CN111060317A - Method for judging fault signal of rolling bearing of mining fan motor - Google Patents
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Abstract
The invention relates to a method for judging a fault signal of a rolling bearing of a mining fan motor, which roughly comprises the following steps: arranging vibration, temperature and noise sensors on a bearing of the mining fan; generating anti-phase secondary noise, performing noise reduction processing on environmental noise, and optimizing the data acquisition environment of the fan; the sensor is arranged on the bearing of the fan motor, so that vibration and temperature signals of the rotating bearing are collected; the sensor uploads the acquired vibration signal data to an analysis system, and continuous discrete dyadic wavelet decomposition is carried out on the signals; calculating the fault frequency of the inner ring, the outer ring and the rolling body of the bearing; and judging whether the bearing has a fault or not. By the arrangement of the invention, the noise of the mine field collection environment can be greatly reduced, so that the collection precision of the field fan bearing is improved.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a processing method of a rolling bearing signal of a mining fan motor based on wavelet change.
Background
The mining field fan plays a role in ventilation at the bottom of a mine, and is directly related to life safety of underground personnel, so that the normal work of the fan is guaranteed to be very important. The rolling bearing is used as a key component in the rotating motor, most of mechanical faults are caused by the bearing faults, and therefore signal acquisition and processing of the rolling bearing of the fan are particularly critical. As is known, rolling bearings generally acquire signals of noise, vibration and temperature. Due to the complex field environment of the mining fan and the high difficulty in acquisition and signal denoising, multiple technologies need to be combined to obtain the required bearing operation signal parameters for equipment fault diagnosis.
CN201120184318 discloses a mine main fan and fan monitoring system, which comprises an upper computer, a network switch, a PLC control cabinet, an operation console, a field sensor, a fan accessory device and a fan starting cabinet, wherein the PLC control cabinet is connected with the upper computer through the network switch, the field sensor, the fan accessory device and the fan starting cabinet are all connected with the PLC control cabinet, and the PLC control cabinet is connected to the operation console. The patent can carry out centralized monitoring, real-time access and automatic analysis on the production process information, is convenient for implementing an optimal operation scheme, ensures the operation safety of a mine and ensures the precious life of miners; the upper computers are monitored, a dual-computer redundancy design is adopted, each upper computer can monitor two fans at the same time, and the reliability is higher; the intelligent digital instrument is independent of a data acquisition signal loop controlled by the PLC control cabinet, and the detection accuracy can be improved.
CN201610927086.1 discloses an orbit detector collected data processing method based on wavelet transformation, which comprises the following steps: step 1: inputting a group of original basic string track data; step 2: performing three-layer wavelet decomposition on the original group of basic string orbital data by using db3 wavelet to respectively obtain high-frequency signals and low-frequency signals of each layer; and step 3: identifying detection data containing gross errors in the high-frequency signals of the first layer of wavelet decomposition and removing the detection data; and 4, step 4: and after the detection data containing the gross errors are removed, performing wavelet reconstruction to obtain filtered basic chordal orbit data. The data processing method of the above patent can protect the edge information while eliminating noise, and does not introduce more signal distortion and feature loss.
CN201310556947 discloses a partial discharge signal denoising method based on lifting wavelet transform, comprising the following steps: (1) inputting a partial discharge signal to be denoised; (2) carrying out lifting wavelet decomposition processing on the local discharge signal to obtain high-frequency coefficient components with different decomposition scales and low-frequency coefficient components with the highest scale; (3) carrying out quantization processing on the high-frequency coefficient component by adopting a layered threshold and a soft threshold function based on wavelet entropy to remove noise components, and storing the high-frequency coefficient component as a new high-frequency coefficient component; (4) and (4) forming a coefficient component for signal reconstruction by using the new high-frequency coefficient component and the low-frequency coefficient component with the highest scale obtained in the step (3), and performing signal reconstruction on the coefficient to obtain a denoised partial discharge signal. The lifting wavelet of the above patent is transformed entirely in the time (space) domain, transforming the high and low pass filters into a series of relatively simple prediction and update steps. Therefore, the denoising speed of the lifting wavelet transformation is high, the design is flexible and simple, and the realization is easy.
