CN114152443A - Embedded acoustic intelligent bearing and monitoring and diagnosing method thereof - Google Patents
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- CN114152443A CN114152443A CN202111621763.4A CN202111621763A CN114152443A CN 114152443 A CN114152443 A CN 114152443A CN 202111621763 A CN202111621763 A CN 202111621763A CN 114152443 A CN114152443 A CN 114152443A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 238000000034 method Methods 0.000 title claims abstract description 13
- 230000004927 fusion Effects 0.000 claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims abstract description 11
- 238000003745 diagnosis Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 15
- 239000000654 additive Substances 0.000 claims description 7
- 230000000996 additive effect Effects 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 4
- 230000017105 transposition Effects 0.000 claims description 3
- 238000002405 diagnostic procedure Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 claims 1
- 230000008447 perception Effects 0.000 claims 1
- 230000005236 sound signal Effects 0.000 abstract description 2
- 230000005284 excitation Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000004026 adhesive bonding Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
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- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
Abstract
The invention provides an embedded acoustic intelligent bearing and a monitoring and diagnosing method thereof, wherein the intelligent bearing comprises a bearing body and M embedded acoustic sensing bolts embedded and installed on the bearing body, M is an integer larger than or equal to 12, and the M embedded acoustic sensing bolts form an acoustic sensing bolt array; the embedded acoustic sensing bolt comprises a bolt body which can be connected with the bearing body to play a role in connection and fastening, and an acoustic monitoring unit which is embedded in the bolt body and comprises a sound sensor, a wireless data transmission module connected with a signal output end of the sound sensor, and a power supply module for supplying power to the sound sensor and the wireless data transmission module. According to the invention, M embedded acoustic sensing bolts are arranged on the wind power bearing to form an acoustic sensing bolt array, and a plurality of sound signals with time difference are processed in a fusion manner, so that the fault misjudgment probability can be reduced, and the intelligent diagnosis of the wind power bearing is realized.
Description
Technical Field
The invention belongs to the technical field of intelligent bearings, and particularly relates to an embedded acoustic intelligent bearing and a monitoring and diagnosing method thereof.
Background
The rolling bearing is a key part widely applied to the rotating machinery and is also a vulnerable part. In the wind turbine generator system, because the bearing service environment is bad, the maintenance cost is high, in order to avoid the halt caused by the fault and further to cause larger economic loss, the health state of the wind turbine bearing is very necessary to be monitored.
The wind power bearing has the characteristics of large structure size, many parts, severe service environment, strong impact, high load and low rotating speed, and has the following excitation characteristics: firstly, due to the fact that fault excitation distribution is discrete and random, after multi-path transmission, signal attenuation and strong background noise interference, a sensor far away from fault impact cannot effectively sense fault information; secondly, the wind power main shaft bearing is driven by external uncertain and strong nonlinear wind power, the rotating speed of the wind power main shaft bearing is slow, and the wind power main shaft bearing has strong fluctuation characteristics, a fixed sensor cannot effectively sense local fault excitation in a global range generally, and can only capture instantaneous local impact for many times in a sensing visual field interval to form false components, so that the fault cannot be detected by a conventional diagnosis method based on a small amount of vibration, strain and ultrasonic sensors; and thirdly, the multi-sensor sensing method based on a large number of vibration, strain and ultrasonic sensors is limited by a structural contact sensing mode, and is interfered by multi-path, strong noise and multi-coupling formed around the wind power bearing and multi-source strong excitation of a large-scale structure of a wind power body, and target information focusing cannot be effectively formed, so that efficient intelligent diagnosis of the wind power bearing with large size, strong impact, high load, strong noise and low rotating speed can be completed.
Disclosure of Invention
The present invention is directed to solving the problems of the prior art, and a first object of the present invention is to provide an embedded acoustic smart bearing. The second purpose of the invention is to provide a monitoring and diagnosing method using the embedded acoustic intelligent bearing.
In order to achieve the first purpose, the invention adopts the following technical scheme: an embedded acoustic intelligent bearing comprises a bearing body and M embedded acoustic sensing bolts embedded and installed on the bearing body, wherein M is an integer larger than or equal to 12, and the M embedded acoustic sensing bolts form an acoustic sensing bolt array; the embedded acoustic sensing bolt comprises a bolt body which can be connected with the bearing body to play a role in connection and fastening, and an acoustic monitoring unit which is embedded in the bolt body and comprises a sound sensor, a wireless data transmission module connected with a signal output end of the sound sensor, and a power supply module for supplying power to the sound sensor and the wireless data transmission module.
According to the technical scheme, M embedded acoustic sensing bolts are installed on a bearing (such as a wind power bearing) to form an acoustic sensing bolt array, a plurality of sound signals with time differences are processed in a fusion mode, the fault misjudgment probability can be reduced, and intelligent diagnosis of the wind power bearing is achieved.
In a preferred embodiment of the invention, the sound hole of the sound sensor is directed to the air side and toward the center of the bearing body. The test result is more accurate.
