CN113702046A - Bearing fault diagnosis method based on mobile equipment under variable rotating speed working condition - Google Patents

Abstract

The invention relates to a bearing fault diagnosis method under the variable rotating speed working condition based on mobile equipment, which comprises the steps of sending a carrier wave through a loudspeaker of the mobile equipment, demodulating the amplitude of a sound signal collected by a microphone, searching and restoring an instantaneous phase signal of a rotating shaft where a bearing is located through a maximum value and fitting a least square polynomial, extracting a fault impact envelope through a spectral kurtosis method to carry out order tracking, and finally analyzing and judging the fault of the bearing through an envelope order spectrum.

Description

Bearing fault diagnosis method based on mobile equipment under variable rotating speed working condition

Technical Field

The invention relates to the technical field of mechanical fault diagnosis and signal processing analysis, in particular to a bearing fault diagnosis method under a variable-rotating-speed working condition based on mobile equipment.

Background

The bearing is a widely used mechanical part, and the health monitoring of the bearing is very important for the safe and stable operation of rotary mechanical equipment. Vibration and noise analysis is a common means for bearing fault diagnosis, generally needs a professional data acquisition card and sensor hardware, and is too expensive for non-key equipment objects. In addition, a large number of bearings work under the working condition of variable rotating speed, and the bearing fault diagnosis under the working condition of variable rotating speed also depends on speed measuring hardware such as a photoelectric probe and an encoder, so that the test cost is increased.

In the document [1], an author acquires radiation noise of a bearing through a built-in microphone of a mobile phone and diagnoses bearing faults through analysis and processing of acoustic signals, the technology is favorable for popularization and application of a bearing fault diagnosis technology to a certain extent, but the method is only suitable for a constant rotating speed working condition and needs to know rotating speed parameters of the bearing in advance.

The patent with patent application number 201910416881.8 realizes the measurement of the rotating speed of the rotating shaft by using the mobile device, and the invention innovatively utilizes the built-in loudspeaker of the mobile device to actively sound, and simultaneously performs signal processing on the sound signal collected by the built-in microphone to realize the estimation of the speed, but the method is only suitable for the rotating speed estimation under the working condition of fixed rotating speed.

[1]Orman M,Rzeszucinski P,Tkaczyk A,et al.Bearing fault detection with the use of acoustic signals recorded by a hand-held mobile phone[C]//2015International Conference on Condition Assessment Techniques in Electrical Systems(CATCON).IEEE,2015:252-256.

Disclosure of Invention

The invention aims to enable field equipment maintainers to use mobile equipment (smart phones and tablet computers) to conveniently diagnose the bearing fault by the proposed invention method without additional special test hardware. The bearing fault under the variable-speed working condition is accurately diagnosed by utilizing the built-in loudspeaker and microphone hardware of the mobile equipment and the calculation display capacity through a signal processing method of envelope demodulation, least square fitting, spectral kurtosis, order tracking and envelope order spectrum.

In order to achieve the purpose, the invention adopts the technical scheme that:

the bearing fault diagnosis method based on the variable rotating speed working condition of the mobile equipment comprises the following steps:

the method comprises the following steps: closely aligning a loudspeaker of the mobile equipment to a rotating shaft where a test bearing is positioned, and sending out a set carrier frequency f through the loudspeaker_cThe microphone records the received sound signal, denoted as x (t);

step two: constructing a zero phase shift band-pass filter, wherein the center frequency of the band-pass filter is the carrier frequency f_cThe bandwidth is 2 times of the limit frequency of the measured bearing, and the signal x (t) is subjected to band-pass filtering to obtain a filtering signal x_F(t), filtering the signal x_F(t) obtaining an amplitude modulation signal x by Hilbert transform_E(t)；

Step three: searching for modulated signal x_EThe maximum point in (t) is taken as the zero crossing { x ] in the key phase signal_E(t₁),x_E(t₂),…,x_E(t_n) And (4) when the rotating shaft rotates for one circle every time of zero crossing, obtaining an instantaneous phase signal theta of the rotating shaft where the bearing is located by least square polynomial fitting to eliminate zero point estimation errors_S(t) converting the signal θ_S(t) differentiating to obtain an instantaneous rotating speed curve S (t) of the shaft;

step four: determining the frequency band of the fault impact component in the signal x (t) by a spectral Kurtogram (Kurtogram) method, and performing band-pass filtering and Hilbert transform on the frequency band to extract the fault impact envelope x_I(t)；

Step five: envelope the fault impact x_I(t) combining the instantaneous phase signal θ_S(t) carrying out order tracking to obtain the fault impact envelope x after angle domain resampling_I(θ_S) And further obtaining an envelope order spectrum through Fourier transform, and judging whether a fault occurs and the type according to whether the bearing fault characteristic order occurs in the envelope order spectrum.

