REFERENCES

1. Ji, C.; Zhou, T.; Huang, H. Operating rules derivation of Jinsha Reservoirs system with parameter calibrated support vector regression. Water. Resour. Manage. 2014, 28, 2435-51.

2. Sun, L.; Niu, D.; Wang, K.; Xu, X. Sustainable development pathways of hydropower in China: interdisciplinary qualitative analysis and scenario-based system dynamics quantitative modeling. J. Clean. Prod. 2021, 287, 125528.

3. Wu, H.; Han, C.; Zhao, L.; Xu, J.; Fu, Y.; Ren, X. Research on 3D modeling digital twin technology based on Kraftwerk-Kennzeichen-System coding for hydropower stations. In 2024 5th International Conference on Clean Energy and Electric Power Engineering (ICCEPE), Yangzhou, China. IEEE; 2024. pp. 358-64.

4. Wang, P.; Guo, Y.; Xu, Z.; Wang, W.; Chen, D. A novel approach of full state tendency measurement for complex systems based on information causality and PageRank: a case study of a hydropower generation system. Mech. Syst. Signal. Process. 2023, 187, 109956.

5. He, Y. L.; Ma, Z. G.; Li, Q. A.; Huang, .; Z, . Y. Research and application of maintenance decision-making for hydropower units based on reliability-centered maintenance. Mech. Electr. Technol. Hydropower. Stn. 2024, 47, 29-31,35. (in Chinese).

6. Kumar, K.; Saini, R. A review on operation and maintenance of hydropower plants. Sustain. Energy. Technol. Assess. 2022, 49, 101704.

7. de Santis, R. B.; Gontijo, T. S.; Costa, M. A. Condition-based maintenance in hydroelectric plants: a systematic literature review. Proc. Inst. Mech. Eng. O. J. Risk. Reliab. 2022, 236, 631-46.

8. Zheng, R.; Chen, B.; Gu, L. Condition-based maintenance with dynamic thresholds for a system using the proportional hazards model. Reliab. Eng. Syst. Saf. 2020, 204, 107123.

9. Lee, J.; Wu, F.; Zhao, W.; Ghaffari, M.; Liao, L.; Siegel, D. Prognostics and health management design for rotary machinery systems - reviews, methodology and applications. Mech. Syst. Signal. Process. 2014, 42, 314-34.

10. Luczak, A. Neurons as autonomous agents: a biologically inspired framework for cognitive architectures in artificial intelligence. Cogn. Syst. Res. 2025, 90, 101338.

11. Cui, Y.; Luo, H.; Yang, T.; Qin, W.; Jing, X. Bio-inspired structures for energy harvesting self-powered sensing and smart monitoring. Mech. Syst. Signal. Process. 2025, 228, 112459.

12. Jiang, Q.; Li, X.; Yang, L.; Ma, Y.; Li, H. Innovation and application of reliability-centered maintenance technology for pumped storage power plant. J. Phys. Conf. Ser. 2024, 2694, 012014.

13. Pourahmadi, F.; Fotuhi-Firuzabad, M.; Dehghanian, P. Application of game theory in reliability-centered maintenance of electric power systems. IEEE. Trans. Ind. Appl. 2017, 53, 936-46.

14. Alagöz, İ.; Bulut, M.; Geylani, V.; Yıldırım, A. Importance of real-time hydro power plant condition monitoring systems and contribution to electricity production. TEPES 2020, 1, 1-11.

15. Guilan, W.; Hongshan, Z.; Shuangwei, G.; Zengqiang, M. Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis. IET. Renew. Power. Gener. 2017, 11, 1597-602.

16. Kamm, S.; Jazdi, N.; Weyrich, M. Knowledge discovery in heterogeneous and unstructured data of Industry 4.0 Systems: challenges and approaches. Procedia. CIRP. 2021, 104, 975-80.

17. Wang, M.; Wang, X.; Yang, L. T.; Deng, X.; Yi, L. Multi-sensor fusion based intelligent sensor relocation for health and safety monitoring in BSNs. Inf. Fusion. 2020, 54, 61-71.

18. Hartigan, J. A.; Wong, M. A. Algorithm AS 136: a K-means clustering algorithm. J. R. Stat. Soc. C. Appl. Stat. 1979, 28, 100-8.