Through patent search, it is found that the existing patent has no judgment method for the fault signal of the bearing of the fan for the mine, and the reason is that: firstly, due to the complex mine environment, more factors are considered when signals such as vibration, temperature and the like are collected; secondly, there is useless data in the collected signals, which increases the difficulty of analysis. Therefore, a signal processing method suitable for the application environment is urgently needed to be found.
Disclosure of Invention
The purpose of the invention is: a method for judging whether a bearing of a fan for a mine fails is provided.
In order to achieve the above object, the technical solution of the present invention is to provide a method for determining a fault signal of a rolling bearing of a mining fan motor, which is characterized by comprising the following steps:
step 1, arranging a vibration sensor, a temperature sensor and a noise sensor on a bearing of a mining fan;
step 2, generating anti-phase secondary noise based on the environmental noise acquired by the noise sensor, so as to realize active noise reduction and reduce the environmental noise, thereby optimizing the data acquisition environment of the vibration sensor and the temperature sensor;
step 3, after active noise reduction, respectively acquiring a vibration signal and a temperature signal through a vibration sensor and a temperature sensor;
and 4, uploading the collected vibration signals and temperature signals to an analysis system, and respectively analyzing and processing the vibration signals and the temperature signals, wherein:
the analysis processing of the vibration signal comprises the following steps:
step 4A01, drawing a power spectrogram after continuous wavelet decomposition of the vibration signal f (x), and performing continuous wavelet transformation on the vibration signal f (x)
Is defined as:
wherein a is a scale expansion parameter, a>0; b is a time translation parameter, and b belongs to R;
is a wavelet basis function that can be scaled and shifted according to the variation of parameters a, b;
step 4A02, calculating the fault frequency f of the bearing inner ringi,p：
Wherein n is the number of rolling elements in the bearing, frThe rotating frequency of the bearing, D is the diameter of the rolling body, D is the pitch diameter of the bearing, and α is a contact angle;
calculating bearing outer ring faultBarrier frequency fa,p：
Calculating the failure frequency f of the rolling bodybc：
Step 4A03, judging the fault frequency f of the inner ring of the bearing on the power spectrogram obtained in the step 4A01i,pBearing outer ring fault frequency fa,pAnd rolling element failure frequency fbcWhether wave crest frequency exists nearby or not, if so, judging the fault frequency f of the inner ring of the bearingi,pBearing outer ring fault frequency fa,pAnd rolling element failure frequency fbcWhether a certain frequency interval amplitude attenuation band exists near each multiple frequency or not is judged, if yes, the bearing inner ring, the bearing outer ring or the rolling body has faults, and if not, the faults do not exist;
the analysis processing of the temperature signal comprises the following steps:
subtracting the environmental temperature during testing from the temperature signal to obtain temperature rise, and judging the fault corresponding to the bearing according to the temperature rise;
and 5, if the bearing is detected to have a fault in the step 4, generating an alarm, otherwise, returning to the step 3.
Preferably, in step 4a01,
is a wavelet function.
