CN113944600B - Method and system for detecting fan main bearing faults by using stress wave technology - Google Patents
Method and system for detecting fan main bearing faults by using stress wave technology Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
- F03D80/70—Bearing or lubricating arrangements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention discloses a method and a system for detecting faults of a main bearing of a fan by using a stress wave technology, wherein the method comprises the following steps: collecting original stress wave waveform data of the operation of a main bearing of the fan; screening the original stress wave waveform data and extracting normal stress wave waveform data; and analyzing the normal stress waveform data, and judging whether the fan main bearing has faults or not. The method applies the stress wave detection technology to the fault detection of the main bearing of the fan, carries out quantitative and qualitative analysis on the fault of the main bearing of the fan, can carry out prospective pre-judgment on the fault, obviously improves the accuracy of the fault detection of the main bearing of the fan, and reduces other adverse effects caused by the fault of the main bearing of the fan.
Description
Technical Field
The invention relates to the field of nondestructive testing, in particular to a method and a system for detecting faults of a main bearing of a fan by using a stress wave technology.
Background
The main shaft bearing of the wind driven generator is a main component for absorbing wind force acting load and transmitting load, and the performance of the main shaft bearing not only has influence on transmission efficiency, but also determines maintenance cost of a main transmission chain.
At present, vibration technology, pulse and other high-frequency detection technology and lubricating oil analysis technology are mostly adopted for fault detection of a main shaft bearing of a wind driven generator. The fundamental defect of the vibration technology is that the conventional vibration accelerometer cannot distinguish the fine signal generated by the initial damage from the vibration signal generated by the normal operation of the device itself, and when the device operates at a lower operating speed, the vibration technology is more ineffective because the energy released by the defect at the low speed is too small to excite the vibration of the device, so that the vibration analysis cannot effectively identify and prevent the damage. The pulses and other high frequency detection techniques can only measure the amplitude of the impact events generated inside the device, indicating the presence of damage, but not the extent of damage. The diagnostic accuracy of the lubricating oil analysis technique is very low, and it is impossible to determine from which specific part the chip comes, and it is impossible to make a practical prediction of the remaining useful life of the equipment.
Therefore, there is a need for a method for detecting faults of a main shaft bearing of a wind driven generator, which can predict faults of the main shaft bearing of the wind driven generator accurately in a prospective manner.
Disclosure of Invention
The invention aims to provide a method and a system for detecting faults of a main bearing of a fan by using a stress wave technology, so as to solve the problems in the prior art, enable the stress wave detection technology to be applied to fault detection of the main bearing of the fan, perform quantitative and qualitative analysis on the faults of the main bearing of the fan, perform prospective pre-judgment on the faults, remarkably improve the accuracy of fault detection of the main bearing of the fan and reduce other adverse effects caused by the faults of the main bearing of the fan.
In order to achieve the above object, the present invention provides the following solutions: the invention provides a method for detecting faults of a main bearing of a fan by using a stress wave technology, which comprises the following steps:
collecting original stress wave waveform data of the operation of a main bearing of the fan;
screening the original stress wave data, and extracting normal stress wave data;
and analyzing the normal stress waveform data, and judging whether the fan main bearing has faults or not.
Optionally, screening the raw stress wave data includes:
setting a level threshold range of the acquired signal of each stress wave channel;
if the original stress wave data fall within the range of the grade threshold value, the original stress wave data are determined to be normal data and are reserved; and if the data fall outside the range of the level threshold, the data are determined to be abnormal data, and the abnormal data are discarded.
Optionally, analyzing the normal stress waveform data includes a quantitative analysis of the fault and a qualitative analysis of the fault.
Optionally, the fault quantitative analysis includes:
performing rotational speed estimation on the normal stress waveform data to obtain a rotational speed value;
performing 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 main bearing of the fan, wherein the quantitative analysis result comprises no fault and faults.
Optionally, the qualitative fault analysis includes:
analyzing the normal stress waveform data to obtain a time domain index and an impact index;
performing 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 and obtaining a qualitative analysis result of the main bearing of the fan based on the comprehensive index, wherein the qualitative analysis result comprises slight faults, faults and normal faults.
Optionally, the calculation method of the rotation speed value is as shown in formula (8):
where x (u) is the source signal and g (u-t) is the window signal.
