CN113944600A - Method and system for detecting fan main bearing fault by utilizing stress wave technology - Google Patents
Method and system for detecting fan main bearing fault by utilizing stress wave technology Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention discloses a method and a system for detecting the fault of a main bearing of a fan by utilizing a stress wave technology, wherein the method comprises the following steps: acquiring original stress wave waveform data of the main bearing of the fan in operation; screening the original stress wave waveform data, and extracting normal stress waveform data; and analyzing the normal stress waveform data, and judging whether the fan main bearing has a fault. The invention applies the stress wave detection technology to the fault detection of the fan main bearing, carries out quantitative and qualitative analysis on the fault of the fan main bearing, can carry out prospective prejudgment on the fault, obviously improves the accuracy of the fault detection of the fan main bearing, and reduces other adverse effects caused by the fault of the fan main bearing.
Description
Technical Field
The invention relates to the field of nondestructive testing, in particular to a method and a system for detecting a fan main bearing fault by utilizing a stress wave technology.
Background
The main shaft bearing of the wind driven generator is a main component for absorbing wind action load and transmitting load, and the performance of the main shaft bearing not only has an influence on transmission efficiency, but also determines the maintenance cost of a main transmission chain.
At present, vibration technology, pulse and other high-frequency detection technologies and lubricating oil analysis technology are mostly adopted for fault detection of a main shaft bearing of the wind driven generator. The fundamental drawback of the vibration technique is that the conventional vibration accelerometer cannot distinguish the subtle signals generated by the initial damage from the vibration signals generated by the normal operation of the equipment itself, and the vibration technique is more ineffective when the equipment is operated at a lower operating speed because the energy released by the defects at low speed is too small to excite the vibration of the equipment, so that the vibration analysis cannot effectively identify and prevent the occurrence of the damage. Pulses and other high frequency detection techniques can only measure the amplitude of shock events generated within the device, indicating the presence of damage, but cannot account for the extent of damage. The diagnostic accuracy of the lubricating oil analysis technique is very low and it is not possible to determine from which particular location the chips came and to make a practical prediction of the remaining useful life of the equipment.
Therefore, a method for detecting a fault of a main shaft bearing of a wind turbine generator is needed to predict the fault of the main shaft bearing of the wind turbine generator in a prospective and accurate manner.
Disclosure of Invention
The invention aims to provide a method and a system for detecting a fan main bearing fault by utilizing a stress wave technology, which are used for solving the problems in the prior art, so that the stress wave detection technology is applied to the fault detection of a fan main bearing, the fan main bearing fault is quantitatively and qualitatively analyzed, the fault can be prospectively predicted, the accuracy of the fan main bearing fault detection is obviously improved, and other adverse effects caused by the fan main bearing fault are reduced.
In order to achieve the purpose, the invention provides the following scheme: the invention provides a method for detecting a fan main bearing fault by utilizing a stress wave technology, which comprises the following steps of:
acquiring original stress wave waveform data of the main bearing of the fan in operation;
screening the original stress wave data, and extracting normal stress waveform data;
and analyzing the normal stress waveform data, and judging whether the fan main bearing has a fault.
Optionally, screening the raw stress wave data comprises:
setting a grade threshold range of the collected signal of each stress wave channel;
if the original stress wave data fall into the range of the grade threshold, the original stress wave data are determined to be normal data and reserved; and if the data falls out of the range of the grade threshold, the data is determined to be abnormal data and discarded.
Optionally, analyzing the normal stress waveform data includes fault quantitative analysis and fault qualitative analysis.
Optionally, the fault quantitative analysis comprises:
carrying out rotation speed estimation on the normal stress waveform data to obtain a rotation speed value;
carrying out spectrum analysis on the normal stress waveform data to obtain fault frequency;
and analyzing the rotating speed value and the fault frequency to obtain a quantitative analysis result of the fan main bearing, wherein the quantitative analysis result comprises no fault and fault.
Optionally, the fault qualitative analysis comprises:
analyzing the normal stress waveform data to obtain a time domain index and an impact index;
carrying out spectrum analysis on the normal stress waveform data to obtain a frequency domain index,
constructing a composite index based on the time domain index, the impact index and the frequency domain index,
and analyzing to obtain a qualitative analysis result of the main bearing of the fan based on the comprehensive indexes, wherein the qualitative analysis result comprises slight faults, faults and normality.
Optionally, the calculation method of the rotation speed value is as shown in formula (8):
wherein x (u) is a source signal, and g (u-t) is a window signal.
