A multimodal explainable AI framework for industrial turbine vibration health monitoring and regulatory decision support in finance

  • Alexey Mikhaylov orcid

    Department of Informatics, Plekhanov Russian University of Economics, Moscow 117997, Russia; Department of Science, Baku Eurasian University, Baku AZ1073, Republic of Azerbaijan
  • Sergey Barykin orcid

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia
  • Daria Dinets orcid

    Department of Finance, Accounting, and Auditing, Peoples’ Friendship University of Russia Named after Patrice Lumumba, Moscow 125167, Russia
  • Vasilii Buniak

    Department of Economics and Finance, Financial University under the Government of the Russian Federation, St. Petersburg 197198, Russia
  • Oksana Solodchenkova

    Department of Economics and Management, Financial University under the Government of the Russian Federation, Smolensk 214018, Russia
  • Elena Sidorova

    Department of Finance, Accounting, and Auditing, Peoples’ Friendship University of Russia Named after Patrice Lumumba, Moscow 125167, Russia
  • Tatyana Kirillova

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia
  • Elvira Rustenova

    Institute of Economics, IT and Vocational Training, West Kazakhstan Agrarian and Technical University Named after Zhangir Khan, Uralsk 090000, Kazakhstan
  • Miras Kilau

    Institute of Economics, IT and Vocational Training, West Kazakhstan Agrarian and Technical University Named after Zhangir Khan, Uralsk 090000, Kazakhstan
  • Gumar Batov

    Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, Nalchik 360002, Russia
  • Akram Ochilov

    Department of Economics, Karshi State University, Karshi 180119, Uzbekistan
  • Yuri Sotskov

    United Institute of Informatics Problems, National Academy of Sciences of Belarus, 220072 Minsk, Belarus
  • Tomonobu Senjyu

    Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
  • Mahmoud Delavar

    Center of Excellence in Geomatic Engineering in Disaster Management and Land Administration in Smart City Lab, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417614411, Iran
  • N.B.A. Yousif

    Department of Sociology, College of Humanities and Science, Ajman University, Ajman P.O. Box 346, United Arab Emirates; Humanities and Social Sciences Research Centre (HSSRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
  • Anthony Nyangarika

    Department of Finance, The Nelson Mandela African Institution of Science and Technology, Arusha 63230, Tanzania
Article ID: 4705
DOI: https://doi.org/10.59400/sv4705
Keywords: multimodal sensor fusion; explainable AI (XAI); vibration monitoring; transformer; SHAP; DEMATEL; industrial diagnostics; gas turbine

Abstract

Industrial rotating machinery plays a pivotal role in global energy infrastructure, yet conventional vibration monitoring systems often operate as black boxes, providing limited interpretability and failing to leverage the rich multi-sensor data available in modern plants. This paper introduces a novel framework that integrates multimodal sensor fusion—combining accelerometer, acoustic, thermal, and operational data—with explainable artificial intelligence (XAI) and multi-criteria decision analysis. The core engine is a domain-collaborative multimodal transformer that jointly processes heterogeneous time-series and image-based streams, producing fault classifications alongside SHapley Additive exPlanations (SHAP)-based feature attributions and natural-language diagnostic narratives. The framework is validated on a 250 MW combined-cycle gas turbine power plant with 24 months of operational data. Experimental results demonstrate a fault detection accuracy of 94.2%, a 14.5% improvement over vibration-only baselines, while achieving the highest interpretability score (5/5) among compared methods. Decision Making Trial and Evaluation Laboratory (DEMATEL) causal analysis identifies diagnostic transparency and system reliability as primary drivers of regulatory compliance. The primary contribution is an open-source, scalable blueprint for trustworthy AI in industrial vibration monitoring, addressing the urgent need for transparent, auditable, and human-centered decision support in critical energy assets. The proposed framework achieves a balanced integration of three critical dimensions: diagnostic accuracy and interpretability, technical performance and regulatory compliance, and automated inference and human oversight. Based on these findings, we recommend that industrial operators for finance risk optimization: (1) deploy multimodal sensor arrays combining vibration, acoustic, thermal, and operational sensors; (2) implement explainable AI protocols utilizing SHAP-based feature attribution; and (3) adopt DEMATEL-derived priorities for risk-informed maintenance scheduling.