Optionally, obtaining the impact indicator includes: noise condition and threshold setting, weighted impact calculation, weighted energy calculation and weighted count calculation.
The system for detecting the faults of the main bearing of the fan by using the stress wave technology comprises the following components: 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 main bearing of the fan and transmitting the 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 faults 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, the stress wave acquisition systems are uniformly distributed on the circumference of the main bearing of the fan by taking the main bearing of the fan as a center.
Optionally, 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 sequentially connected.
The invention discloses the following technical effects:
according to the method and the system for detecting the faults of the main bearing of the fan, provided by the invention, the stress wave is utilized for real-time online analysis, the amplitude and the duration of an impact event are measured, the severity of damage is quantitatively measured, fault prediction can be realized just before early degradation and change of equipment and before actual damage occur, the development trend of damage can be accurately predicted, early warning can be timely carried out, the accuracy and the timeliness of fault early warning are obviously improved, and the loss caused by the faults of the main bearing of the fan is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of detecting a fan main bearing fault by using stress waves in an embodiment of the invention;
FIG. 2 is a schematic diagram of a system for detecting a failure of a main bearing of a fan by using stress waves in an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating generation of a bearing failure impact signal according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a stress wave signal model in an embodiment of the invention;
FIG. 5 is a schematic diagram of the relative stages of various detection techniques in the health management of the whole life cycle of a device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of impact index acquisition in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The invention provides a method and a system for detecting faults of a main bearing of a fan by using a stress wave technology, wherein the system is shown in fig. 2 and comprises the following steps: the system comprises a plurality of stress wave acquisition systems, a data acquisition station, a communication module and a ground data station, wherein in the embodiment, the stress wave acquisition systems adopt stress wave sensors, and the communication module adopts 4G wireless equipment. The stress wave sensors are uniformly distributed on the circumference of the main bearing of the fan by taking the main bearing of the fan as the center. In this embodiment, taking 4 stress wave sensors as an example, 4 sensors are uniformly distributed on one circumference in the front bearing near the shaft. Firstly, a couplant is smeared at the bottom of the sensor to ensure good signal acquisition and coupling quality, then the bearing is tightly connected with the inner cavity through the fixation of the magnetic seat, and finally the installation of the stress wave sensor is completed. The stress wave sensor converts the stress wave generated by the acoustic 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 strength of the stress wave source and is between tens of microvolts and a few volts.
The stress wave sensor transmits the acquired main bearing stress wave data of the fan to the data acquisition station for storage, performs primary analysis on the acquired main bearing stress wave data of the fan, extracts time domain indexes, frequency domain indexes, impact indexes and the like, and the data acquisition station transmits the primary analysis result of the stress wave data to the ground data station through 4G wireless equipment, performs deep analysis on the primary analysis result of the stress wave data, and judges whether the main bearing of the fan has faults and fault severity. The ground data station comprises a receiving and storing module, a data analysis module and a display module, wherein after the stress wave data arrives at the ground data station, the receiving and storing module stores the stress wave data, the data analysis module carries out quantitative and qualitative analysis on the stress wave data, judges whether faults and fault degrees exist or not, and displays the analysis process and the analysis result through the display module.
The method for detecting the faults of the main bearing of the fan by using the stress wave technology, as shown in fig. 1, comprises the following steps:
s100, collecting original stress wave waveform data of the running main bearing of the fan.
In the running process of the main fan main shaft bearing, the stress state on the contact surface is complex, the fault source germination point is generally limited in a narrow area on the surface layer of the bearing, when the surface contact stress exceeds a certain value, the micro-vibration activity is locally shown on the surface, and stress wave signals are generated. The process of generating stress wave signals is analyzed from a microscopic view, the rolling bearing rolling body, the outer ring or the inner ring have certain roughness, and the collision of the protruding parts of the two rough surfaces generates stress wave signals. When there is a localized defect on the surface of the rolling element, as shown in fig. 3, the rolling elements will periodically collide with the fault point when passing through the raceway defect, so that a stress wave signal will be generated, which is characterized as a pulse of abrupt impact, in the form of ringing, and the frequency of the pulses of discrete abrupt impacts is the fault frequency. When the bearing breaks down, the sensor is excited with a certain characteristic frequency, and useful information is provided for fault diagnosis.