The invention has the following beneficial effects:

a) the method of the invention uses the mobile device to accurately diagnose the bearing fault under the variable rotating speed working condition, does not need to use extra vibration noise test equipment and speed measurement hardware, has low test cost, and is beneficial to technical popularization and use of field maintainers in the form of application of mobile phone programs.

b) The bearing fault characteristic order is constant and is irrelevant to the working rotating speed of the bearing, and compared with the traditional envelope spectrum, the existence and the type of the fault can be easily determined through the envelope order spectrum.

c) For bearing fault diagnosis under the working condition of constant rotating speed, the method is also effective, and the working rotating speed of the bearing is not required to be known in advance.

Drawings

FIG. 1 is a schematic test diagram of an embodiment of the present invention.

FIG. 2 is a flow chart of the present invention.

Fig. 3 shows a sound signal x (t) recorded by a microphone according to an embodiment of the invention.

FIG. 4 shows an amplitude modulation signal x according to an embodiment of the present invention_E(t)。

FIG. 5 is a diagram showing an instantaneous phase signal θ of a rotating shaft on which a bearing according to an embodiment of the present invention is located_S(t)。

FIG. 6 is a graph S (t) showing the instantaneous rotational speed of the rotating shaft on which the bearing of the embodiment of the present invention is located.

FIG. 7 shows a fault strike envelope signal x according to an embodiment of the invention_I(t)。

FIG. 8 shows a fault strike envelope signal x according to an embodiment of the invention_I(t) spectrum.

FIG. 9 shows a fault impact envelope signal x after angle resampling according to an embodiment of the present invention_I(θ_S)。

FIG. 10 is a graph of an envelope order spectrum according to an embodiment of the present invention.

FIG. 11 is a photograph of a bearing failure in accordance with an embodiment of the present invention.

Detailed Description

The invention is described in detail below with reference to the figures and examples.

Fig. 1 is a schematic test diagram of an embodiment, where 1 is a mobile device, 2 is a rotation axis where a faulty bearing is located, 3 is the faulty bearing, 4 is a carrier component and a modulation echo transmitted by a mobile phone, and 5 is a fault impact radiated by the faulty bearing.

The specific parameters are as follows: 1) the mobile equipment 1 is a mobile phone (millet 11), and the distance between the mobile equipment and the surface of the rotating shaft 2 is 5 cm; 2) the rotating shaft 2 is driven to rotate by the servo motor, and the rotating speed is increased from about 340RPM to about 730RPM in 10 seconds; 3) structural parameters of the failed bearing 3: a) roller diameter: 12.3mm, b) pitch diameter: 52mm, c) contact angle: 0 °, d) number of rollers: 8; 4) the frequency of the carrier wave sent by the mobile phone is 5 kHz; 5) the mobile phone carries out high-frequency sampling and data processing on the sound signal, wherein the sampling frequency of the sound signal is 44100Hz, and the sampling time duration is 10 seconds.

The method for analyzing the sound signal and diagnosing the bearing fault is applied, and as shown in figure 2, the method for diagnosing the bearing fault under the variable rotating speed working condition based on the mobile equipment comprises the following steps:

the method comprises the following steps: aligning a loudspeaker of the mobile equipment to the surface of a rotating shaft where a test bearing is located at a distance of 5cm, and emitting a set carrier frequency f through the loudspeaker_cA 5000Hz sine wave, and a microphone records a received sound signal, which is denoted as x (t), and x (t) in this embodiment is shown in fig. 3;

step two: constructing a zero phase shift band-pass filter, wherein the center frequency of the band-pass filter is the carrier frequency f_cThe bandwidth is 5000Hz and is 2 times of the limit frequency of the measured bearing, the bandwidth is 60Hz in the embodiment, and the signal x (t) is subjected to band-pass filtering to obtain a filtering signal x_F(t), filtering the signal x_F(t) obtaining an amplitude modulation signal x by Hilbert transform_E(t), this example x_E(t) as shown in FIG. 4;

