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Abstract

A cavitation state identification method driven by vibration data of fluid machinery is disclosed in the present invention, and belongs to the field of big data learning models. According to the present invention, an adaptive neural network is trained by means of a cavitation data set to form a cavitation state identification model, such that vibration signal sequences can be collected online by all vibration sensors arranged at different positions of a target centrifugal pump, the collected vibration signal sequences are input into the cavitation state identification model obtained by training, and a current real-time cavitation intensity of the target centrifugal pump is predicted online. Moreover, the cavitation intensity predicted in the present invention can use a more detailed quantitative label, such that fine-grained prediction about a cavitation development degree is achieved.

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First Claim