Method, device and equipment for diagnosing fault of centrifugal compressor

By constructing a fault feature sample library using the time-frequency analysis method of linear transformation and the target particle swarm algorithm, the problem of training fault diagnosis models for centrifugal compressors in existing technologies is solved. This enables rapid and accurate fault type identification, reduces the professional requirements, and is suitable for large-scale applications.

CN117307512BActive Publication Date: 2026-05-19WANHUA CHEM GRP CO LTD
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Patent Information

Application Number
CN202210680416.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-16
Publication Date
2026-05-19
Estimated Expiration
2042-06-16

AI Technical Summary

Technical Problem

Existing centrifugal compressor fault diagnosis technology models are difficult to train accurately and have a long training cycle, resulting in insufficient accuracy of analysis results. Furthermore, they are highly specialized and not suitable for large-scale applications.

Method used

The fault characteristic parameters of vibration signals are extracted by time-frequency analysis using linear transformation, and a fault characteristic sample library is constructed by using the target particle swarm algorithm and a preset clustering algorithm. The fault type is determined by analogy analysis, thus avoiding the model training process.

Benefits of technology

It enables rapid and accurate fault type identification, reduces the requirements for expertise, and is suitable for large-scale applications.

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Abstract

Embodiments of the present disclosure provide a centrifugal compressor fault diagnosis method, device and equipment. The method comprises: acquiring a current vibration signal of a centrifugal compressor to be diagnosed; extracting a current fault characteristic parameter of the current vibration signal based on a linear transformation time-frequency analysis method; acquiring a pre-stored fault characteristic sample library; and performing analogy analysis on the current fault characteristic parameter and the fault characteristic sample library to determine a fault type of the centrifugal compressor. In this way, the current fault characteristic parameter and the fault characteristic sample library can be automatically subjected to analogy analysis, so as to accurately and quickly locate the fault type of the centrifugal compressor. Since this fault analysis method does not require model training, the fault analysis result is not affected by the model precision, and the algorithm is simple to use, so the identification accuracy of the fault type of the centrifugal compressor can be ensured.
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