Abstract
The fault propagation process of rotating machinery is affected by multi-physical field coupling and multi-level causality, and traditional methods have limitations in data fusion and causal inference, which make it difficult to effectively analyze the dynamic mechanism of fault chain propagation. In this paper, we propose a fault propagation analysis method for rotating machinery based on the fusion of multidimensional data and causal chain explanatory structural model, aiming to realize the dynamic modeling of fault paths and causal explanatory analysis. Through CCISM, a multi-level causal chain network model is constructed to analyze the explicit causal logic and implicit correlation paths of fault propagation, and the research results provide a theoretical basis for optimizing the reliability of rotating machinery systems and formulating active maintenance strategies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mkedder, N., Das, M.: Metaverse integration challenges: an in-depth ISM and MICMAC analysis. J. Retail. Consum. Serv. 77, 103684 (2024)
Bagherian, A., Gershon, M., Kumar, S., et al.: Analyzing the relationship between digitalization and energy sustainability: a comprehensive ISM-MICMAC and DEMATEL approach. Expert Syst. Appl. 236, 121193 (2024)
Aba, A., Esztergár-Kiss, D.: Creation of the MaaS readiness index with a modified AHP-ISM method. Commun. Transp. Res. 4, 100122 (2024)
Singh, D.: Internet of Things. Factories of the Future: Technological Advancements in the Manufacturing Industry, 195–227 (2023)
Acciarini, C., Cappa, F., Boccardelli, P., et al.: How can organizations leverage big data to innovate their business models? A systematic literature review. Technovation 123, 102713 (2023)
Arpaci, I., Masrek, M.N., Al-Sharafi, M.A., et al.: Evaluating the actual use of cloud computing in higher education through information management factors: a cross-cultural comparison. Educ. Inf. Technol., 1–21 (2023)
Kumar, R., Goel, P.: Exploring the domain of interpretive structural modelling (ISM) for sustainable future panorama: a bibliometric and content analysis. Arch. Comput. Methods Eng. 29(5), 2781–2810 (2022)
Wang, H., Fu, T., Du, Y., et al.: Scientific discovery in the age of artificial intelligence. Nature 620(7972), 47–60 (2023)
Chen, J., Xia, C., Perc, M.: The SIQRS propagation model with quarantine on simplicial complexes. IEEE Trans. Comput. Soc. Syst. 11(3), 4267–4278 (2024)
Chakraborty, A., Veeresha, P.: Effects of global warming, time delay and chaos control on the dynamics of a chaotic atmospheric propagation model within the frame of caputo fractional operator. Commun. Nonlinear Sci. Numer. Simul. 128, 107657 (2024)
Liu, Y., Sarris, C.D.: Efficient computation of scattered fields from reconfigurable intelligent surfaces for propagation modeling. IEEE Trans. Antennas Propag. 72(2), 1817–1826 (2024)
Wu, H., Yan, X., Gao, S., Deng, Z., Chi, H.: Analysis and control of rumor propagation model considering multiple waiting phases. Mathematics 13(2), 312 (2025)
He, H., Wang, Q., Wang, L., Ji, B.: Predicting the fatigue life of double-sided rib-to-deck welds: a single-crack propagation model and parametric investigations. In: Structures, vol. 74, p. 108624. Elsevier, April 2025
Huang, J., Morris, J.S.: Infectious disease modeling. Ann. Rev. Stat. Appl. 12 (2024)
Kibrete, F., Woldemichael, D.E., Gebremedhen, H.S.: Multi-sensor data fusion in intelligent fault diagnosis of rotating machines: a comprehensive review. Measurement, 114658 (2024)
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 62476085, the Key Research and Development Program of Hunan Province under Grant 2024JK2065, and the Natural Science Foundation of Hunan Province under Grant 2023JJ50200, and in part by the Hunan Province Graduate Research Innovation Project under Grant CX20240917.
Author information
Authors and Affiliations
Contributions
Competing Interests.
The authors declare no competing interests.
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, M., Peng, C., Wang, J., He, J., Deng, C., Zhang, R. (2025). A Research Method for Analyzing Fault Propagation of Rotating Machinery Based on Multidimensional Data Fusion and Causal Chain Explanatory Structural Modeling. In: Huang, DS., Chen, W., Pan, Y., Chen, H. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2025. Lecture Notes in Computer Science, vol 15857. Springer, Singapore. https://doi.org/10.1007/978-981-96-9921-6_21
Download citation
DOI: https://doi.org/10.1007/978-981-96-9921-6_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-9920-9
Online ISBN: 978-981-96-9921-6
eBook Packages: Computer ScienceComputer Science (R0)