CN102788695B - Identification method of rolling bearing abrasion - Google Patents

Abstract

The invention discloses a method for identifying rolling bearing wear, which comprises the following steps: (1) calculating the characteristic frequency of the intermediate bearing in different working stages; (2) performing short-time Fourier transform on the bearing vibration test signal to obtain the Fourier transform Spectrum diagram; (3) Set the frequency band, and use the integral method to obtain the energy of this frequency band as the calculated instantaneous energy of the bearing characteristic frequency; (4) Calculate the actual instantaneous energy and the instantaneous energy under normal working conditions for a period of time; (5) Use The instantaneous energy under normal working conditions is the baseline, and the threshold is set to compare and judge the wear trend and the degree of bearing damage. This identification method can judge the wear of rolling bearings according to the magnitude of instantaneous energy, and has the characteristics of online real-time performance and the ability to measure the degree of wear.

Description

An identification method for rolling bearing wear

technical field

The invention belongs to the research field of mechanical fault monitoring and diagnosis methods, and in particular relates to a method for identifying early wear states of bearings based on an instantaneous energy method.

Background technique

The outer ring of the rolling bearing is placed in the bearing seat hole, and the inner ring is connected with the transmission shaft and rotates with the shaft. If the rolling bearing itself is faulty, when the shaft runs at a certain speed and under a certain load, it will excite the vibration system composed of the bearing and the bearing seat or the shell, and generate vibration. This vibration includes a comprehensive excitation system formed by external excitation and internal excitation caused by bearing internal structure, installation, and assembly errors. In order to effectively separate the bearing operation fault from the vibration signal, it is necessary to firstly overcome the extraction of the weak fault characteristic information of the bearing under the large noise background, secondly, the characteristic frequency change and the corresponding bandwidth of the bearing under different speed conditions, and finally study the method of effectively extracting fault information .

The surface wear of high-speed and heavy-duty rolling bearings gradually intensifies with the length of working time, which is a gradual fault. The vibration generated after the bearing surface wears has a corresponding characteristic frequency, and the vibration level (amplitude) after wear is obviously higher than that of normal bearings. Therefore, the aggravation and overrun of vibration can also preliminarily judge the operating condition of the bearing. The characteristic frequency of bearing failure is generally below low frequency (1kHz), but the high-frequency natural vibration components of the bearing system will be induced by the impact of surface damage, such as the radial bending natural vibration of the outer ring of the bearing.

The instantaneous eigenfrequency, harmonics and background noise based on the analysis of eigenfrequency and its harmonics constitute the general characteristics of rolling element wear. Therefore, it is recommended to use the harmonic spectrum energy method in the instantaneous characteristic area to monitor the occurrence, development and trend of wear.

The magnitude of the background noise of wear is related to the speed, and also represents the occurrence and development of wear. The source of background noise is the vibration noise generated by the friction of objects rubbing against each other and the increase in the noise level of the entire system due to the increase in the gap after wear, which brings a wide range of harmonic components, which we call sub-bands, so we can calculate the wear corresponding to The wear process can be judged by the noise level in the sub-band range where the characteristic frequency is located.

High-speed and heavy-duty bearings, such as the intermediate bearings of aero-engines, are the supporting components between the high-pressure rotor and the low-pressure rotor. If the intermediate bearing fails, the vibration of the system will increase, and the rotor will lock up and the engine will stop in the air. The working principle of the intermediate bearing is to rely on the drag force generated between the active ring and the roller to drive the rolling body and the cage components to rotate to achieve rotation.

During the wear process of the bearing, the bearing fault frequency and its harmonics will appear successively on the frequency spectrum. As the wear intensifies, side frequencies will appear at the bearing fault frequency and its harmonics. When the bearing wear occurs and develops, the unique frequency will be replaced by noise. Side lobes will be the only sign of these faults (at low speeds). The frequency band composed of side frequency and fault frequency forms a peak-like shape, commonly known as "haystack". "Haystack effect" means that the haystack will gradually increase with the deterioration of the failure, so the energy of the "haystack" can be used to measure the degree of failure. The present invention produces based on this thinking promptly.

Contents of the invention

The purpose of the present invention is to provide a method for identifying the wear of rolling bearings, which can judge the wear of rolling bearings according to the magnitude of instantaneous energy, and has the characteristics of online real-time performance and the ability to measure the degree of wear.

In order to achieve the above object, the solution of the present invention is:

A method for identifying rolling bearing wear, comprising the steps of:

(1) Calculate the characteristic frequency of the bearing at different working stages;

(2) Short-time Fourier transform is performed on the bearing vibration test signal to obtain a Fourier transform spectrum diagram;

(3) Set the frequency band, and use the integral method to obtain the energy of this frequency band as the calculated instantaneous energy of the characteristic frequency of the bearing;

(4) Calculate the actual instantaneous energy and the instantaneous energy under normal working conditions for a period of time respectively;

(5) Set the threshold value based on the instantaneous energy in the normal working condition, and compare and judge the wear trend and the degree of bearing damage.