19. Medeiros, A.; Cardoso, R.; Oliveira Júnior, J.; Alves, S. Failure analysis of gear using continuous wavelet transform applied in the context of wind turbines. Proc. Inst. Mech. Eng. J. J. Eng. Tribol. 2024, 238, 860-8.

20. Ghods, M.; Tabarniarami, Z.; Faiz, J.; Bazrafshan, M. A. Turn-to-turn and phase-to-phase short circuit fault detection of wind turbine permanent magnet generator based on equivalent magnetic network modelling by wavelet transform approach. IET. Electric. Power. Appl. 2024, 18, 1005-20.

21. Tian, H.; Yang, L.; Ji, P. Intelligent analysis of vibration faults in hydroelectric generating units based on empirical mode decomposition. Processes 2023, 11, 2040.

22. An, X.; Pan, L. Characteristic parameter degradation prediction of hydropower unit based on radial basis function surface and empirical mode decomposition. J. Vib. Control. 2015, 21, 2200-11.

23. Liu, T.; Kong, F.; Yang, L.; Guo, Z. Operational risk assessment of hydropower units based on PSSCA-VMD-CNN-GBiLSTM and multi-feature fusion. Comput. Electr. Eng. 2024, 118, 109412.

24. Fang, M.; Zhang, F.; Yang, Y.; Tao, R.; Xiao, R.; Zhu, D. The influence of optimization algorithm on the signal prediction accuracy of VMD-LSTM for the pumped storage hydropower unit. J. Energy. Storage. 2024, 78, 110187.

25. Zhang, F.; Guo, J.; Yuan, F.; Shi, Y.; Li, Z. Research on denoising method for hydroelectric unit vibration signal based on ICEEMDAN-PE-SVD. Sensors 2023, 23, 6368.

26. Ren, Y.; Liu, P.; Hu, L.; et al. Research on noise reduction method of pressure pulsation signal of draft tube of hydropower unit based on ALIF-SVD. Shock. Vib. 2021, 2021, 5580319.

27. Lu, Z.; Tao, R.; Xiao, R.; Li, P. Forecasting the hydropower unit vibration based on adaptive variational mode decomposition and neural network. Appl. Soft. Comput. 2024, 150, 111040.

28. Szymański, J.; Operlejn, M.; Weichbroth, P. Enhancing word embeddings for improved semantic alignment. Appl. Sci. 2024, 14, 11519.

29. Yang, Z. Fault diagnosis of wind turbine bearing based on CNN-XGBoost. J. Phys. Conf. Ser. 2021, 2033, 012200.

30. Łuczak, D. Data-driven rotary machine fault diagnosis using multisensor vibration data with bandpass filtering and convolutional neural network for signal-to-image recognition. Electronics 2024, 13, 2940.

31. Valentín, D.; Presas, A.; Valero, C.; Egusquiza, M.; Egusquiza, E. Selection and optimization of sensors for monitoring of francis turbines. IOP. Conf. Ser. Earth. Environ. Sci. 2021, 774, 012028.

32. Li, J.; Pang, J.; Qin, M.; et al. Study on sand wear testing and numerical simulation of a 500 MW class pelton turbine. Water 2025, 17, 317.

33. Shrestha, R.; Gurung, P.; Chitrakar, S.; et al. Review on experimental investigation of sediment erosion in hydraulic turbines. Front. Mech. Eng. 2024, 10, 1526120.

34. Mazaheri-Tehrani, E.; Faiz, J. Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: an overview. IET. Electric. Power. Appl. 2022, 16, 277-99.

35. Li, Y.; Li, Z. Application of a novel wavelet shrinkage scheme to partial discharge signal denoising of large generators. Appl. Sci. 2020, 10, 2162.

36. Lucena, E. S. D.; Oliveira, T. F. D.; Maciel, M. C.; et al. Hydrological cycle and humidity on the behavior of partial discharges in hydrogenerator. Int. J. Power. Energy. Syst. 2020, 40, 203-0178.

37. Zio, E. Reliability engineering: old problems and new challenges. Reliab. Eng. Syst. Saf. 2009, 94, 125-41.

38. Wu, J.; Kang, R.; Li, X. Uncertain accelerated degradation modeling and analysis considering epistemic uncertainties in time and unit dimension. Reliab. Eng. Syst. Saf. 2020, 201, 106967.

39. Wang, P.; Xu, Z.; Chen, D. An integrated framework for reliability prediction and condition-based maintenance policy for a hydropower generation unit using GPHM and SMDP. Reliab. Eng. Syst. Saf. 2023, 238, 109419.

