Method for extracting fault features of rotating mechanical equipment

Abstract

The invention provides a method for extracting the fault features of rotating mechanical equipment, which comprises the following steps: (1) carrying out equal-time-interval sampling on a vibration signal and carrying out time domain processing, and extracting time domain features; (2) carrying out envelope detection processing on an equal-time-interval sampling vibration signal x(n) so as to obtain an equal-time-interval sampling envelope signal, and extracting the features of the envelope signal; (3) reconstituting an equal-angle-interval sampling envelope signal y(n) by using the equal-time-interval sampling envelope signal and rotating speed information; (4) carrying out equal-angle moving average filter processing on the equal-angle-interval sampling envelope signal y(n) so as to obtain a signal z (n); and (5) solving the Fourier transform of the signal z (n), seeking feature frequencies by using a feature frequency matching method, and extracting the fault features of a frequency domain. The method not only can suppress interferences under the condition that the resolution ratio of a frequency domain is not reduced, but also can extract actual feature frequencies under the condition that rotating speeds and mechanical parts have deviations, and therefore, smaller potential faults can be effectively found.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of mechanical equipment, in particular to a method for extracting fault features of rotating mechanical equipment. Background technique [0002] With the development of modern industry and science and technology and the further improvement of automation, machinery is developing in the direction of large-scale, high-speed, continuous, centralized, and automated. Rotating mechanical equipment has been in high-speed operation for a long time (generally more than 3000 revolutions per minute or even as high as tens of thousands of revolutions). Due to the influence of various factors, some failures will inevitably occur, and these failures often cause huge economic losses or even catastrophic consequences. , so it is very important for fault diagnosis of rotating machinery equipment. [0003] Vibration detection and diagnosis method is the most commonly used diagnosis method in fault diagnosis...

AI Technical Summary

Problems solved by technology

However, since this method uses the method of segmenting the data and then adding and averaging different segments of data to suppress noise, the continuity of the data is destroyed, and continuous monitoring of the state cannot be realized; in addition, because the long data is divided into short data , this method also reduces the frequency-domain resolution of the subsequent Fourier transform

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