Optionally, the obtaining the impact index includes: noise condition and threshold setting, weighted impact calculation, weighted energy calculation, and weighted count calculation.
Still provide a system for utilize stress wave technique to detect fan main bearing trouble, include: a plurality of stress wave acquisition systems, a data acquisition station, a communication module and a ground data station,
the stress wave acquisition system is used for acquiring stress wave data of the fan main bearing and transmitting the stress wave data to the data acquisition station;
the data acquisition station is used for collecting and storing the stress wave data and transmitting the stress wave data to the ground data station;
the ground data station is used for analyzing the stress wave data and judging the fault of the main bearing of the fan,
and the communication module is used for information interaction between the data acquisition station and the ground data station.
Optionally, a plurality of stress wave collection systems are uniformly distributed on the circumference of the fan main bearing by taking the fan main bearing as a center.
Optionally, the ground data station includes a data receiving and storing module, a data analyzing module, and a display module, and the data receiving and storing module, the data analyzing module, and the display module are connected in sequence.
The invention discloses the following technical effects:
according to the method and the system for detecting the failure of the main bearing of the fan by using the stress wave technology, the stress wave is used for real-time online analysis, the amplitude and the duration of an impact event are measured, the severity of damage is quantitatively measured, the failure prediction can be realized just before the early-stage degeneration change of equipment begins and the actual damage does not occur, the development trend of the damage can be accurately predicted, early warning is timely performed, the accuracy and the timeliness of the failure early warning are obviously improved, and the loss caused by the failure of the main bearing of the fan is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart illustrating a method for detecting a failure of a main bearing of a wind turbine by using stress waves according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a system for detecting a failure of a main bearing of a wind turbine by using stress waves according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a bearing fault impact signal generation in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a stress wave signal model according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating relative stages in the health management of a device over its life cycle for various detection techniques in accordance with an embodiment of the present invention;
fig. 6 is a schematic diagram of impact indicator acquisition in the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a method and a system for detecting a fault of a main bearing of a fan by utilizing a stress wave technology, wherein the system is shown as figure 2 and comprises the following steps: the stress wave acquisition system adopts a stress wave sensor, and the communication module adopts 4G wireless equipment. The stress wave sensors are uniformly distributed on the circumference of the fan main bearing by taking the fan main bearing as a center. In this embodiment, 4 stress wave sensors are taken as an example, and 4 sensors are uniformly distributed on one circumference in the shaft near the front bearing. Firstly, coating a coupling agent on the bottom of the sensor to ensure good signal acquisition coupling quality, then fixing the sensor through a magnetic seat to enable the bearing to be tightly connected with the inner cavity, and finally completing the installation of the stress wave sensor. The stress wave sensor converts the stress wave generated by the sound source on the surface of the detected object into an electric signal, and the output voltage of the stress wave sensor depends on the signal intensity of the stress wave source and is between tens of microvolts and several volts.
The stress wave sensor transmits collected fan main bearing stress wave data to the data acquisition station for storage and carries out preliminary analysis on the collected fan main bearing stress wave data, time domain indexes, frequency domain indexes, impact indexes and the like are extracted, the data acquisition station transmits the preliminary analysis result of the stress wave data to the ground data station through the 4G wireless equipment, the preliminary analysis result of the stress wave data is subjected to depth analysis, and whether the fan main bearing has faults and fault severity is judged. The ground data station comprises a receiving storage module, a data analysis module and a display module, wherein the receiving storage module is used for storing stress wave data after the stress wave data reach the ground data station, the data analysis module is used for carrying out quantitative and qualitative analysis on the stress wave data, judging whether faults exist or not and judging the fault degree, and displaying the analysis process and the result through the display module.
The method for detecting the fault of the main bearing of the fan by utilizing the stress wave technology is shown in figure 1 and comprises the following steps:
s100, collecting original stress wave waveform data of the main bearing of the fan in operation.
When the main fan main shaft bearing operates, the stress state on the contact surface is complex, the fault source initiation point is usually limited in a narrow area on the surface layer of the bearing, and when the surface contact stress exceeds a certain value, the surface locally shows the micro-seismic activity, and a stress wave signal is generated. The process of stress wave signal generation is analyzed from a microscopic angle, a rolling body, an outer ring or an inner ring of the rolling bearing have certain roughness, and stress wave signals are generated by collision of protruding parts of two rough surfaces. When local defects exist on the surface of the rolling element, as shown in fig. 3, the rolling elements periodically impact a fault point to generate collision when passing through a raceway defect, so that a stress wave signal is generated, pulses characterized by abrupt impact are in a damping oscillation mode, and the frequency of the occurrence of the pulses of the discrete abrupt impact is the fault frequency. When the bearing is in fault, the sensor will be excited at a certain characteristic frequency, which provides useful information for fault diagnosis.