Published
2026-08-17
How to Cite
Mikhaylov, A., Barykin, S., Dinets, D., Buniak, V., Solodchenkova, O., Sidorova, E., Kirillova, T., Rustenova, E., Kilau, M., Batov, G., Ochilov, A., Sotskov, Y., Senjyu, T., Delavar, M., N.B.A. Yousif, & Nyangarika, A. (2026). A multimodal explainable AI framework for industrial turbine vibration health monitoring and regulatory decision support in finance. Sound & Vibration, 60(5). https://doi.org/10.59400/sv4705
Issue
Vol. 60 No. 5 (2026): In Progress
Section
Article (This article belongs to the special issue "Intelligent Systems in Sound and Vibration Analysis")

Copyright (c) 2026 Alexey Mikhaylov, Sergey Barykin, Daria Dinets, Vasilii Buniak, Oksana Solodchenkova, Elena Sidorova, Tatyana Kirillova, Elvira Rustenova, Miras Kilau, Gumar Batov, Akram Ochilov, Yuri Sotskov, Tomonobu Senjyu, Mahmoud Delavar, N.B.A. Yousif, Anthony Nyangarika

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Aboutiman A, Maamoun KSA, Karimi HR, et al. Hybrid deep learning–based active noise control for encapsulated structures with openings. Expert Systems with Applications. 2026; 309: 131247. doi: 10.1016/j.eswa.2026.131247

[2]Ahangar MN, Farhat ZA, Sivanathan A, et al. Explainable AI-driven quality and condition monitoring in smart manufacturing. Sensors. 2026; 26(3): 911. doi: 10.3390/s26030911

[3]Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion. 2020; 58: 82–115. doi: 10.1016/j.inffus.2019.12.012

[4]Attouri K, Mansouri M, Kouadri A. Adaptive polynomial Kolmogorov–Arnold networks with multimodal sensor data fusion for high-precision fault diagnosis in wind energy systems. IEEE Sensors Journal. 2025; 25(24): 44318–44325. doi: 10.1109/JSEN.2025.3623916

[5]Calafà M, Xia Y, Jeong CH. HergNet: A fast neural surrogate model for sound field predictions via superposition of plane waves. In: Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 3–8 May 2026; Barcelona, Spain. pp. 15197–15201. doi: 10.1109/ICASSP55912.2026.11461335

[6]Cerrada M, Sánchez RV, Li C, et al. A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing. 2018; 99: 169–196. doi: 10.1016/j.ymssp.2017.06.012

[7]Ciobotaru A, Corches C, Gota D, et al. An explainable deep learning-based predictive maintenance solution for air compressor condition monitoring. Sensors. 2025; 25(18): 5797. doi: 10.3390/s25185797

[8]Fontela E, Gabus A. The DEMATEL Observer. Battelle Geneva Research Center; 1976.

[9]Gunning D, Stefik M, Choi J, et al. XAI—Explainable artificial intelligence. Science Robotics. 2019; 4(37): eaay7120. doi: 10.1126/scirobotics.aay7120

[10]He D, Li R, Zhu J, et al. Data mining based full ceramic bearing fault diagnostic system using AE sensors. IEEE Transactions on Neural Networks. 2011; 22(12): 2022–2031. doi: 10.1109/TNN.2011.2169087

[11]Jardine AKS, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing. 2006; 20(7): 1483–1510. doi: 10.1016/j.ymssp.2005.09.012

[12]Konar P, Chattopadhyay P. Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Applied Soft Computing. 2011; 11(6): 4203–4211. doi: 10.1016/j.asoc.2011.03.014