When a bearing in operation fails, the corresponding components will have their different characteristic frequencies. Let the number of rolling elements in the bearing be Z, the diameter of the rolling elements be D, the diameter of the pitch circle be D, the rotating speed of the main shaft be n, and the contact angle alpha. According to the basic parameter values of each part of the bearing, the characteristic frequency of the rolling bearing in theory when the rolling bearing breaks down can be obtained.
Rotation frequency f of spindle s As shown in formula (1):
characteristic frequency f of outer ring failure i As shown in formula (2):
characteristic frequency f of inner ring failure o As shown in formula (3):
characteristic frequency f of rolling element failure b As shown in formula (4):
characteristic frequency f of cage failure c As shown in formula (5):
the stress wave signal generating process is a process of elastic stress wave release. According to the generation mechanism of the stress wave signals analyzed in the previous section, the signals generated by the faults of the rolling bearing are burst pulse type stress wave signals, one stress wave signal is an 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 impact in fig. 3 is a waveform of a typical burst-type stress wave signal, which can be described in the time domain as a very "narrow" burst-type spike wave, which is generated mainly due to a relative collision from a split structure or an impact with an external object, etc.
The burst stress wave signal generation process is brief, and the generated signal reaches a maximum value rapidly and decays rapidly in a short time (hundreds of microseconds to milliseconds). For each burst stress wave signal, an exponential decay model may be used at any time t as shown in FIG. 4, the mathematical model of which may be represented by equation (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 the angular frequency of signal oscillation,for the initial phase angle, e is a mathematical constant, a base of a natural logarithmic function, e≡2.71828, t represents time.
Since there are a large number of microscopic protrusions on each of the two contact surfaces, the actual stress wave signal is caused by the mutual collision of these large numbers of protrusions, so the collected rolling bearing stress wave fault signal is a continuous stress wave signal formed by the superposition of a large number of transient burst type signals, as shown in the continuous signal in fig. 3, the mathematical model of which can be represented by the formula (7):
wherein N is the number of impact of the micro concave-convex body on the instantaneous surface, the value of the impact is a large number A i In order to be amplitude-value,is phase angle omega i For angular frequency, i is the number of the acquired transient burst pulse signals, and the value range of i is 0 to N, tau 1 Representing the continuous signal decay constant, the time required for the signal to decay from a maximum to the original 1/e.
And S200, screening the original stress wave data, and extracting normal stress wave data as a data source to be analyzed.
Setting a level threshold range of the acquired signal of each stress wave channel; if the original stress wave waveform data falls into the range of the grade threshold value, the original stress wave waveform data is determined to be normal data and is reserved; if the data fall outside the range of the level threshold, the data are determined to be abnormal data, and the abnormal data are discarded.
The main bearing of the fan is generally used for discarding stress wave signals of 30-80mv in the range of 6-14rpm, and data of < 30mv or data of > 80mv are regarded as abnormal data.
And S300, analyzing the normal stress waveform data, and judging whether the fan main bearing has faults and the fault degree.
Analysis of normal stress wave waveform data includes both quantitative and qualitative analysis.
Wherein the quantitative analysis comprises the steps of:
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 as formula (8):
where x (u) is the source signal, g (u-t) is the window signal, j is the imaginary part indication function, t is time, fu is frequency, u is the amplitude indication function, e is the mathematical constant, and e is the base of the natural logarithmic function, e.about.2.71828. The time window functions are multiplied by the source signals to realize windowing and translation near u, then the most obvious frequency in each window function is tracked in a matching way, namely the rotating speed frequency, and finally the rotating speed frequency is converted into a rotating speed value.
S312, performing spectrum analysis on the normal stress waveform data to acquire fault frequency.
And collecting a long waveform as an original waveform every half hour 24 hours in one day, and performing fault frequency analysis by using the fast Fourier transform (FFT spectrum analysis) original waveform to obtain the fault frequency.
S313, analyzing the rotating speed value and the fault frequency, and obtaining a quantitative analysis result of the main bearing of the fan, wherein the quantitative analysis result comprises no fault and faults.
The qualitative analysis includes the following steps:
s321, analyzing normal stress waveform data to obtain a time domain index and an impact index.