By the arrangement of the invention, the noise of the mine field collection environment can be greatly reduced, so that the collection precision of the field fan bearing is improved. The wavelet transformation method adopted in the invention can further process the signals collected by the sensor, and solve the unreal signals caused by sampling errors, system internal instability and other factors, thereby providing data support for further state monitoring and fault diagnosis of the fan.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram showing vibration signals when a rolling bearing has a failure;
FIG. 3 is a diagram of the wavelet transformed signals of FIG. 2;
FIG. 4 is a graph of a fan rolling bearing vibration signal;
fig. 5 is a signal and power spectrum diagram of a fan rolling bearing.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
As shown in fig. 1, the method for determining a fault signal of a rolling bearing of a mining fan motor provided by the invention generally comprises the following steps:
step 1, arranging vibration, temperature and noise sensors on a bearing of a fan for a mine;
step 2, generating reverse-phase secondary noise, performing noise reduction treatment on environmental noise, and optimizing the data acquisition environment of the fan;
step 3, mounting a sensor on a bearing of the fan motor, and collecting vibration and temperature signals of a rotating bearing;
step 4, uploading the acquired vibration signal data to an analysis system by a sensor, and carrying out continuous discrete dyadic wavelet decomposition on the signals;
step 5, calculating the fault frequency of the bearing inner ring, the bearing outer ring and the rolling body;
and 6, judging whether the bearing has a fault or not.
In the above steps, the analyzing and processing by using the vibration signal to determine whether the bearing has a fault includes the following steps:
and carrying out continuous wavelet decomposition on the vibration signal. The wavelet is used as a wave with volatility and attenuation, can perform time-frequency localization analysis, gradually performs multi-scale thinning on signals through telescopic translation operation, can automatically adapt to the requirements of signal time-frequency analysis, and is more suitable for the problem of difficult processing than Fourier transform.
Continuous wavelet transform of a vibration signal f (x)
Can be defined as: :
in the formula: a is a scale expansion parameter, a>0; b is a time translation parameter, and b belongs to R;
is a wavelet basis function that can be scaled and shifted according to the variation of the parameters a, b,
as a function of wavelets
In the discrete wavelet transformation of the vibration signal f (x), the discrete processing is carried out on the scale parameters according to power series, and the mean value discrete value is carried out on the time (the sampling rate is required to meet the Nyquist sampling theorem).
Calculating the fault frequency of the inner ring, the outer ring and the rolling body of the bearing;
wherein, the failure frequency f of the inner ring of the bearingi,pThe calculation formula is as follows:
bearing outer ring failure frequency fa,pThe calculation formula is as follows:
bearing rolling element failure frequency fbcThe calculation formula is as follows:
wherein n is the number of rolling elements in the bearing, frThe rotating frequency of the bearing, D is the diameter of the rolling body, D is the pitch diameter of the bearing, and α is the contact angle.
Judging whether the bearing has faults or not
For the vibration signal, the vibration signal causing the bearing fault is in a damped oscillation shape, and the characteristic that the Daur technologies (db) wavelet function can better reflect the vibration signal of the rolling bearing fault. To better highlight this feature and thus enable good detection of fault signals from signals mixed with a lot of background noise, db1 is used as the wavelet basis function for wavelet analysis.
For example, the rotation frequency of the main shaft is set to be 1500r/min, the frequency is set to be 25Hz, and the fault characteristic frequencies of the rolling bearing are calculated to be 71.99Hz of the outer ring fault frequency, 103.00Hz of the inner ring fault frequency and 68.25Hz of the rolling body fault frequency.
Fig. 2 is a vibration signal when a certain rolling bearing has a fault, fig. 3 is a signal after wavelet transformation and a power frequency diagram, it can be analyzed from the power frequency diagram that peak frequencies exist at f-104.5 Hz,209(2 x 104.5) Hz,312.5(3 x 104.5) Hz, and amplitude attenuation bands exist at both sides of the frequency of 104.5Hz and multiples, and compared with the characteristic frequency of the fault of the inner ring of 103Hz calculated before, 104.5Hz is close to the characteristic frequency, so that the fault of the inner ring can be judged.
In addition, for example, with the signal diagram of the fan rolling bearing in fig. 4, fig. 4 is a vibration signal diagram, fig. 5 is a signal after wavelet transformation and a power spectrum, it can be seen from the power spectrum that there are spectrum peaks at f 65Hz and 130(2 x 65) Hz, and there are certain amplitude drops at both sides of the frequency positions of 65Hz and twice of the frequency position, and similarly, 65Hz is closer than the fault characteristic frequency of 68.25Hz calculated before, so it can be determined that the rolling element has a fault.