In the above step (1), the characteristic frequency includes the following parameters:

Rolling element rotation frequency:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\frac{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Frequency of rolling elements passing through the inner ring:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; bi</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Frequency of rolling elements passing the outer ring:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; bo</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Cage frequency or rolling element revolution frequency:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; bo</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Among them, f is the rotation frequency of the rotating shaft, D _m is the middle diameter of the bearing, d is the diameter of the rolling elements, Z is the number of rolling elements, and α is the contact angle.

In the above step (3), the frequency segment is divided, and with the occurrence of wear and tear, harmonic components appear near the characteristic frequency:

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; \&NotElement;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>\end{array}\right.$

In the formula, ω _min and ω _max are the lower limit and upper limit of the signal analysis frequency range respectively, and the upper and lower limits of the frequency refer to the appropriate width around the characteristic frequency, centered on the characteristic frequency and excluding other characteristic frequencies.

In the above step (4), the Fourier transform STFT _H of the band-selective filtered component is integrated in the frequency domain to obtain the instantaneous energy:

${\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; STFT</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d\&omega;</span>}$

Among them, E _t is the instantaneous energy related to the characteristic frequency of the signal, and ω _min and ω _max are the lower limit and upper limit of the signal analysis frequency range respectively.

In the above step (5), the method for setting the threshold is: the average value of the instantaneous energy under normal working conditions is obtained by adding its 2 times the variance.

After adopting the above scheme, the present invention is based on the characteristic that the corresponding harmonic components will appear in the wear process near the characteristic frequency. In the early wear and tear, only the harmonic component has no characteristic frequency, and the short-time Fourier transform is used to calculate different working conditions and bearings in real time. The characteristic frequency of different parts is calculated by instantaneous energy at the same time to obtain the threshold value for measuring the degree of wear, and to distinguish the early wear and development trend of the bearing. It has the characteristics of online real-time and can measure the degree of wear.

Description of drawings

Fig. 1 is a flow chart of the present invention;

Fig. 2 is a comparison diagram of instantaneous energy in the vicinity of a characteristic frequency of a certain working condition in the present invention.

Detailed ways

The technical solutions and beneficial effects of the present invention will be described in detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the present invention provides a method for identifying rolling bearing wear, comprising the following steps:

(1) Calculate the characteristic frequency of the bearing at different working stages;

Let the rotational frequency of the rotating shaft be f, the middle diameter of the bearing be D _m , the diameter of rolling elements be d, the number of rolling elements be Z, and the contact angle α.

Rolling element rotation frequency:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}\frac{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Frequency of rolling elements passing through the inner ring:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; bi</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Frequency of rolling elements passing the outer ring:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; bo</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; Z</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

Cage frequency or rolling element revolution frequency:

${\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; bo</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\frac{\mathrm{<span\; class="notranslate">\; d</span>}\mathrm{<span\; class="notranslate">\; cos</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}{{\mathrm{<span\; class="notranslate">\; D.</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; f</span>}$

(2) Perform short-time Fourier transform on the bearing vibration test signal at the same time, monitor whether the characteristic frequency in step (1) appears and its size through the Fourier transform spectrogram, and determine the characteristic frequency and its scope;

Definition of short-time Fourier transform: Given a window function r(t) with a short time width and sliding the window, the STFT of the signal s(t) is defined as:

$\mathrm{<span\; class="notranslate">\; STFT</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{<span\; class="notranslate">\; -</span><span\; class="notranslate">\; \&infin;</span>}^{<span\; class="notranslate">\; \&infin;</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; r</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ]</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&pi;ft</span>}}\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

The STFT of the signal s(t) at time τ is the Fourier transform of the signal s(t) multiplied by an "analysis window" r ^* (t-τ) centered on τ, which is equivalent to taking out the signal at t=τ nearby local spectrum. After bandpass filtering

STFT _H = (STFT(τ,f))H(ω) (2)

(3) Set the frequency band, and use the integral method to obtain the energy of this frequency band as the calculated instantaneous energy of the characteristic frequency of the bearing;

Divide the frequency segment, and with the occurrence of wear and tear, harmonic components appear near the characteristic frequency

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; \&NotElement;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>\\ \mathrm{<span\; class="notranslate">\; 1</span>}& \mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

In the formula, ω _min and ω _max are the lower limit and upper limit of the signal analysis frequency range respectively. The upper and lower limits of the frequency refer to the characteristic frequency near the characteristic frequency, centering on the characteristic frequency and not including other characteristic frequencies (such as the frequency) should be determined according to the monitored spectrogram and different units.

(4) Calculate the actual instantaneous energy within a certain period of time and the instantaneous energy under normal working conditions;

The Fourier transform STFT _H of the band-selective filtered components is integrated in the frequency domain to obtain the instantaneous energy.