step three: searching for modulated signal x_EThe maximum point in (t) is taken as the zero crossing { x ] in the key phase signal_E(t₁),x_E(t₂),…,x_E(t_n) And the zero-crossing points are marked by asterisks in figure 4, each time a zero-crossing point is passed, the rotating shaft rotates for one circle, and in order to eliminate zero-point estimation errors, an instantaneous phase signal theta of the rotating shaft where the bearing is located is obtained through least square polynomial fitting_S(t), example θ_S(t) As shown in FIG. 5, the signal θ_S(t) differentiating to obtain an instantaneous speed curve s (t) of the shaft, where the instantaneous speed curve s (t) is consistent with a speed regulation curve of the set motor as shown in fig. 6 in this embodiment;

step (ii) ofFourthly, the method comprises the following steps: determining the frequency band of the fault impact component in the signal x (t) by a spectral Kurtogram (Kurtogram) method, and performing band-pass filtering and Hilbert transform on the frequency band to extract the fault impact envelope x_I(t), this example x_I(t) impact envelope x, as shown in FIG. 7_I(t) the frequency spectrum of the rotation speed tends to become dense in the time domain along with the increase of the rotation speed, as shown in fig. 8, obvious frequency blurring can be seen, and the bearing fault cannot be judged according to the traditional envelope analysis method;

step five: envelope the fault impact x_I(t) combining the instantaneous phase signal θ_S(t) carrying out order tracking to obtain the fault impact envelope x after angle domain resampling_I(θ_S) Example x_I(θ_S) As shown in fig. 9, the resampled impact envelopes are distributed at equal intervals in an angle domain, and then an envelope order spectrum is obtained through fourier transform, the envelope order spectrum of this embodiment is shown in fig. 10, harmonic waves of the bearing outer ring fault order appear in the envelope order spectrum, and therefore it can be judged that the bearing has an outer ring fault and is consistent with the actual fault of the bearing, and a fault bearing photograph is shown in fig. 11.

The bearing fault diagnosis method based on the variable-speed working condition of the mobile equipment does not need special test instrument equipment, fully exerts the loudspeaker and microphone of the mobile equipment and the calculation and display capacity, extracts fault characteristic information, and effectively diagnoses the bearing fault under the variable-speed working condition.

Claims (1)

1. The bearing fault diagnosis method based on the variable rotating speed working condition of the mobile equipment comprises the following steps:

the method comprises the following steps: closely aligning a loudspeaker of the mobile equipment to a rotating shaft where a test bearing is positioned, and sending out a set carrier frequency f through the loudspeaker_cThe microphone records the received sound signal, denoted as x (t);

step two: constructing a zero phase shift band-pass filter, wherein the center frequency of the band-pass filter is the carrier frequency f_cThe bandwidth is 2 times of the limit frequency of the measured bearing, and the signal x (t) is subjected to band-pass filtering to obtain a filtering signal x_F(t), filtering the signal x_F(t) obtaining an amplitude modulation signal x by Hilbert transform_E(t)；

Step three: searching for modulated signal x_EThe maximum point in (t) is taken as the zero crossing { x ] in the key phase signal_E(t₁),x_E(t₂),…,x_E(t_n) And (4) when the rotating shaft rotates for one circle every time of zero crossing, obtaining an instantaneous phase signal theta of the rotating shaft where the bearing is located by least square polynomial fitting to eliminate zero point estimation errors_S(t) converting the signal θ_S(t) differentiating to obtain an instantaneous rotating speed curve S (t) of the shaft;

step four: determining the frequency band of the fault impact component in the signal x (t) by a spectral Kurtogram (Kurtogram) method, and performing band-pass filtering and Hilbert transform on the frequency band to extract the fault impact envelope x_I(t)；

Step five: envelope the fault impact x_I(t) combining the instantaneous phase signal θ_S(t) carrying out order tracking to obtain the fault impact envelope x after angle domain resampling_I(θ_S) And further obtaining an envelope order spectrum through Fourier transform, and judging whether a fault occurs and the type according to whether the bearing fault characteristic order occurs in the envelope order spectrum.

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