40. Liu, Y.; Xu, Y.; Liu, J.; Shi, Y.; Li, S.; Zhou, J. Real-time comprehensive health status assessment of hydropower units based on multi-source heterogeneous uncertainty information. Measurement 2023, 216, 112979.

41. O’Connor, P. D. T. Commentary: reliability-past, present, and future. IEEE. Trans. Rel. 2000, 49, 335-41.

42. Guo, L.; Li, N.; Jia, F.; Lei, Y.; Lin, J. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing 2017, 240, 98-109.

43. Cao, X.; Li, P.; Ming, S. Remaining useful life prediction-based maintenance decision model for stochastic deterioration equipment under data-driven. Sustainability 2021, 13, 8548.

44. Backlund, F.; Akersten, P. RCM introduction: process and requirements management aspects. J. Qual. Maint. Eng. 2003, 9, 250-64.

45. Gupta, G.; Mishra, R. P. A SWOT analysis of reliability centered maintenance framework. J. Qual. Maint. Eng. 2016, 22, 130-45.

46. Zhu, H.; Liu, S.; Qu, Y.; Han, X.; He, W.; Cao, Y. A new risk assessment method based on belief rule base and fault tree analysis. Proc. Inst. Mech. Eng. O. J. Risk. Reliab. 2022, 236, 420-38. https://www.researchgate.net/publication/350977628_A_new_risk_assessment_method_based_on_belief_rule_base_and_fault_tree_analysis. (accessed 15 Sep 2025).

47. Zhu, Y. L.; Chen, H. N.; Shen, H. Bio-inspired computing: individual, swarm and community evolution models and methods. Tsinghua University Press; 2013. https://xueshu.baidu.com/ndscholar/browse/detail?paperid=770483c21984702b7aad97ff3256b530. (accessed 15 Sep 2025).

48. Mcculloch, W. S.; Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943, 5, 115-33.

49. Hebb, D. O. The organization of behavior: a neuropsychological theory. 1st edition. Psychology Press; 2005.

50. Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 1958, 65, 386-408.

51. Holland, J. H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press; 1992.

52. Minsky, M.; Papert, S. A. Perceptrons: an introduction to computational geometry. MIT press; 1988.

53. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U. S. A. 1982, 79, 2554-8.

54. Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning representations by back-propagating errors. Nature 1986, 323, 533-6.

55. Dorigo, M.; Maniezzo, V.; Colorni, A. Ant system: optimization by a colony of cooperating agents. IEEE. Trans. Syst. Man. Cybern. B. Cybern. 1996, 26, 29-41.

56. Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, Australia. November 27 - December 01, 1995. IEEE; 1995. pp. 1942-8.

57. Hinton, G. E.; Osindero, S.; Teh, Y. W. A fast learning algorithm for deep belief nets. Neural. Comput. 2006, 18, 1527-54.

58. Hinton, G. E.; Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504-7.

59. Gandolfi, D.; Mapelli, J.; Puglisi, F. M. Editorial: brain-inspired computing: from neuroscience to neuromorphic electronics for new forms of artificial intelligence. Front. Neurosci. 2025, 19, 1565811.

60. Zhang, G.; Zhang, P.; Zhou, F.; et al. Multi-scale spatio-temporal data modelling and brain-like intelligent optimisation strategies in power equipment operation and inspection. Appl. Math. Nonlinear. Sci. 2025, 10, 20250022.

61. Zhang, P.; Zhang, G.; Zhou, F.; et al. Research on power dynamic data sample generation technology based on brain-like computation and its efficient computation methods. Appl. Math. Nonlinear. Sci. 2025, 10, 20250023.

62. Tozer, L. ‘Biocomputer’ combines lab-grown brain tissue with electronic hardware. Nature 2023, 624, 481.

63. Kar, A. K. Bio inspired computing - a review of algorithms and scope of applications. Expert. Syst. Appl. 2016, 59, 20-32.

64. Li, J.; Hu, Y.; Yang, S. X. A novel knowledge-based genetic algorithm for robot path planning in complex environments. IEEE. Trans. Evol. Comput. 2025, 29, 375-89.

65. Binitha, S.; Sathya, S. S. A survey of bio inspired optimization algorithms. Int. J. Soft. Comput. Eng. 2012, 2, 137-50. https://www.ijsce.org/wp-content/uploads/papers/v2i2/B0523032212.pdf. (accessed 15 Sep 2025).