[13]Kumar A, Zhou Y, Mucchi E, et al. Explainable artificial intelligence-based simulation-to-real domain adaptation for robust rotor condition monitoring. Advanced Engineering Informatics. 2026; 74: 104652. doi: 10.1016/j.aei.2026.104652

[14]Lei Y, Lin J, He Z, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing. 2013; 35(1–2): 108–126. doi: 10.1016/j.ymssp.2012.09.015

[15]Lei Y, Yang B, Jiang X, et al. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing. 2020; 138: 106587. doi: 10.1016/j.ymssp.2019.106587

[16]Li S, Liu G, Tang X, et al. An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis. Sensors. 2017; 17(8): 1729. doi: 10.3390/s17081729

[17]Liu B, Gao Z, Lu B, et al. Deep learning-based remaining useful life estimation of bearings with time-frequency information. Sensors. 2022; 22(19): 7402. doi: 10.3390/s22197402

[18]Li Y, Wang X, Liu Z, et al. The entropy algorithm and its variants in the fault diagnosis of rotating machinery: A review. IEEE Access. 2018; 6: 66723–66741. doi: 10.1109/ACCESS.2018.2873782

[19]Li X, Ding Q, Sun JQ. Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety. 2018; 172: 1–11. doi: 10.1016/j.ress.2017.11.021

[20]Liu Y, Zhang R, Grossi L, et al. A novel condition monitoring approach using hybrid lightweighted adaptive models for complex machinery. Engineering Applications of Artificial Intelligence. 2025; 162: 112461. doi: 10.1016/j.engappai.2025.112461

[21]Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017); 4–9 December 2017; Long Beach, CA, USA. Available online: https://papers.nips.cc/paper_files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

[22]Miao T, Liu T, Liu L, et al. Based on acoustic emission and neural network pattern recognition to reduce the noise of in-service reinforced concrete columns. CE/Papers. 2025; 8(2): 854–860. doi: 10.1002/cepa.3175

[23]Mikhaylov A, Ikramov M, Nabiyeva N, et al. Multi-criteria decision-making for sound and vibration reduction platforms for financial and marketing optimization in energy. Sound & Vibration. 2026; 60(2): 3941. doi: 10.59400/sv3941

[24]Munguba CFDL, Barboza LEA, Ochoa AAAV, et al. Development of a methodology based on explainable artificial intelligence for validating pseudo-labels in the diagnosis of faults in wind turbines. e-Journal of Nondestructive Testing. 2026; 31(2). doi: 10.58286/32451

[25]Park M-H, Yeo S, Choi J-H, et al. Review of noise and vibration reduction technologies in marine machinery: Operational insights and engineering experience. Applied Ocean Research. 2024; 152: 104195. doi: 10.1016/j.apor.2024.104195

[26]Peng ZK, Chu FL. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing. 2004; 18(2): 199–221. doi: 10.1016/S0888-3270(03)00075-X

[27]Rout S, Phan K, Lin CA. Acoustics-based active control of unsteady flow dynamics using reinforcement learning-driven synthetic jets. arXiv preprint. 2023. doi: 10.48550/arXiv.2312.16376

[28]Qaid HAAM, Zhang B, Su S, et al. Large language models for explainable fault diagnosis of machines. SSRN. 2025. doi: 10.2139/ssrn.5404598

[29]Qin Y, Zhou Y, Cao C, et al. Integrating VMD and adversarial MLP for robust acoustic detection of bolt loosening in transmission towers. Electronics. 2025; 14(20): 4062. doi: 10.3390/electronics14204062

[30]Randall RB. Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons; 2011. doi: 10.1002/9780470977668

[31]Lee JW, Jeon YE, Seo JI. An integrated oversampling and noise reduction method for robust predictive analytics. Decision Analytics Journal. 2025; 16: 100612. doi: 10.1016/j.dajour.2025.100612

[32]Zhou C, Li Q, Li C, et al. A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT. arXiv preprint. 2023. doi: 10.48550/arXiv.2302.09419

