal of Prognostics and Health Management. 2017. Vol. 8(2). Pp. 1-14. DOI: 10.36001/ijphm.2017.v8i2.2643.
Atamuradov V., Medjaher K., Camci F. et al. Machine Health Indicator Construction Framework for Failure Diagnostics and Prognostics // Journal of Signal Processing Systems. 2020. Vol. 92 (8). DOI: 10.1007/s11265-019-01491-4.
Cernuda C. On the relevance of preprocessing in predictive maintenance for dynamic systems // Predictive Maintenance in Dynamic Systems. 2019. pp. 53-93. DOI: 10.1007/978-3-030-05645-2_3.
Haneen Arafat Abu Alfeilat, Ahmad B.A. Hassanat, Omar Lasassmehet al. Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review // Big Data. 2019.Vol. 7(4). Pp. 221-248. DOI: 10.1089/big.2018.0175.
Romanssini M., de Aguirre P., Severo L., Girardi A. A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery // Eng - Advances in Engineering. 2023.Vol. 4(3). pp.1797-1817. DOI: 10.3390/eng4030102.
ГОСТ Р ИСО 13380-2002 Диагностирование машин по рабочим характеристикам. Общие положения: Межгосударственный стандарт: дата введения 2005-08-11 / Федеральное агентство по техническому регулированию. - Изд. официальное. М.:Стандартинформ, 205. 23 с.
ГОСТ Р ИСО 13374-1-2011. Контроль состояния и диагностика машин. Обработка, передача и представление данных: Национальный стандарт Российской Федерации: дата введения 2012-12-01 / Федеральное агентство по техническому регулированию. Изд. официальное. М.:Стандартинформ, 2018. 20 с.
FossierS.Robic P. Maintenance of complex systems - From preventive to predictive // 2017 12th International Conference on Live Maintenance (ICOLIM). 2017. pp. 1-6. DOI: 10.1109/ICOLIM.2017.7964123.
Gu C., He Y., Han X, Chen Z. Product quality oriented predictive maintenance strategy for manufacturing systems // 2017 Prognostics and System Health Management Conference (PHM-Harbin). 2017. pp. 1-7.DOI: 10.1109/PHM.2017.8079213.
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