Diagnosing cavitation in industrial pipelines is difficult because acoustic signals shift with operating conditions, and visually similar time-frequency patterns can represent different cavitation states. This ambiguity makes standard pattern-recognition approaches unreliable in real-world settings.
To address this, the paper introduces a physical operating-condition-guided vision-language matching method. By explicitly integrating operating-condition information into the matching process, the model can better align acoustic features with the correct cavitation state, reducing confusion caused by condition-dependent variability.
The work highlights the value of combining physical knowledge with modern vision-language frameworks for industrial monitoring. While the abstract does not provide quantitative results, the proposed direction suggests a practical path toward more robust cavitation diagnosis under changing operating environments.