[33]Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV); 22–29 October 2017; Venice, Italy. pp. 618–626. doi: 10.1109/ICCV.2017.74

[34]Syah RBY, Elveny M. An adaptive analytics framework for customer retention through integrative feature optimization and ensemble learning. Decision Analytics Journal. 2025; 16: 100626. doi: 10.1016/j.dajour.2025.100626

[35]Jayanthakumaran M, Shukla N, Pradhan B, et al. A systematic review of sentiment analytics in banking headlines. Decision Analytics Journal. 2025; 15: 100584. doi: 10.1016/j.dajour.2025.100584

[36]Latuny W, Lawalata VO, Imasuly GB, et al. A decision support framework using pruned trees for analytical quality assessment in agri-marine products. Decision Analytics Journal. 2025; 16: 100628. doi: 10.1016/j.dajour.2025.100628

[37]Zhao R, Yan R, Chen Z, et al. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing. 2019; 115: 213–237. doi: 10.1016/j.ymssp.2018.05.050

[38]Wang MC, Chang T, Mikhaylov A, et al. A measure of quantile-on-quantile connectedness for the US Treasury yield curve spread, the US dollar, and gold price. The North American Journal of Economics and Finance. 2024; 74: 102232. doi: 10.1016/j.najef.2024.102232

[39]Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?” Explaining the predictions of any classifier. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations; 12–17 June 2016; San Diego, CA, USA. pp. 97–101. doi: 10.18653/v1/N16-3020

[40]Takaloo AJ, Shoorehdeli MA, Mohammadi E. XMDCA-TL: An explainable multi-domain channel attention transfer learning framework for fault diagnosis in industrial gas turbines. arXiv preprint. 2026. doi: 10.48550/arXiv.2606.21991

[41]Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning; 6–11 August 2017; Sydney, Australia. pp. 3319–3328. Available online: https://proceedings.mlr.press/v70/sundararajan17a.html

[42]Mutalimov V, Kovaleva I, Mikhaylov A, et al. Assessing regional growth of small business in Russia. Entrepreneurial Business and Economics Review. 2021; 9(3): 119–133. doi: 10.15678/EBER.2021.090308

[43]Zhu H, Song X, Wang Y, et al. Analysis of the effect of wheel–rail high-frequency excitation on brake disc vibration of high-speed trains. Noise Control Engineering Journal. 2026; 74(2): 136–154. doi: 10.3397/1/377418

[44]Sowan B, Zhang L, Matar N, et al. A novel lift adjustment methodology for improving association rule interpretation. Decision Analytics Journal. 2025; 15: 100582. doi: 10.1016/j.dajour.2025.100582

[45]Toufighi SP, Norouzzadeh AM, Vang J, et al. An Intuitionistic fuzzy cognitive mapping approach for blockchain-driven decision support in sustainable construction supply chains. Decision Analytics Journal. 2025; 16: 100615. doi: 10.1016/j.dajour.2025.100615

[46]Alamoodi A, Albahri O, Garfan S, et al. An analytical framework for tourism application selection using neutrosophic decision techniques. Decision Analytics Journal. 2025; 17: 100638. doi: 10.1016/j.dajour.2025.100638

[47]Wang H, Chen P. Intelligent diagnosis method for rolling bearing fault using possibility theory and neural network. Computers & Industrial Engineering. 2011; 60(4): 511–518. doi: 10.1016/j.cie.2010.12.004

[48]Zhang W, Zhao Y. A new inverse design method for sound-absorbing metamaterial based on deep learning. Applied Acoustics. 2025; 241: 111024. doi: 10.1016/j.apacoust.2025.111024

[49]Errezgouny A, Chater Y, González CDB, et al. An integrated deep learning approach for predictive vehicle maintenance. Decision Analytics Journal. 2025; 16: 100597. doi: 10.1016/j.dajour.2025.100597

