A Review of Machine Learning for Cavitation Intensity Recognition in Complex Industrial Systems

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.

💡 Research Summary

This review paper provides a comprehensive and systematic analysis of the technological evolution of Cavitation Intensity Recognition (CIR) in complex industrial systems, spanning a significant period from 2002 to 2025. Cavitation, a phenomenon characterized by the formation and collapse of vapor bubbles in hydraulic machinery, poses severe threats to operational safety, mechanical integrity, and economic efficiency. Therefore, advancing CIR technology is paramount for predictive maintenance and performance optimization in large-scale industrial infrastructures.

The paper categorizes the historical trajectory of CIR into three distinct technological eras. The first era, dominated by traditional machine learning, relied heavily on manual feature engineering. During this period, domain experts were required to extract specific time-domain and frequency-domain features from raw signals, a process that was labor-intensive and limited by the complexity of real-world industrial environments.

The second era marks the revolution brought by deep learning. The transition to end-to-end learning architectures enabled the automatic extraction of intricate features from multi-source signals, such as vibration and acoustic emissions. This advancement significantly enhanced the robustness and recognition accuracy of models, allowing them to handle the high-dimensional and non-linear nature of cavitation signals more effectively than their predecessors.

The third and current era focuses on the emergence of physics-informed diagnostic models. Recognizing the “black-box” nature and data-dependency of pure deep learning, researchers have begun integrating physical laws and domain-specific knowledge into neural network architectures. By embedding fluid dynamics principles into the learning process, these models achieve superior interpretability and better generalization capabilities, especially when dealing with unseen operating conditions or limited training datasets.

Looking toward the future, the paper identifies several critical frontiers that will define the next generation of CIR technology. Key research directions include:

1. Transfer Learning: Leveraging pre-trained models to overcome the scarcity of labeled data in specific industrial applications.
2. Multi-modal Fusion: Integrating diverse sensor inputs to create a holistic understanding of the cavitation state.
3. Lightweight Network Architectures: Developing efficient models capable of real-time deployment on edge computing devices within industrial environments.
4. Deployment of Industrial Agents: Moving toward autonomous, intelligent agents capable of self-monitoring and decision-making.

In conclusion, this paper serves as a strategic roadmap for researchers and practitioners, highlighting the shift from purely data-driven approaches to a sophisticated synergy between deep learning and physical domain knowledge, ultimately aiming for the realization of fully autonomous intelligent industrial monitoring systems.