${\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}^{{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; STFT</span>}}_{\mathrm{<span\; class="notranslate">\; h</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; d\&omega;</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

_Et is the instantaneous energy of the signal relative to the characteristic frequency. E _t reflects the instantaneous totality of the time-frequency distribution of the signal at time t, and can also be regarded as the energy of the components whose frequency is within the range of [ω _min ,ω _max ] in the τ period.

(5) Taking the instantaneous energy in normal working conditions as the baseline, set the threshold, which can be the average value of the instantaneous energy in normal working conditions plus its 2 times the variance. If the threshold is exceeded, wear is judged, and the trend of wear and tear is compared and judged. Extent of bearing damage.

As shown in Fig. 2, the energy comparison chart of 55-90Hz under the MC working condition of the present invention on August 12, 14, 15, 17, and 18 is shown, wherein the number of short-time Fourier transform points is 4096, and the frequency band is set as: 55-90Hz (the fault frequency of the bearing under MC working condition is 68Hz). It can be seen from the figure that the wear of the rolling body of the bearing is reflected in the increase of instantaneous energy with the increase of time. After August 14th, it can be judged that the bearing is damaged due to wear , due to the sharp increase in the instantaneous vibration energy.

The above embodiments are only to illustrate the technical ideas of the present invention, and can not limit the protection scope of the present invention with this. All technical ideas proposed in accordance with the present invention, any changes made on the basis of technical solutions, all fall within the protection scope of the present invention. Inside.

Claims (3)

1. a recognition methods for rolling bearing wearing and tearing, is characterized in that comprising the steps:

(1) calculation bearing is in the characteristic frequency in different operating stage;

(2) bear vibration test signal is carried out to Short Time Fourier Transform, obtain Fourier transform spectrogram;

(3) frequency range is set, uses integral method to ask for this band energy as the calculating instantaneous energy of bearing characteristic frequency;

Specifically, dividing frequency band,, there is harmonic components in the appearance along with wearing and tearing near this characteristic frequency:

$H\left(\mathrm{\&omega;}\right)=\left\{\begin{array}{cc}0& \mathrm{\&omega;}\&NotElement;({\mathrm{\&omega;}}_{\min },{\mathrm{\&omega;}}_{\max })\\ 1& \mathrm{\&omega;}\&Element;({\mathrm{\&omega;}}_{\min },{\mathrm{\&omega;}}_{\max })\end{array}\right.$

In formula, ω _min, ω _maxbe respectively lower limit and the upper limit of signal analysis frequency range, the upper and lower limit of described frequency refers near characteristic frequency, centered by characteristic frequency and do not comprise the suitable width of other characteristic frequencies;

(4) calculate respectively the instantaneous energy under interior actual instantaneous energy of a period of time and nominal situation;

Specifically, the Fourier transform STFT with composition after filtering to choosing _hin Frequency Domain Integration, obtain instantaneous energy:

$E_t={\&Integral;}_{{\mathrm{\&omega;}}_{\min }}^{{\mathrm{\&omega;}}_{\max }}\left({\mathrm{STFT}}_H\right)\mathrm{d\&omega;}$

Wherein, E _tbe the instantaneous energy that signal is relevant to characteristic frequency, ω _min, ω _maxbe respectively lower limit and the upper limit of signal analysis frequency range;

(5) take the instantaneous energy size at nominal situation place is baseline, setting threshold, the relatively trend of judgement wearing and tearing and the degree of bearing damage.

2. the recognition methods of a kind of rolling bearing wearing and tearing as claimed in claim 1, is characterized in that: in described step (1), described characteristic frequency comprises following parameter:

Rolling body rotation frequency:

$f_b=\frac{1}{2}\frac{D_m}{d}[1-{\left(\frac{d\cos \mathrm{\&alpha;}}{D_m}\right)}^2]\&times;f$

Rolling body passes through inner ring frequency:

$f_{\mathrm{bi}}=\frac{1}{2}[(1+\frac{d\cos \mathrm{\&alpha;}}{D_m})\&times;Z]\&times;f$

Rolling body is by the frequency of outer ring:

$f_{\mathrm{bo}}=\frac{1}{2}[(1-\frac{d\cos \mathrm{\&alpha;}}{D_m})\&times;Z]\&times;f$

Retainer frequency or rolling body revolution frequency:

$f_c=\frac{1}{2}(1-\frac{d\cos \mathrm{\&alpha;}}{D_m})\&times;f$

Wherein, the gyro frequency that f is rotating shaft, D _mfor the central diameter of bearing, d is rolling body diameter, and Z is rolling body number, and α is contact angle.

3. the recognition methods of a kind of rolling bearing wearing and tearing as claimed in claim 1, is characterized in that: in described step (5), the establishing method of described threshold value is: the mean value of instantaneous energy size under nominal situation is added to its 2 times of variances obtain.