[50]Safarpour H, Safarpour M, Jamali J, et al. Sound radiation and phase velocity characteristics of FG bio-composite annular plates. Journal of Vibration Engineering & Technologies. 2025; 13: 366. doi: 10.1007/s42417-025-01936-0

[51]Zheng W. A shadow-based framework for label noise detection and data quality enhancement. Decision Analytics Journal. 2025; 15: 100588. doi: 10.1016/j.dajour.2025.100588

[52]Zhang J, Yao D, Peng W, et al. Optimal design of lightweight acoustic metamaterials for low-frequency noise and vibration control of high-speed train composite floor. Applied Acoustics. 2022; 199: 109041. doi: 10.1016/j.apacoust.2022.109041

[53]Si SL, You XY, Liu HC, et al. DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Mathematical Problems in Engineering. 2018; 2018(1): 3696457. doi: 10.1155/2018/3696457

[54]Vanegas-López JG, Pérez-Aguirre CA, López-Cadavid DA. An analytical framework for causal decision-making in international trade. Decision Analytics Journal. 2026; 18: 100686. doi: 10.1016/j.dajour.2026.100686

[55]Samek W, Wiegand T, Müller KR. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint. 2017. doi: 10.48550/arXiv.1708.08296

[56]Alim W, Khan NU, Zhang VW, et al. Influence of political stability on stock market returns and volatility: GARCH and EGARCH approach. Financial Innovation. 2024; 10: 139. doi: 10.1186/s40854-024-00658-8

[57]Hayati EN, Jauhari WA, Damayanti RW, et al. An integrated analytics model for supplier selection and order allocation with machine learning and multi-criteria optimization. Decision Analytics Journal. 2025; 16: 100599. doi: 10.1016/j.dajour.2025.100599

[58]Tamashiro K, Omine E, Krishnan N, et al. Optimal components capacity-based multi-objective optimization and optimal scheduling-based MPC-optimization algorithm in smart apartment buildings. Energy and Buildings. 2023; 278: 112616. doi: 10.1016/j.enbuild.2022.112616

[59]Wang J, Ma Y, Zhang L, et al. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems. 2018; 48: 144–156. doi: 10.1016/j.jmsy.2018.01.003

[60]Schröer M, Reucher E. An expert system for modeling skill levels for corporate power relations in an entropy-based environment using SPIRIT. Decision Analytics Journal. 2025; 14: 100556. doi: 10.1016/j.dajour.2025.100556

[61]Wang H, Li S, Song L, et al. A novel convolutional neural network-based fault recognition method via image fusion of multi-vibration signals. Computers in Industry. 2019; 105: 182–190. doi: 10.1016/j.compind.2018.12.013

[62]Shah FA, Zamir T, Akbar NS, et al. Levenberg-marquardt design for analysis of maxwell fluid flow on ternary hybrid nanoparticles passing over a riga plate under convective boundary conditions. Results in Engineering. 2024; 24: 103502. doi: 10.1016/j.rineng.2024.103502

[63]Crespo JDO, Domínguez JMF, Guzmán DMC, et al. An analytical study of structural equation modeling on organizational resilience and financial performance in Ecuadorian SMEs. Decision Analytics Journal. 2025; 15: 100575. doi: 10.1016/j.dajour.2025.100575

[64]Zhang Y, Chen Z, Ling Y, et al. Deep learning-based inverse design of micro-perforated plate and Fibonacci-cavity coupling structure for low-frequency broadband sound absorption. Applied Acoustics. 2026; 243: 111150. doi: 10.1016/j.apacoust.2025.111150

[65]Kluczek A, Woźniak A, Żegleń P. Uncovering economic impacts and dynamics of European energy policy: Evidence from DEMATEL, panel data, and cluster analysis. PLOS ONE. 2025; 20(11): e0322525. doi: 10.1371/journal.pone.0322525

[66]Zhu JY, Yuan YY, Hu ZW, et al. Flow-induced noise from a seal-vibrissa-shaped cylinder. Journal of Sound and Vibration. 2024; 570: 118135. doi: 10.1016/j.jsv.2023.118135