When a bearing in operation fails, the corresponding components will have different characteristic frequencies. The number of rolling elements in the bearing is Z, the diameter of the rolling elements is D, the diameter of a pitch circle is D, the rotating speed of the main shaft is n, and the contact angle alpha is set. According to basic parameter values of all parts of the bearing, the theoretical characteristic frequency of the rolling bearing when the rolling bearing breaks down can be obtained.
Rotational frequency f of the spindlesAs shown in formula (1):
outer ring fault characteristic frequency fiAs shown in formula (2):
characteristic frequency f of inner ring faultoAs shown in formula (3):
characteristic frequency f of rolling element failurebAs shown in formula (4):
characteristic frequency f of cage failurecAs shown in formula (5):
the stress wave signal generating process is an elastic stress wave releasing process. According to the generation mechanism of the stress wave signals analyzed in the previous section, the signals generated by the rolling bearing faults are burst pulse type stress wave signals, one stress wave signal is one impact, each impact (spike) corresponds to one stress wave activity, and the amplitude of the spike is related to the energy released by the sound source and the activity. The single shock in fig. 3 is a typical burst-type stress wave signal waveform that can be described in the time domain as a very "narrow" burst-type sharp pulse wave, which is generated mainly due to relative impact from a crack member or impact with an external object, etc.
The generation process of the burst pulse type stress wave signal is short-lived, and the generated signal reaches the maximum value rapidly and decays rapidly in a short time (hundreds of microseconds to a few milliseconds). For each burst pulse type stress wave signal, at any time t, an exponential decay model can be used as shown in fig. 4, and the mathematical model can be represented by formula (6):
wherein y (t) is a burst pulse type stress wave time domain signal, A is the maximum amplitude of the pulse signal, tau is a signal attenuation constant, omega is a signal oscillation angular frequency,
for the initial phase angle, e is a mathematical constant, the base of the natural logarithmic function, e ≈ 2.71828, and t denotes time.
Because a large number of microscopic protrusions exist on each of the two contact surfaces, the actual stress wave signal is caused by the mutual impact of the large number of protrusions, so that the collected stress wave fault signal of the rolling bearing is a continuous stress wave signal formed by the superposition of a large number of transient burst pulse type signals, as shown in a continuous signal in fig. 3, and the mathematical model of the signal can be represented by formula (7):
wherein N is the number of the instantaneous surface micro-asperities which are impacted, and the value is a large number, AiIn order to be the amplitude value,
is a phase angle, ωiIs angular frequency, i is the number of the collected transient burst pulse type signals, and the value range of i is 0 to N, tau1Represents the time required for the continuous signal to decay from a maximum to 1/e of the original value, with a constant decay.
S200, screening the original stress wave data, and extracting normal stress waveform data to serve as a data source to be analyzed.
Setting a grade threshold range of the collected signal of each stress wave channel; if the original stress wave waveform data fall into the range of the grade threshold, the original stress wave waveform data are determined as normal data and reserved; if the data falls out of the range of the grade threshold, the data is determined to be abnormal data and discarded.
The main bearing of the fan, in the range of 6-14rpm, the stress wave signal is generally 30-80mv, and the data less than 30mv or the data more than 80mv are considered as abnormal data and discarded.
And S300, analyzing the normal stress waveform data, and judging whether the fan main bearing has faults or not and the fault degree.
The analysis of the normal stress wave waveform data comprises two processes of quantitative analysis and qualitative analysis.
Wherein, the quantitative analysis comprises the following steps:
and S311, carrying out rotation speed estimation on the normal stress waveform data to obtain a rotation speed value.
And extracting and matching the rotating speed by using a matching tracking algorithm according to the original waveform signal of the stress wave. The calculation formula is shown in formula (8):
where x (u) is the source signal, g (u-t) is the window signal, j is the imaginary indicator function, t is time, fu is frequency, u is the amplitude indicator function, e is the mathematical constant, which is the base of the natural logarithmic function, and e ≈ 2.71828. And multiplying the time window function by the source signal to realize windowing and translation near u, matching and tracking the most obvious frequency in each window function to be the rotating speed frequency, and finally converting the rotating speed frequency into a rotating speed value.