In the above steps, the analyzing and processing by using the temperature signal to determine whether the bearing has a fault includes the following steps:
for the collected temperature signals, because the part for installing the bearing is allowed to have certain temperature when the host computer operates, generally, detection personnel can touch the shell of the host computer by hands and need not feel scalding hands as normal, otherwise, the temperature of the bearing is over-high. For the temperature signal, the reason why the bearing temperature is too high in general is as follows: the quality of the lubricating oil is not qualified or deteriorated, and the viscosity of the lubricating oil is too high; over-tight assembly of the host (insufficient clearance): the bearing is assembled too tightly; the bearing race has overlarge rotational load on the shaft or the shell; bearing cages or rolling element chipping, etc.
According to the regulations related to national and international standards, the maximum temperature of the rolling bearing is not more than 95 ℃, and the maximum temperature of the sliding bearing is not more than 80 ℃. And the temperature rise is not more than 55 ℃, wherein the temperature rise is the temperature of the bearing minus the environmental temperature during the test, specifically HG 25103-91.
And combining the vibration and temperature signals, and if one of the vibration and temperature signals has a fault alarm, judging that the bearing has a fault.
Claims (2)
1. A method for judging fault signals of a rolling bearing of a mining fan motor is characterized by comprising the following steps:
step 1, arranging a vibration sensor, a temperature sensor and a noise sensor on a bearing of a mining fan;
step 2, generating anti-phase secondary noise based on the environmental noise acquired by the noise sensor, so as to realize active noise reduction and reduce the environmental noise, thereby optimizing the data acquisition environment of the vibration sensor and the temperature sensor;
step 3, after active noise reduction, respectively acquiring a vibration signal and a temperature signal through a vibration sensor and a temperature sensor;
and 4, uploading the collected vibration signals and temperature signals to an analysis system, and respectively analyzing and processing the vibration signals and the temperature signals, wherein:
the analysis processing of the vibration signal comprises the following steps:
step 4A01, drawing a power spectrogram after continuous wavelet decomposition of the vibration signal f (x), and performing continuous wavelet transformation on the vibration signal f (x)
Is defined as:
in the formula, a is a scale expansion parameter, and a is more than 0; b is a time shift parameter that is,
is a wavelet basis function that can be scaled and shifted according to the variation of parameters a, b;
step 4A02, calculating the fault frequency f of the bearing inner ringi，p：
Wherein n is the number of rolling elements in the bearing, frThe rotating frequency of the bearing, D is the diameter of the rolling body, D is the pitch diameter of the bearing, and α is a contact angle;
calculating the failure frequency f of the bearing outer ringa，p：
Calculating the failure frequency f of the rolling bodybc：
Step 4A03, obtaining in step 4A01On the obtained power spectrogram, judging the fault frequency f of the inner ring of the bearingi，pBearing outer ring fault frequency fa，pAnd rolling element failure frequency fbcWhether wave crest frequency exists nearby or not, if so, judging the fault frequency f of the inner ring of the bearingi，pBearing outer ring fault frequency fa，pAnd rolling element failure frequency fbcWhether a certain frequency interval amplitude attenuation band exists near each multiple frequency or not is judged, if yes, the bearing inner ring, the bearing outer ring or the rolling body has faults, and if not, the faults do not exist;
the analysis processing of the temperature signal comprises the following steps:
subtracting the environmental temperature during testing from the temperature signal to obtain temperature rise, and judging the fault corresponding to the bearing according to the temperature rise;
and 5, if the bearing is detected to have a fault in the step 4, generating an alarm, otherwise, returning to the step 3.
2. The method for judging the fault signal of the rolling bearing of the mining fan motor according to claim 1, characterized in that in step 4A01,
is a wavelet function.
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| CN113702041A (en) * | 2021-09-18 | 2021-11-26 | 华能威宁风力发电有限公司 | Bearing fault diagnosis method |
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Application publication date: 20200424 |