[67]Mikhaylov A, Dinçer H, Yüksel S. Analysis of financial development and open innovation-oriented fintech potential for emerging economies using an integrated decision-making approach of MF-X-DMA and golden cut bipolar q-ROFSs. Financial Innovation. 2023; 9: 4. doi: 10.1186/s40854-022-00399-6

[68]Yadav AS, Charles V, Gherman T, et al. PharmChain: A data-driven scenario-based drug traceability and regulation blockchain framework. Decision Analytics Journal. 2025; 17: 100620. doi: 10.1016/j.dajour.2025.100620

[69]Surucu O, Gadsden SA, Yawney J. Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications. 2023; 221: 119738. doi: 10.1016/j.eswa.2023.119738

[70]Rajendran B, Babu M, Anandhabalaji V. A predictive modelling approach to decoding consumer intention for adopting energy-efficient technologies in food supply chains. Decision Analytics Journal. 2025; 15: 100561. doi: 10.1016/j.dajour.2025.100561

[71]Dinçer H, Yüksel S, Mikhaylov A, et al. Analysis of renewable-friendly smart grid technologies for distributed energy investment projects using a hybrid picture fuzzy rough decision-making approach. Energy Reports. 2022; 8: 11466–11477. doi: 10.1016/j.egyr.2022.08.275

[72]Wang H, Wu Z, Wu Z, et al. Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit. Journal of Environmental Management. 2024; 364: 121451. doi: 10.1016/j.jenvman.2024.121451

[73]Keshvarinia M, MacKenzie CA, Zhao Z. A simulation-based digital twin model for data-driven decision optimization. Decision Analytics Journal. 2025; 17: 100646. doi: 10.1016/j.dajour.2025.100646

[74]Sediqi MM, Nakadomari A, Mikhaylov A, et al. Impact of time-of-use demand response program on optimal operation of Afghanistan real power system. Energies. 2022; 15(1): 296. doi: 10.3390/en15010296

[75]Juanita S, Daeli AH, Syafrullah M, et al. A machine learning approach for text pattern diagnosis in mental health consultations. Decision Analytics Journal. 2025; 15: 100572. doi: 10.1016/j.dajour.2025.100572

[76]Wang Y, He Z, Zi Y. A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis. Journal of Vibration and Acoustics. 2010; 132(2): 021010. doi: 10.1115/1.4000770

[77]Singh A, Murzello Y, Pokhrel S, et al. An investigation of supervised machine learning models for predicting drivers’ ethical decisions in autonomous vehicles. Decision Analytics Journal. 2025; 14: 100548. doi: 10.1016/j.dajour.2025.100548

[78]Yamamoto S, Furukakoi M, Uehara A, et al. MPC-based robust optimization of smart apartment building considering uncertainty for conservative reduction. Energy and Buildings. 2024; 318: 114461. doi: 10.1016/j.enbuild.2024.114461

[79]Gaona À, Guisasola A, Baeza JA. An integrated TOPSIS framework with Full-Range Weight Sensitivity Analysis for robust decision analysis. Decision Analytics Journal. 2025; 17: 100642. doi: 10.1016/j.dajour.2025.100642

[80]Touvron H, Martin L, Stone K, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint. 2023. doi: 10.48550/arXiv.2307.09288

[81]Moiseev N, Mikhaylov A, Dinçer H, et al. Market capitalization shock effects on open innovation models in e-commerce: Golden cut q-rung orthopair fuzzy multicriteria decision-making analysis. Financial Innovation. 2023; 9: 55. doi: 10.1186/s40854-023-00461-x

[82]Wong A, Garcia AV, Lim YW. A data-driven approach to customer lifetime value prediction using probability and machine learning models. Decision Analytics Journal. 2025; 16: 100601. doi: 10.1016/j.dajour.2025.100601