66. Fister, I. Jr.; Yang, X. S.; Fister, I.; Brest, J.; Fister, D. A brief review of nature-inspired algorithms for optimization. arXiv 2013, arXiv:1307.4186. https://doi.org/10.48550/arXiv.1307.4186. (accessed 2025-09-15).

67. Li, J.; Yang, S. X. Intelligent collective escape of swarm robots based on a novel fish-inspired self-adaptive approach with neurodynamic models. IEEE. Trans. Ind. Electron. 2024, 71, 14460-9.

68. Samigulina, G.; Samigulina, Z. Diagnostics of industrial equipment and faults prediction based on modified algorithms of artificial immune systems. J. Intell. Manuf. 2022, 33, 1433-50.

69. Li, J.; Yang, S. X. A novel feature learning-based bio-inspired neural network for real-time collision-free rescue of multirobot systems. IEEE. Trans. Ind. Electron. 2024, 71, 14420-9.

70. Ma, L.; Chen, S.; Wei, D.; Zhang, Y.; Guo, Y. A comprehensive hybrid deep learning approach for accurate status predicting of hydropower units. Appl. Sci. 2024, 14, 9323.

71. Dao, F.; Zeng, Y.; Zou, Y.; Qian, J. Fault diagnosis method for hydropower unit via the incorporation of chaotic quadratic interpolation optimized deep learning model. Measurement 2024, 237, 115199.

72. Li, X.; Zhang, J.; Xiao, B.; et al. Fault diagnosis of hydropower units based on Gramian angular summation field and parallel CNN. Energies 2024, 17, 3084.

73. Wang, Y.; Xiao, Z.; Liu, D.; Chen, J.; Liu, D.; Hu, X. Degradation trend prediction of hydropower units based on a comprehensive deterioration index and LSTM. Energies 2022, 15, 6273.

74. Zhang, J.; Liu, L.; Wang, L.; Xi, W. Fault detection of key parts of wind turbine based on BP neural network combination prediction model. Energy. Inform. 2024, 7, 436.

75. Gao, Y.; Miyata, S.; Akashi, Y. Automated fault detection and diagnosis of chiller water plants based on convolutional neural network and knowledge distillation. Build. Environ. 2023, 245, 110885.

76. Cacace, J.; Scognamiglio, V.; Ruggiero, F.; Lippiello, V. Motor fault detection and isolation for multi-rotor UAVs based on external wrench estimation and recurrent deep neural network. J. Intell. Robot. Syst. 2024, 110, 2176.

77. Torres-Cabrera, J.; Maldonado-Correa, J.; Valdiviezo-Condolo, M.; Artigao, E.; Martín-Martínez, S.; Gómez-Lázaro, E. A novel data-driven approach with a long short-term memory autoencoder model with a multihead self-attention deep learning model for wind turbine converter fault detection. Appl. Sci. 2024, 14, 7458.

78. Perez-Sanjines, F.; Peeters, C.; Verstraeten, T.; Antoni, J.; Nowé, A.; Helsen, J. Fleet-based early fault detection of wind turbine gearboxes using physics-informed deep learning based on cyclic spectral coherence. Mech. Syst. Signal. Process. 2023, 185, 109760.

79. Yan, K.; Chong, A.; Mo, Y. Generative adversarial network for fault detection diagnosis of chillers. Build. Environ. 2020, 172, 106698.

80. Yang, S.; Zhou, Y.; Chen, X.; Li, C.; Song, H. Fault diagnosis for wind turbines with graph neural network model based on one-shot learning. R. Soc. Open. Sci. 2023, 10, 230706.

81. Li, P.; Anduv, B.; Zhu, X.; Jin, X.; Du, Z. Diagnosis for the refrigerant undercharge fault of chiller using deep belief network enhanced extreme learning machine. Sustain. Energy. Technol. Assess. 2023, 55, 102977.

82. Cherng, A. Optimal sensor placement for modal parameter identification using signal subspace correlation techniques. Mech. Syst. Signal. Process. 2003, 17, 361-78.

83. Zhang, X. H.; Wu, S. B.; Fang, S. E.; Chen, L. X. Multi-objective optimization of sensor placement for structural health monitoring using pareto artificial fish swarm algorithm. J. Vib. Eng. 2022, 35, 351-8. (in Chinese). http://zdgcxb.csve.org.cn/cn/article/pdf/preview/202202010.pdf. (accessed 15 Sep 2025).

