A Review of the Application of Optimization Algorithms in Condition Monitoring of Rotating Machinery
Subject Areas : Journal of New Applied and Computational Findings in Mechanical Systems
Mehdi Shekarzadeh
1
,
Behnam Gazarzadeh
2
1 - Department of Mechanic, Ahv.C., Islamic Azad University, Ahvaz, Iran
2 - Department of mechanic, Ahv.C. , Islamic Azad University, Ahvaz, Iran
Keywords: Rotating machinery, Condition monitoring, Optimization algorithms, Fault diagnosis,
Abstract :
Due to the critical role of condition monitoring of rotating machinery in enhancing reliability, reducing unexpected failures, and saving maintenance costs, it has garnered significant attention. Traditional condition monitoring methods face challenges such as large data volumes, noise presence, and complexity in fault diagnosis. In recent years, optimization methods have been introduced as effective tools to overcome these problems and have been applied in areas such as data preprocessing, feature selection, and fault classification. This paper provides a comprehensive review of the applications of optimization algorithms in the condition monitoring of rotating machinery and examines their role in improving vibration analysis, thermal monitoring, and predictive maintenance. Furthermore, the integration of these algorithms with artificial intelligence and deep learning, computational challenges, and the need for real-time decision-making are discussed. Finally, recommendations for future research are presented, emphasizing the use of hybrid and advanced methods.
[1] Lei, Y., Li, N., Guo, L., Li, N., Yan, T., Lin, J., (2018), Machinery health prognostics: A systematic review from data acquisition to RUL prediction, 104, 1, pp 799-834, doi: 10.1016/j.ymssp.2017.11.016A.
[2] Heng, S., Zhang, A., Tan, C. C., Mathew, J., (2009), Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical systems and signal processing, 23(3), pp 724-739, doi: 10.1016/j.ymssp.2008.06.009.
[3] Liang, P., Deng, C., Wu, J., Yang, Z., (2020), Intelligent fault diagnosis of rotating machinery via wavelet transforms, generative adversarial nets and convolutional neural network, Measurement, 159, p 107768.
[4] Van Tung, T., Yang, B.-S., (2009), Machine fault diagnosis and prognosis: the state of the art. International Journal of Fluid Machinery and Systems, 2(1), pp 61-71, doi: 10.5293/ijfms.2009.2.1.061.
[5] Jardine, A. K. S., Lin, D., Banjevic, D., (2006), A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical systems and signal processing, 20(7), pp1483-1510, doi.org/10.1016/j.ymssp. 2005.09.012.
[6] Fischer, K., Coronado, D., (2015). Condition monitoring of wind turbines: State of the art, user experience and recommendations, VGB Power Tech, (07), pp 51-56.
[7] Liang, P., Deng, C., Wu, J., Li, G., Yang, Z., Wang, Y., (2019), Intelligent fault diagnosis via semi supervised generative adversarial nets and wavelet transform, IEEE Transactions on Instrumentation and Measurement, 69(7), pp 4659-4671, doi: 10.1109/ TIM. 2019. 2956613.
[8] Viale, L., Daga, A. P., Garibaldi, L., Caronia, S., Ronchi, I., (2022), Books trimmer industrial machine knives diagnosis: a condition-based maintenance strategy through vibration monitoring via novelty detection, In ASME International Mechanical Engineering Congress and Exposition, 86717, p V009T14A021.
[9] Lu, H., Thelen, A., Fink, O., Hu, C., Laflamme, S., (2024), Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis, Mechanical Systems and Signal Processing, 210, p 111068.
[10] Zhao, Z., Jiao, Y., Xu, Y., Chen, Z., Zhao, R., (2024), Smeta-LU: A self-supervised meta-learning fault diagnosis method for rotating machinery based on label updating, Advanced Engineering Informatics, 62, p 102875.
[11] Fu, W., Yang, K., Wen, B., Shan, Li, Y. S., Zheng, B., (2024), Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN, Symmetry, 16(3), p 285.
[12] Gao, Y., Ahmad, Z., Kim, J. M., (2024), Fault diagnosis of rotating machinery using an optimal blind deconvolution method and hybrid invertible neural network, Sensors, 24(1), p 256.
[13] Dong, Y., Xu, M., Li, Y., Wang, R., (2024), General feature spatial location and distance-based unknown Detection: A universal domain adaptation fault diagnosis framework of rotating Machinery, Mechanical Systems and Signal Processing, 208, p 110979.
[14] Surucu, O., Gadsden, S. A., Yawney, J., (2023), Condition monitoring using machine learning: a review of theory, applications, and recent advances, Expert Systems with Applications, 221. doi: 10.1016/j.eswa.2023.119738.
[15] AlShorman, O., Irfan, M., Abdelrahman, R.B, Masadeh M., Alshorman, A., Sheikh, M. A., Saad, N., Rahman, S., (2024), Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors. Engineering Applications of Artificial Intelligence, 130, p 107724.
[16] Zou, L., Zhuang, K. J., Zhou, A., Hu, J., (2023), Bayesian optimization and channel-fusion-based convolutional autoencoder network for fault diagnosis of rotating machinery. Engineering Structures, 280, p 115708.
[17] Jardine, A. K. S., Lin, D., Banjevic, D., (2006), A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), pp 1483-1510, doi: 10.1016/j. ymssp.2005.09.012.
[18] Ali, M. I., Lai, N. S., Abdulla, R., (2024), Predictive maintenance of rotational machinery using deep learning. International Journal of Electrical and Computer Engineering (IJECE), 14(1), pp 1112-1121.
[19] Tsoutsanis, E., Meskin, N., Benammar, M., Khorasani, K., (2014), A component map tuning method for performance prediction and diagnostics of gas turbine compressors, Applied Energy, 135, pp.572-585. doi: 10.1016/j.apenergy.2014.08.115.
