A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909961916776448 |
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| author | Sha, Yu Liu, Ningtao Liu, Haofeng Tao, Junqi Niu, Zhenxing Huang, Guojun Yao, Yao Liang, Jiaqi Qian, Moxian Stoecker, Horst Vnucec, Domagoj Widl, Andreas Zhou, Kai |
| author_facet | Sha, Yu Liu, Ningtao Liu, Haofeng Tao, Junqi Niu, Zhenxing Huang, Guojun Yao, Yao Liang, Jiaqi Qian, Moxian Stoecker, Horst Vnucec, Domagoj Widl, Andreas Zhou, Kai |
| contents | Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_15497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems Sha, Yu Liu, Ningtao Liu, Haofeng Tao, Junqi Niu, Zhenxing Huang, Guojun Yao, Yao Liang, Jiaqi Qian, Moxian Stoecker, Horst Vnucec, Domagoj Widl, Andreas Zhou, Kai Signal Processing 68T05, 76F70, 93C95 I.5.4; I.2.6; J.2 Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems. |
| title | A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems |
| topic | Signal Processing 68T05, 76F70, 93C95 I.5.4; I.2.6; J.2 |
| url | https://arxiv.org/abs/2511.15497 |