Time domain index:
kurtosis (Kurtosis) can reflect the impact characteristics of signals, is a dimensionless index, and can be used for measuring the strength of fault impact in acoustic emission waveforms; 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 acoustic emission signals; an impact factor (Impulse factor) is a peak-to-average ratio that can be used to evaluate the amplitude characteristics of an acoustic emission signal; the Margin coefficient (Margin factor) reflects the peak characteristics of the acoustic emission signal.
As shown in fig. 6, acquiring the impact index includes:
setting noise conditions and threshold:
the noise level is 50mv when the equipment is operated, and the noise level is converted into 68dB; and analyzing waveforms of a plurality of fault devices to determine impact generated by bearing faults.
Weighted impact calculation:
an acoustic emission signal exceeding a set threshold is counted as an impact, and each impact (spike) is counted as one time.
And (5) calculating weighted energy:
the energy can reflect the relative energy or intensity of the signal, the unit is mv.us, the size can reflect the intensity and frequency of the signal, and the energy can be used for evaluating the intensity and activity of acoustic emission. Therefore, the magnitude of the energy is counted in the detection, and the degree of the fault can be quantitatively determined to a certain degree. The area under the signal detection envelope is the weighted energy.
And (5) calculating a weighted count:
the weighted count refers to the number of oscillations exceeding a threshold signal, and the magnitude of the weighted count can roughly reflect the signal intensity and frequency, and is suitable for evaluating the acoustic emission activity. Here as a reference index for a quantitative evaluation.
S322, performing spectrum analysis on the normal stress waveform data to obtain a frequency domain index.
Frequency domain index:
peak factor: in the feature spectrogram, the method is used for evaluating impact characteristics near the fault feature frequency; the higher the peak factor, the more pronounced the peak corresponding to the fault signature frequency. Sparseness: 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 value near the characteristic frequency is, compared with a fault factor, the method has the advantages that the interference of multiple peaks near the characteristic frequency can be restrained, the smaller the amplitude is, and the obvious sparsity of the peak value is not greatly different from the unobvious peak value. Full distance coefficient: in the feature spectrogram, the signal to noise ratio corresponding to the feature frequency is evaluated, and because the fault feature frequency concerned in the embodiment is mostly in a low frequency band and is easy to be interfered by noise, in order to highlight the definition degree of the peak corresponding to the fault feature frequency, a full-distance coefficient is introduced, and the advantage is that noise interference can be suppressed.
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 index, wherein the qualitative analysis result comprises slight faults, faults and normal faults.
Firstly, calculating a time domain index and a frequency domain index of each group of data samples and constructing a feature vector; secondly, dimension reduction is carried out on the feature vector through a data compression technology, and nonlinear relations among all data samples are maintained; and finally, normalizing the feature vector after dimension reduction and inputting the feature vector into the constructed classification model for degradation evaluation analysis.
And (5) synthesizing a quantitative analysis result and a qualitative analysis result, and finally judging the fault condition of the main bearing of the fan.
The invention has obvious advantages in the health management of the whole life cycle of the equipment by using the stress wave technology to perform the fault detection and vibration detection technology, the pulse and other high-frequency detection technology, the lubricating oil analysis technology and the smoke alarm technology of the main bearing of the fan, as shown in figure 5. Aiming at the stress wave technology generated by friction and impact, the related state can be found immediately after equipment fails or even at the beginning of the condition of 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 of the whole life cycle.
Unlike vibration detection techniques, stress waves are acoustic-based techniques that enable diagnostic and assessment analysis to be made at the earliest time for equipment faults and operating conditions, i.e., early prediction and reporting of early degradation changes in the equipment just before actual damage has occurred. The design of the stress wave energy analysis system is to overcome the problem which has plagued the effectiveness of vibration technology. The fundamental disadvantage of vibration technology is that conventional vibration accelerometers cannot distinguish the subtle signals generated by incipient damage from those generated by normal operation of the device itself. Any device has its own vibrations, and this vibration is far stronger than the abnormal vibrations caused by the initial damage of the device, and the stress wave technique can completely ignore the vibrations generated by the dynamic motion of the device itself, focusing only on the abnormal impact and friction events. Once detected, it is subjected to measurement analysis and a trend report is formed therefrom that the device health condition may deteriorate over time. Moreover, when the equipment operates at a lower working speed, the vibration technology is more incapacitated, because the energy released by the defect at the 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 the impact events generated inside the device, indicating the presence of damage, but not the extent of damage; stress wave technology can measure the magnitude and duration of impact events, quantitatively measuring the severity of damage: area/size of equipment damage caused by impact and friction.