And S312, carrying out spectrum analysis on the normal stress waveform data to obtain fault frequency.
And acquiring a long waveform as an original waveform every half hour within 24 hours of a day, and performing fault frequency analysis by using the original waveform of fast Fourier transform (FFT spectrum analysis) to obtain the fault frequency.
And S313, analyzing the rotating speed value and the fault frequency, and obtaining a quantitative analysis result of the fan main bearing, wherein the quantitative analysis result comprises no fault and fault.
The qualitative analysis comprises the following steps:
and S321, analyzing the normal stress waveform data to obtain a time domain index and an impact index.
Time domain index:
kurtosis (Kurtosis) can reflect the impact characteristic of a signal, is a dimensionless index and can be used for measuring the intensity of fault impact in an acoustic emission waveform; the Crest factor (Crest factor) is the ratio of the peak value to the effective value of the waveform, is also a dimensionless unit and can be used for measuring the waveform characteristics of the acoustic emission signal; the impact factor (Impulse factor) is the ratio of the peak value to the average value and can be used for evaluating the amplitude characteristic of the acoustic emission signal; the Margin factor (Margin factor) reflects the peak characteristics of the acoustic emission signal.
As shown in fig. 6, acquiring the impact index includes:
setting a noise condition and a threshold:
the noise level is 50mv when the equipment is in operation, and is converted into 68 dB; and analyzing the waveforms of the plurality of fault devices to determine the impact generated by the bearing fault.
Weighted impact calculation:
an acoustic emission signal exceeding a set threshold is counted as one impact, one for each impact (spike).
And (3) weighted energy calculation:
the energy can reflect the relative energy or intensity of the signal, the unit is mv · us, the magnitude of the energy can reflect the signal intensity and frequency, and the energy can be used for evaluating the intensity and activity of the acoustic emission. Therefore, the energy amount is counted in the detection, and the degree of the failure can be quantitatively determined to some extent. The area under the signal detection envelope is the weighted energy.
And (3) calculating a weighted count:
the weighted count refers to the number of oscillations that exceed a threshold signal, the magnitude of which also roughly reflects the signal strength and frequency, and is suitable for evaluation of acoustic emission activity. Here as a reference index for quantitative evaluation.
And S322, carrying out spectrum analysis on the normal stress waveform data to obtain a frequency domain index.
Frequency domain index:
crest factor: in the characteristic spectrogram, the method is used for evaluating the impact characteristics near the fault characteristic frequency; the higher the crest factor, the more pronounced the crest value corresponding to the fault signature frequency. Sparsity: in the characteristic spectrogram, the characteristic of a single peak value near the characteristic frequency is measured; the higher the sparsity is, the more obvious the single peak near the characteristic frequency is, compared with the fault factor, the advantage is that the interference of multiple peaks near the characteristic frequency can be suppressed, the disadvantage is that the amplitude is smaller, and the difference between the sparsity with obvious peaks and the sparsity with unnoticeable peaks is not large. The coefficient of the total distance: in the characteristic spectrogram, the characteristic spectrogram is used for evaluating the signal-to-noise ratio corresponding to the characteristic frequency, and as the fault characteristic frequency concerned in the embodiment is mostly in a low frequency band and is easily interfered by noise, in order to highlight the definition degree of the peak value corresponding to the fault characteristic frequency, a full range coefficient is introduced, so that the advantage of suppressing the noise interference is realized.
And S323, constructing a comprehensive index according to the time domain index, the impact index and the frequency domain index. And analyzing to obtain a qualitative analysis result of the main bearing of the fan according to the comprehensive indexes, wherein the qualitative analysis result comprises slight faults, faults and normality.
Firstly, calculating time domain indexes and frequency domain indexes of each group of data samples and constructing a characteristic vector; secondly, reducing the dimension of the characteristic vector by a data compression technology, and keeping the nonlinear relation among all data samples; and finally, normalizing the feature vectors subjected to the dimensionality reduction and inputting the normalized feature vectors into the constructed classification model for degradation evaluation analysis.
And finally, judging the fault condition of the main bearing of the fan by integrating the quantitative analysis result and the qualitative analysis result.