84. Yue, H. Y.; Lv, M.; Li, H. W.; Liu, Z. Z.; Zhong, Y. F. Arrangement of pressure monitoring points in water supply network based on swarm intelligence optimization algorithm. China. Water. Wastewater. 2020, 36, 66-70. (in Chinese).

85. Shmelev, N. G.; Gorbatsevich, M. I.; Kryukov, I. I.; Kovalev, A. G. Inspection of rotor disks of HPT and LPT of TK-10-4 gas-compressor units by the ultrasonic flaw detection method. Russ. J. Nondestruct. Test. 2012, 48, 15-22.

86. Rafajlowicz, E. Optimal experiment design for identification of linear distributed-parameter systems: frequency domain approach. IEEE. Trans. Autom. Control. 1983, 28, 806-8.

87. Niknam, T.; Olamaei, J.; Amiri, B. A hybrid evolutionary algorithm based on ACO and SA for cluster analysis. J. Appl. Sci. 2008, 8, 2695-702.

88. Zhang, W. S.; Wang, Z. G. Missing data prediction based on improved sparrow algorithm optimized deep extreme learning machine. Electr. Meas. Technol. 2024, 45, 63-7. (in Chinese).

89. Tyagi, S.; Panigrahi, S. An improved envelope detection method using particle swarm optimisation for rolling element bearing fault diagnosis. J. Comput. Des. Eng. 2017, 4, 305-17.

90. Cerrada, M.; Zurita, G.; Cabrera, D.; Sánchez, R.; Artés, M.; Li, C. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mech. Syst. Signal. Process. 2016, 70-1, 87-103.

91. Cao, C.; Li, M.; Jiang, S.; Zhang, G.; Li, Z.; Lu, N. Fault warning method of a hydropower unit based on IMSGP-WEDI. J. Vib. Shock. 2024, 43, 52-60. https://jvs.sjtu.edu.cn/EN/Y2024/V43/I8/52. (accessed 15 Sep 2025).

92. Shao, H.; Jiang, H.; Zhao, H.; Wang, F. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech. Syst. Signal. Process. 2017, 95, 187-204.

93. Long, X.; Li, S.; Wu, X.; Jin, Z.; Salcedo, J. V. Wind turbine anomaly identification based on improved deep belief network with SCADA data. Math. Probl. Eng. 2021, 2021, 1-15.

94. Liu, P.; Zhang, W. A fault diagnosis intelligent algorithm based on improved BP neural network. Int. J. Patt. Recogn. Artif. Intell. 2019, 33, 1959028.

95. Li, Q.; Zhuo, Z.; Gao, R.; et al. A pig behavior-tracking method based on a multi-channel high-efficiency attention mechanism. Agric. Commun. 2024, 2, 100062.

96. Fu, Z. X.; Yin, G.; Zhu, J. P.; Yuan, Y. Research on deterioration degree prediction method for hydropower units based on EEMD and LSTM. Acta. Energiae. Solaris. Sin. 2022, 43, 75-81. (in Chinese).

97. Luo, Y.; Wu, B. X. Water turbine vibration fault prediction method based on deep learning LSTM-DBN. J. Vib. Meas. Diagn. 2022, 42, 1233-8+51. (in Chinese).

98. Lan, Q.; Zhu, Y.; Lin, B.; Zuo, Y.; Lai, Y. Fault prediction for rotating mechanism of satellite based on SSA and improved informer. Appl. Sci. 2024, 14, 9412.

99. Ayoobian, N.; Mohsendokht, M. Multi-objective optimization of maintenance programs in nuclear power plants using genetic algorithm and sensitivity index decision making. Ann. Nucl. Energy. 2016, 88, 95-9.

100. Al-Majali, B. H.; Zobaa, A. F. Analyzing bi-objective optimization Pareto fronts using square shape slope index and NSGA-II: a multi-criteria decision-making approach. Expert. Syst. Appl. 2025, 272, 126765.

101. Tripathi, A.; Gupta, P.; Trivedi, A.; Kala, R. Wireless sensor node placement using hybrid genetic programming and genetic algorithms. Int. J. Intell. Inf. Technol. 2011, 7, 63-83.