[20] de Castro-Cros, M., Velasco, M., Angulo, C., (2021), Machine-learning-based condition assessment of gas turbines—a review. Energies, 14 [online]. doi: 10.3390/en 14248468.
[21] Krishnababu, S., Valero, O., Wells, R., (2021), AI assisted high fidelity multi-physics digital twin of industrial gas turbines, in Proceedings of the ASME Turbo Expo. doi: 10.1115/GT2021-58925.
[22] Xiong, H., Peng, Y., Hu, Y., Zhang, L., Li, Y., (2022). Vibration fault signal analysis and diagnosis of flue gas turbine, Engineering Failure Analysis, 134, p 105981.
[23] Tahan, M., Tsoutsanis, E., Muhammad, M., Abdul Karim, Z. A., (2017), Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review, Applied energy, 198, pp 122-144, doi: 10.1016/ j. apenergy.2017.04.048.
[24] Yang, W., Tavner, P. J., Crabtree, C. J., Feng, Y., Qiu, Y., (2014), Wind turbine condition monitoring: Technical and commercial challenges, Wind energy, 17(5), pp 673-693, doi: 10.1002/we.1508.
[25] Zhou, Z. D., Chen, Y. P., Fuh, J. Y. H., Nee, A. Y. C., (2000), Integrated condition monitoring and fault diagnosis for modern manufacturing systems, CIRP Annals, 49(1), pp 387-390, doi: 10.1016/S0007-8506(07)62971-0.
[26] Hassani, S., Dackermann, U., (2023), A systematic review of optimization algorithms for structural health monitoring and optimal sensor placement Sensors, 23(6), p 3293.
[27] Tan, Y., Zhang, L., (2020), Computational methodologies for optimal sensor placement in structural health monitoring: A review, Structural Health Monitoring, 19(4), pp 1287-1308, doi: 10.1177/1475921719877579.
[28] Sun, H., Büyüköztürk, O., (2015), Optimal sensor placement in structural health monitoring using discrete optimization, Smart Materials and Structures, 24(12), p125034.
[29] Van, M., Hoang, D. T., Kang, H. J., (2020), Bearing fault diagnosis using a particle swarm optimization-least squares wavelet support vector machine classifier, Sensors, 20(12), p 3422.
[30] Chen, Z., Deng, S., Chen, X., Li, C., Sanchez, R. V., Qin, H., (2017). Deep neural networks-based rolling bearing fault diagnosis, Microelectronics Reliability, 75, pp 327-333, doi: 10.1016/j.microrel.2017.03.006.
[31] Wu, J., Li, M., Gao, C., Liu, Z., Zhang, B., Zhang, X., (2023). Research on deployment scheme and routing optimization algorithm of distribution cable condition monitoring devices, Energies, 16(19), p 6930.
[32] Van, M., Hoang, D. T., Kang, H. J., (2020). Bearing fault diagnosis using a particle swarm optimization-least squares wavelet support vector machine classifier, Sensors, 20(12), p 3422.
[33] Kadri, O., Mouss, L.H., (2017), Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization, Academic Journal of Manufacturing Engineering, 15(2), pp 43-50.
[34] Hassani, S., Dackermann, U., (2023), A systematic review of optimization algorithms for structural health monitoring and optimal sensor placement, Sensors, 23(6), p 3293.
[35] Kocak, G., Gokcek, V., Genc, Y., (2023), Condition monitoring and fault diagnosis of a marine diesel engine with machine learning techniques, Pomorstvo, 37(1), pp 32-46, doi: 10. 31217/p.37.1.4.
[36] Brusa, E., Cibrario, L., Delprete, C., Di Maggio, L.G., (2023), Explainable AI for machine fault diagnosis: understanding features’ contribution in machine learning models for industrial condition monitoring, Applied Sciences, 13(4), p 2038.
[37] Di Francescomarino, C., Dumas, M., Federici, M., Ghidini, C., Maggi, F.M., Rizzi, W., Simonetto, L., (2018), Genetic algorithms for hyperparameter optimization in predictive business process monitoring, Information Systems, 74, pp 67-83.
[38] Zhang, W., Zhao, B., Zhou, L., Wang, J., Niu, K., Wang, F., Wang, R., (2022), Research on comprehensive operation and maintenance based on the fault diagnosis system of combine harvester, Agriculture, 12(6), p 893.
[39] Azadeh, A., Saberi, M., Kazem, A., Ebrahimipour, V., Nourmohammadzadeh, A., Saberi, Z., (2013), A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization, Applied Soft Computing, 13(3), pp 1478-1485, doi: 10.1016/j.asoc. 2012.06.020.
[40] Liang, J., Liao, Y., Chen, Z., Lin, H., Jin, G., Gryllias, K., Li, W., (2022), Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators, IET Collaborative Intelligent Manufacturing, 4(3), pp 94-207, doi: 10.1049/cim2.12055.
[41] Song, B., Liu, Y., Fang, J., Liu, W., Zhong, M., Liu, X., (2024), An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples, Neurocomputing, 574, p 127284.
[42] Parziale, M., Lomazzi, L., Giglio, M., Cadini, F., (2023), Physics-informed neural networks for the condition monitoring of rotating shafts, Sensors, 24(1), p 207.
[43] Dereci, U., Tuzkaya, G., (2024), An explainable artificial intelligence model for predictive maintenance and spare parts optimization, Supply Chain Analytics, 8, p 100078.
[44] Brusa, E., Cibrario, L., Delprete, C., Di Maggio, L.G., (2023), Explainable AI for machine fault diagnosis: understanding features’ contribution in machine learning models for industrial condition monitoring, Applied Sciences, 13(4), p 2038.