[83]Kabir MA, Khan SA, Gunasekaran A, et al. Multi-criteria decision making to explore the relationship between supply chain mapping and performance. Decision Analytics Journal. 2025; 15: 100577. doi: 10.1016/j.dajour.2025.100577

[84]Usamentiaga R, Venegas P, Guerediaga J, et al. Infrared thermography for temperature measurement and non-destructive testing. Sensors. 2014; 14(7): 12305–12348. doi: 10.3390/s140712305

[85]Jargalsaikhan N, Ueda S, Masahiro F, et al. Exploring the influence of air density deviation on the power production of a wind energy conversion system: Study on a correction method. Renewable Energy. 2024; 220: 119636. doi: 10.1016/j.renene.2023.119636

[86]Velandia-Cárdenas C, Vidal Y, Pozo F. Enhancing wind turbine diagnostics with SCADA-vibration fusion, contrastive learning, and linear predictive coefficients. Structural Control and Health Monitoring. 2026; 2026(1): 4023580. doi: 10.1155/stc/4023580

[87]Irshad AS, Elkholy MH, Alshammari NF, et al. Novel approach for energy balancing with intermittent renewable energy source using multi-objective genetic algorithm. IEEE Access. 2024; 12: 179318–179329. doi: 10.1109/ACCESS.2024.3507216

[88]Shaikh ZA, Kraikin A, Mikhaylov A, et al. Forecasting stock prices of companies producing solar panels using machine learning methods. Complexity. 2022; 2022(1): 9186265. doi: 10.1155/2022/9186265

[89]Tang J, Niu H. Physics-informed neural network with pretraining optimization for ocean acoustic field prediction. The Journal of the Acoustical Society of America. 2025; 158(5): 3846–3860. doi: 10.1121/10.0039871

[90]Rashwan A, Mikhaylov A, Senjyu T, et al. Modified droop control for microgrid power-sharing stability improvement. Sustainability. 2023; 15(14): 11220. doi: 10.3390/su151411220

[91]Zhao F, Xing M, Liu J, et al. Study on the mechanical properties of convex-stud array mesh pads. Noise Control Engineering Journal. 2026; 74(2): 155–168. doi: 10.3397/1/377422

[92]Zhao WX, Zhou K, Li J, et al. A survey of large language models. arXiv preprint. 2023. doi: 10.48550/arXiv.2303.18223

[93]Huang Y, Yona A, Takahashi H, et al. Energy management system optimization of drug store electric vehicles charging station operation. Sustainability. 2021; 13(11): 6163. doi: 10.3390/su13116163

[94]Zhang W, Peng G, Li C, et al. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors. 2017; 17(2): 425. doi: 10.3390/s17020425

[95]Yumashev A, Mikhaylov A. Development of polymer film coatings with high adhesion to steel alloys and high wear resistance. Polymer Composites. 2020; 41(7): 2875–2880. doi: 10.1002/pc.25583

[96]Shao B, Wu TC, Yan ZX, et al. Deep learning-empowered triboelectric acoustic textile for voice perception and intuitive generative AI-voice access on clothing. Science Advances. 2025; 11(41): eadx3348. doi: 10.1126/sciadv.adx3348

[97]Zou MS, Tang HC, Yang YN, et al. Numerical-analytical hybrid calculation method for acoustic radiation of underwater vehicles with internal vibration isolation system. Journal of Sound and Vibration. 2025; 615: 119213. doi: 10.1016/j.jsv.2025.119213

[98]Dong P, Li W, Cao J, et al. 4D-printed cementitious metamaterials with tunable vibration performance: Integrating programmable thermomechanics and forward prediction. Construction and Building Materials. 2026; 530: 146518. doi: 10.1016/j.conbuildmat.2026.146518

[99]Machado MR, Chen DT, Osterrieder JR. An analytical approach to credit risk assessment using machine learning models. Decision Analytics Journal. 2025; 16: 100605. doi: 10.1016/j.dajour.2025.100605

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