102. Xu, X.; Deng, J.; Lin, H.; Li, Z.; Wen, H. Lightweight anomalous detection of hydro turbine operation sound using fusion network enhanced by load information. IEEE. Trans. Instrum. Meas. 2025, 74, 1-13.

103. Kumar, N.; Kumar, H. A fuzzy clustering technique for enhancing the convergence performance by using improved fuzzy c-means and particle swarm optimization algorithms. Data. Knowl. Eng. 2022, 140, 102050.

104. Wang, Y.; Xu, W. A novel decision optimization for thermal power unit based on condition-based predictive maintenance and equilibrium optimizer. Phys. Commun. 2024, 65, 102372.

105. Bai, M.; Liu, J.; Ma, Y.; Zhao, X.; Long, Z.; Yu, D. Long short-term memory network-based normal pattern group for fault detection of three-shaft marine gas turbine. Energies 2021, 14, 13.

106. Han, S.; Shao, H.; Huo, Z.; Yang, X.; Cheng, J. End-to-end chiller fault diagnosis using fused attention mechanism and dynamic cross-entropy under imbalanced datasets. Build. Environ. 2022, 212, 108821.

107. Wu, Z.; He, L.; Wang, W.; Ju, Y.; Guo, Q. A fault prediction method for CNC machine tools based on SE-ResNet-Transformer. Machines 2024, 12, 418.

108. Yang, Z.; Li, G.; Xue, G.; He, B.; Song, Y.; Li, X. A novel multi-sensor local and global feature fusion architecture based on multi-sensor sparse Transformer for intelligent fault diagnosis. Mech. Syst. Signal. Process. 2025, 224, 112188.

109. Li, T.; Zhou, Z.; Li, S.; Sun, C.; Yan, R.; Chen, X. The emerging graph neural networks for intelligent fault diagnostics and prognostics: a guideline and a benchmark study. Mech. Syst. Signal. Process. 2022, 168, 108653.

110. Nguyen, V.; Do, P.; Vosin, A.; Iung, B. Artificial-intelligence-based maintenance decision-making and optimization for multi-state component systems. Reliab. Eng. Syst. Saf. 2022, 228, 108757.

111. Liao, W.; Long, X.; Jiang, C. A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth. Int. J. Fatigue. 2025, 191, 108678.

112. Jiang, X. C.; Zang, Y. M.; Liu, Y. D.; Sheng, G. H.; Xu, Y. P.; Qian, Q. L. ChatGPT-like models and key technologies for power equipment. High. Volt. Eng. 2023, 49, 4033-45. (in Chinese).

113. Li, T.; Yang, J.; Ioannou, A. Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning. Renew. Energy. 2024, 234, 121265.

114. Li, Y.; Jiang, W.; Zhang, G.; Shu, L. Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data. Renew. Energy. 2021, 171, 103-15.

115. Mei, X.; Yuan, X.; Jin, J.; et al. ATGCN: an adaptive temporal-topological graph convolution network with nodal attention for robot fault diagnosis. IEEE/ASME. Trans. Mechatron. 2025, 1-11.

116. Grieves, M.; Vickers, J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen F, Flumerfelt S, Alves A, editors. Transdisciplinary perspectives on complex systems. Cham: Springer International Publishing; 2017. pp. 85-113.

117. Wang, Y. H.; Cao, T.; Gao, S. L.; et al. Conceptualization and application prospects of a digital twin system for hydropower unit. Proc CSEE 2025;45:4526-42. (in Chinese) https://cstj.cqvip.com/Qikan/Article/Detail?id=7201259657&from=Qikan_Search_Index. (accessed 15 Sep 2025).

118. Li, J.; Xu, Z.; Zhu, D.; et al. Bio-inspired intelligence with applications to robotics: a survey. Intell. Robot. 2021, 1, 58-83.

119. Liu, Z.; Zheng, J.; Zhang, Q.; Xu, R. Advances and trends in intelligent maintenance for wind turbine systems. Sustain. Energy. Technol. Assess. 2025, 80, 104398.

120. Dhinakaran, D.; Edwin Raja, S.; Velselvi, R.; Purushotham, N. Intelligent IoT-driven advanced predictive maintenance system for industrial applications. SN. Comput. Sci. 2025, 6, 151.

121. Hulwan, D. B.; Shitra, C.; Chokkalingan, A.; et al. AI-based fault detection and predictive maintenance in wind power conversion systems. E3S. Web. Conf. 2024, 591, 02003.

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