The fan main bearing fault detection and vibration detection technology, the pulse and other high-frequency detection technology, the lubricating oil analysis technology and the smoke alarm technology which are carried out by utilizing the stress wave technology have obvious advantages in the health management of the whole life cycle of the equipment, as shown in figure 5. Aiming at the stress wave technology generated by friction and impact, the relevant state can be found just after the equipment fails, even at the beginning of the condition causing the equipment to fail, and the method has obvious technical advantages compared with the traditional detection means of vibration, lubricating oil, temperature and the like, and is an ideal means for health monitoring in the whole life cycle.
In contrast to vibration detection techniques, stress waves are based on acoustic techniques, which enable diagnostic and evaluation analyses to be made in the earliest possible time with respect to the failure and operating conditions of the installation, i.e. they are predicted and reported in advance just before an early degenerative change of the installation begins and before actual damage has not yet occurred. The stress wave energy analysis system is designed to overcome the problem which always besets the effectiveness of the vibration technology. The fundamental drawback of vibration technology is that conventional vibration accelerometers cannot distinguish the subtle signals generated by incipient damage from the vibration signals generated by normal operation of the device itself. Any equipment has inherent vibration which is far stronger than abnormal vibration generated by initial damage of the equipment, and the stress wave technology can completely ignore the vibration generated by dynamic motion of the equipment and only focus on the events of abnormal impact and friction. Once detected, it is analyzed for measurements and, based thereon, a trend report is formed that the health of the device may deteriorate over time. Moreover, when the equipment runs at a lower working speed, the vibration technology is more useless, and the energy released by the defects at a low speed is too small to excite the vibration of the equipment, so that the vibration analysis cannot effectively identify and prevent the occurrence of damage; but at this point the stress wave technique is still adequate.
For pulse and other high frequency detection techniques, it can only measure the amplitude of shock events generated inside the device, indicating the presence of damage, but cannot account for the extent of damage; the stress wave technique can measure the amplitude and duration of the impact event, quantitatively measuring the severity of the damage: the area/size of the device damage resulting from impact and friction.
Compared with stress wave energy analysis, the diagnosis accuracy of the lubricating oil analysis technology is very low, which specific part the chips come from cannot be determined, and the actual prediction on the residual service life of the equipment cannot be made; while stress wave analysis is real-time online and does not require time-consuming and laborious sample collection and processing activities, the stress wave peak amplitude histogram can be used for lubrication troubleshooting, which can clearly illustrate whether lubrication problems or other random events are the cause of stress wave energy rise.
Based on the characteristics different from other detection technologies, the stress wave detection technology can realize prospective equipment management, namely, the fault prediction is realized just before the early degeneration change of the equipment begins and the actual damage does not occur. The stress wave technology can not only find problems in the early stage, but also accurately predict the development trend of damage, and remind operators when the damage reaches a certain degree; for equipment needing to be shut down for carrying out major repair, enough time is provided for a user to make a maintenance plan, purchase parts and organize technicians, and the optional maintenance is realized. In the industries of foreign aviation, ships, automobiles, petrochemical industry, electric power and the like, the stress wave technology provides great advantages of availability, reliability and reduction of initial faults.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (10)
1. A method for detecting the fault of a main bearing of a fan by utilizing a stress wave technology is characterized by comprising the following steps of:
acquiring original stress wave waveform data of the main bearing of the fan in operation;
screening the original stress wave waveform data, and extracting normal stress waveform data;
and analyzing the normal stress waveform data, and judging whether the fan main bearing has a fault.
2. The method of claim 1, wherein screening the raw stress wave waveform data comprises:
setting a grade threshold range of the collected signal of each stress wave channel;
if the original stress wave data fall into the range of the grade threshold, the original stress wave data are determined to be normal data and reserved; and if the data falls out of the range of the grade threshold, the data is determined to be abnormal data and discarded.
3. A method for detecting a failure of a main bearing of a wind turbine according to claim 1, wherein analyzing said normal stress waveform data comprises quantitative and qualitative failure analysis.
4. A method for detecting a failure of a main bearing of a wind turbine according to claim 3, wherein said quantitative failure analysis comprises:
carrying out rotation speed estimation on the normal stress waveform data to obtain a rotation speed value;
carrying out spectrum analysis on the normal stress waveform data to obtain fault frequency;
and analyzing the rotating speed value and the fault frequency to obtain a quantitative analysis result of the fan main bearing, wherein the quantitative analysis result comprises no fault and fault.
