Pump potential cavitation fault detecting method based on quick spectrum kurtosis analysis

Abstract

The invention discloses a pump potential cavitation fault detecting method based on quick spectrum kurtosis analysis. The pump potential cavitation fault detecting method comprises the steps that 1, vibration acceleration signals are collected to be subject to noise reduction to serve as to-be-processed signals; 2, according to the data size of the signals, the resolving order of signal processingis determined; 3, according to the quick spectrum kurtosis algorithm computation result, the optimal carrier frequency and bandwidth are selected; 4, the selected signals within the carrier frequencyand bandwidth are subject to Fourier transformation, and a frequency spectrum envelope diagram is obtained; and 5, an original signal time domain figure, a signal time domain figure treated through quick spectrum kurtosis filtering processing and the frequency spectrum envelope diagram obtained after selected zone Fourier transformation are compared, and cavitation trouble signal time and frequency characteristics are analyzed. By means of the method, more cavitation instant signals can be detected, information in the aspects of time domain and frequency domain is seen more clearly, and the normal state and the cavitation state of a pump can be distinguished obviously.

Description

technical field [0001] The invention belongs to the field of signal processing, in particular to a method for analyzing the real-time state of a pump and detecting its potential cavitation fault based on fast spectrum kurtosis. Background technique [0002] High-performance centrifugal pumps are widely used and in great demand in today's society. Due to working under complex conditions such as high pressure and high speed, cavitation failures of centrifugal pumps occur frequently, resulting in increased vibration frequency, increased noise, and blade corrosion, which seriously restricts pump performance and life. The traditional detection method is not sensitive to the detection of signal changes such as pump flow, head, vibration and noise in the initial stage of pump cavitation; but when the cavitation signal changes significantly, the cavitation fault has rapidly developed to a quite serious level. [0003] The acoustic signal of cavitation bubbles has a large bandwidth ...

AI Technical Summary

Problems solved by technology

However, there are still deficiencies in the selection of wavelet bases that are not unique and the combination of wavelet parameters is not robust

At the same time, there is also a combination of support vector machine and artificial neural network method to improve wavelet decomposition transformation, using pressure fluctuation signal and cavitation field distribution image, it is very effective for transient cavitation feature extraction, but the algorithm complexity is high and the parameter setting is still need experience interpretation

In addition, the cavitation diagnosis method based on multi-dimensional analysis (impeller-guide vane-load-acoustic signal spectrum) is complementary to multi-scale analysis such as wavelet transform, and has successfully developed a high-sensitivity and high-reliability hydraulic turbine cavitation monitoring and diagnosis system, but this multi-dimensional analysis method fails to obtain high-resolution dynamic spectrum texture, which is not conducive to characterizing the key transition process from sheet to cloud cavitation

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