Compared with stress wave energy analysis, the diagnosis accuracy of the lubricating oil analysis technology is very low, the specific part from which scraps come cannot be determined, and the actual prediction of the residual service life of equipment cannot be made; whereas stress wave analysis is real-time online and does not require time and effort consuming sample collection and handling activities, stress wave peak amplitude histograms can be used for lubrication troubleshooting, which clearly indicates whether lubrication problems or other random events are responsible for the rise in stress wave energy.
Based on the characteristics different from other detection technologies, the stress wave detection technology can realize prospective equipment management, namely, failure prediction is realized just before early degradation change of equipment and actual damage does not occur. The stress wave technology not only can discover problems in early stage, but also can accurately predict the development trend of damage, and reminds operators when the damage reaches a certain degree; for equipment which needs to be shut down to implement overhaul, enough time is provided for users to make maintenance plans, purchase parts and organize technicians, so that the equipment can be maintained according to conditions. Stress wave technology offers great advantages in the foreign aviation, marine, automotive, petrochemical, electrical, etc. industries, in terms of availability, reliability and reduced incipient faults.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.
Claims (5)
1. The method for detecting the faults of the main bearing of the fan by using the stress wave technology is characterized by comprising the following steps of:
collecting original stress waveform data of the operation of a main bearing of the fan;
screening the original stress waveform data, and extracting normal stress waveform data;
screening the raw stress waveform data includes:
setting a level threshold range of the acquired signal of each stress wave channel;
if the original stress waveform data fall within the range of the grade threshold value, the original stress waveform data are determined to be normal data and are reserved; if the data fall outside the range of the level threshold, the data are determined to be abnormal data, and the abnormal data are discarded;
analyzing the normal stress waveform data and judging whether the fan main bearing has faults or not;
analyzing the normal stress waveform data comprises fault quantitative analysis and fault qualitative analysis;
the quantitative analysis of the faults comprises the following steps:
performing rotational speed estimation on the normal stress waveform data to obtain a rotational speed value;
performing spectrum analysis on the normal stress waveform data to obtain fault frequency;
analyzing the rotating speed value and the fault frequency to obtain a quantitative analysis result of the main bearing of the fan, wherein the quantitative analysis result comprises no fault and faults;
the qualitative fault analysis comprises the following steps:
analyzing the normal stress waveform data to obtain a time domain index and an impact index;
performing 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,
based on the comprehensive index, analyzing to obtain a qualitative analysis result of the main bearing of the fan, wherein the qualitative analysis result comprises slight faults, faults and normal faults;
the obtaining the impact index includes: noise condition and threshold setting, weighted impact calculation, weighted energy calculation and weighted count calculation.
2. The method for detecting faults of a main bearing of a fan by using stress wave technology according to claim 1, wherein the calculation method of the rotating speed value is as follows:
wherein (1)>As source signal +.>For window signal +.>Indicating a function for the imaginary part>For time (I)>For frequency +.>For the amplitude indication function, +.>The mathematical constants are the bases of natural logarithmic functions.
3. A system for detecting a failure of a main bearing of a wind turbine using stress wave technology, the system being adapted to implement a method for detecting a failure of a main bearing of a wind turbine using stress wave technology as claimed in any one of claims 1-2, 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 waveform data of the main bearing of the fan and transmitting the data to the data acquisition station;
the data acquisition station is used for collecting and storing the stress waveform data and transmitting the stress waveform data to the ground data station;
the ground data station is used for analyzing the stress waveform data and judging faults 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.
4. A system for detecting a failure of a fan main bearing using stress wave technology according to claim 3, wherein a plurality of the stress wave collecting systems are uniformly distributed on the circumference of the fan main bearing centering around the fan main bearing.
5. A system for detecting a failure of a main bearing of a wind turbine using stress wave technology according to claim 3, 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 sequentially connected.
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Citations (4)
| 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 |
| 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 |
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Patent Citations (4)
| 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 |
| 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 |
|---|
| 基于分形-小波的低速轴承磨损故障物理特性研究;王楠;中国博士学位论文全文数据库（电子期刊）工程科技II辑;第13-26、38、52-60、67-72、90-91页 * |
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