Rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis

Abstract

The invention provides a rotating machine fault diagnosis method based on Fast ICA-spectrum kurtosis-envelope spectrum analysis. The method includes the steps that (1), an acceleration sensor is used for obtaining vibration acceleration test signals of a rotating machine; (2), the test signals are separated in a decoupling mode by the adoption of a Fast ICA method with negentropy maximized; (3), the spectrum kurtosis of the separated signals is calculated, and the separated signals capable of representing fault information best are screened out; (4), Hilbert envelope spectrum analysis is performed on the selected separated signals; (5), the frequency corresponding to the peak value of an envelope spectrum is compared with the fault character frequency of a bearing, so that a concrete fault is diagnosed. The effectiveness of the method is verified well by the inner ring fault diagnosis of a rolling bearing of the rotating machine, the fault information is obviously increased, the precision of the fault diagnosis is greatly improved, the fault diagnosis method is easy to achieve and good in real-time performance, and it is illustrated that the rotating machine fault diagnosis method has good application prospects.

Description

technical field [0001] The invention relates to a method for diagnosing a fault of a rotating machine, in particular to a method for diagnosing a fault of a rotating machine based on FastICA-spectral kurtosis-envelope spectrum analysis. Background technique [0002] Rotating machinery is a very important mechanical equipment in the field of production. Due to the many excitation sources and complex nature of rotating machinery vibration. At present, for the fault diagnosis of rotating machinery, most of them need to be unpacked or disassembled, which will inevitably cause downtime and affect operating efficiency. On the other hand, when the rotating machinery is running, due to the fluctuation of its speed, the change of the load or the failure of the equipment, the signal at the measuring point is often obtained by mixing multiple vibration sources through a certain path. In addition, there may be other components operating outside, which will also interfere with the usefu...

AI Technical Summary

Problems solved by technology

[0003] In this case, FastICA can effectively separate a variety of vibration source signals, but after the test signal is separated by FastICA, there are separated signals that cannot effectively distinguish the most representative fault information. Currently, the frequency spectrum of the separated signal of the fault test signal and the normal Compare the frequency spectrum of the separation signal of the running test signal, and then select the separation signal representing the fault information based on experience, but it will lead to wrong judgments

Secondly, for a complex rotating mechanical system, when a fault occurs, the number of modulation sidebands increases and the amplitude increases, which makes it difficult to extract fault information and frequency spectrum analysis, and the accuracy is not high enough, resulting in only obtaining other information from the signal. Time-frequency domain characteristics, and then achieve the purpose of diagnosis, it is almost impossible

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