5. A method for detecting a fault of a main bearing of a wind turbine according to claim 4, wherein said qualitative analysis of the fault comprises:
analyzing the normal stress waveform data to obtain a time domain index and an impact index;
carrying out spectrum analysis on the normal stress waveform data to obtain a frequency domain index,
constructing a composite index based on the time domain index, the impact index and the frequency domain index,
and analyzing to obtain a qualitative analysis result of the main bearing of the fan based on the comprehensive indexes, wherein the qualitative analysis result comprises slight faults, faults and normality.
6. The method for detecting the fault of the main bearing of the wind turbine by using the stress wave technology as claimed in claim 4, wherein the calculation method of the rotating speed value is as shown in formula (8):
wherein x (u) is a source signal, and g (u-t) is a window signal.
7. The method of detecting a wind turbine main bearing fault using stress wave technology of claim 5, wherein obtaining the impact indicator comprises: noise condition and threshold setting, weighted impact calculation, weighted energy calculation, and weighted count calculation.
8. A system for detecting a failure of a main bearing of a wind turbine by using a stress wave technique, the system being used for implementing the method for detecting a failure of a main bearing of a wind turbine by using a stress wave technique according to any one of claims 1 to 7, and the method comprising: a plurality of stress wave acquisition systems, a data acquisition station, a communication module and a ground data station,
the stress wave acquisition system is used for acquiring stress wave data of the fan main bearing and transmitting the stress wave data to the data acquisition station;
the data acquisition station is used for collecting and storing the stress wave data and transmitting the stress wave data to the ground data station;
the ground data station is used for analyzing the stress wave data and judging the fault of the main bearing of the fan;
and the communication module is used for information interaction between the data acquisition station and the ground data station.
9. The system for detecting a failure of a fan main bearing according to claim 8, wherein the stress wave collection systems are evenly distributed around the circumference of the fan main bearing and centered on the fan main bearing.
10. The system for detecting the fault of the main bearing of the wind turbine according to the claim 8, wherein the ground data station comprises a data receiving and storing module, a data analyzing module and a display module, and the data receiving and storing module, the data analyzing module and the display module are connected in sequence.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116861218A (en) * | 2023-07-25 | 2023-10-10 | 上海华菱电站成套设备股份有限公司 | Mine winder key equipment state monitoring early warning system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1776390A (en) * | 2005-11-30 | 2006-05-24 | 沈阳工业大学 | Low-speed heavy-load rotary machinery fault diagnosis method |
| CN104374576A (en) * | 2014-11-25 | 2015-02-25 | 沈阳化工大学 | Method for extracting fault stress waves of low-speed bearing |
| US20160178478A1 (en) * | 2013-09-13 | 2016-06-23 | Aktiebolaget Skf | Device & method for testing an acoustic emission sensor |
| EP3054292A1 (en) * | 2015-02-06 | 2016-08-10 | Mitsubishi Heavy Industries, Ltd. | Apparatus and method for diagnosing abnormality of bearing |
| CN110487917A (en) * | 2019-08-30 | 2019-11-22 | 重庆高开清芯科技产业发展有限公司 | A kind of measure on stress pulse neural network based and analysis system |
-
2021
- 2021-10-15 CN CN202111202715.1A patent/CN113944600B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1776390A (en) * | 2005-11-30 | 2006-05-24 | 沈阳工业大学 | Low-speed heavy-load rotary machinery fault diagnosis method |
| US20160178478A1 (en) * | 2013-09-13 | 2016-06-23 | Aktiebolaget Skf | Device & method for testing an acoustic emission sensor |
| CN104374576A (en) * | 2014-11-25 | 2015-02-25 | 沈阳化工大学 | Method for extracting fault stress waves of low-speed bearing |
| EP3054292A1 (en) * | 2015-02-06 | 2016-08-10 | Mitsubishi Heavy Industries, Ltd. | Apparatus and method for diagnosing abnormality of bearing |
| CN110487917A (en) * | 2019-08-30 | 2019-11-22 | 重庆高开清芯科技产业发展有限公司 | A kind of measure on stress pulse neural network based and analysis system |
Non-Patent Citations (1)
| Title |
|---|
| 王楠: "基于分形-小波的低速轴承磨损故障物理特性研究", 中国博士学位论文全文数据库（电子期刊）工程科技II辑, pages 13 - 26 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116861218A (en) * | 2023-07-25 | 2023-10-10 | 上海华菱电站成套设备股份有限公司 | Mine winder key equipment state monitoring early warning system |
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