In a preferred embodiment of the present invention, the head of the bolt body is provided with a groove, and the acoustic monitoring unit is embedded in the groove. The mode of installing the acoustic monitoring unit is simple, so that the structure of the embedded acoustic sensing bolt is simple.
In a preferred embodiment of the invention, the M embedded acoustic sensing bolts are circumferentially spaced around the center of the bearing body. And the structure is more accurate due to the uniform arrangement.
In order to achieve the first purpose, the invention adopts the following technical scheme: a monitoring and diagnosing method for an embedded acoustic intelligent bearing is characterized in that a plane where a plurality of acoustic monitoring units are located is an x-y plane, a spherical coordinate system is adopted to represent the arrival direction of incident waves, and an origin O of the coordinate system is located at the center of a bearing body;
assuming that there are D signal sources and the array consists of M omnidirectional array elements, the array received signal can be expressed as:
X(t)＝AS(t)+N(t) (1)
in formula (1), s (t) is a signal matrix, and its expression is as follows:
formula (1), a is an array prevalence matrix, and its expression is as follows:
equation (1), n (t) is an additive noise matrix, and its expression is as follows:
N(t)＝[n1(t),n2(t),......,nM(t)]T (4)
in equations (2) to (4), i is the signal source number, t is time, and si(t) is the ith signal, which reaches the center of the array]TIs the transposition of the matrix;
zi(t) is the complex envelope of the signal;
ω0＝2πf0is the phase of the signal; f. of0Is the natural frequency;
ξ＝krsinθ，k＝2π/λ0，λ0the wavelength is, r is the radius of the circular array, and theta is the included angle between the line from the sound source to the original point and the axis direction of the intelligent bearing;
φi∈[0,2π]representing the included angle between the projection of the connecting line from the origin of the coordinate system to the ith sound source on the x-y plane and the x axis;
nm(t) denotes additive noise of the mth array element, M ═ 1, 2.
In the technical scheme, a plurality of embedded acoustic sensing bolts are arranged according to a certain mode, and an acoustic sensing bolt array with space focusing performance and good anti-noise capability is formed. By selecting a proper self-adaptive algorithm, space beams are dynamically formed, interference can be suppressed to the maximum extent by array output, and an expected signal is finally obtained. Therefore, the acoustic sensing bolt array is applied to the wind power bearing, a plurality of acoustic signals with time difference are processed in a fusion mode, the fault misjudgment probability can be reduced, and intelligent diagnosis of the wind power bearing is achieved.
In a further preferred embodiment of the invention, the weight vector W is used to determine the weight of the vectorHCarrying out weighted summation on the components of the array receiving signal vector X (k) on each array element, and carrying out denoising treatment to obtain a denoising fusion signal y (k):
the output denoising fusion signal y (k) can be used for monitoring and intelligently diagnosing the state of the wind power large bearing.
In another preferred embodiment of the invention, the weight vector WHThe determination may be made by a generalized sidelobe canceller, a minimum undistorted response, or a least mean square algorithm.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic structural diagram of an embedded acoustic smart bearing according to an embodiment of the present application.
Fig. 2 is a schematic perspective view of an embedded acoustic sensing bolt in the embodiment.
Fig. 3 is a schematic top view of an embedded acoustic sensing bolt in an embodiment.
Fig. 4 is a schematic diagram of a circular array model.
Reference numerals in the drawings of the specification include: the bearing comprises a bearing body 10, an embedded acoustic sensing bolt 20, a bolt body 21, a groove 211, an acoustic monitoring unit 22, a sound sensor 221, a wireless data transmission module 222, a power supply module 223 and a sound hole facing to a.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "vertical", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The invention provides an embedded acoustic intelligent bearing and a monitoring and diagnosing method of the embedded acoustic intelligent bearing. As shown in FIGS. 1-3, in a preferred embodiment of the present invention, the smart bearing comprises a bearing body 10, and M embedded acoustic sensing bolts 20 embedded in the bearing body 10, wherein M is an integer greater than or equal to 12. Preferably, the M embedded acoustic sensing bolts 20 are circumferentially distributed at intervals with the center of the bearing body 10 as a center, that is, the M sound sensors 221 are uniformly arranged on a circumference with a radius r to form an acoustic sensing bolt circle array with the radius r.
The embedded acoustic sensing bolt 20 comprises a bolt body 21 which can be connected with the bearing body 10 for connection and fastening, and an acoustic monitoring unit 22 which is embedded on the bolt body 21. Preferably, the head of the bolt body 21 is provided with a groove 211, and the acoustic monitoring unit 22 is embedded in the groove 211, for example, the acoustic monitoring unit 22 is integrated in the groove 211 by means of glue bonding and is mounted on the wind power bearing along with the bolt body 21.
Specifically, the acoustic monitoring unit 22 includes a sound sensor 221, a wireless data transmission module 222 connected to a signal output terminal of the sound sensor 221, and a power supply module 223 supplying power to the sound sensor 221 and the wireless data transmission module 222, and preferably, a sound hole of the sound sensor 221 is directed toward (mark a in fig. 1) the air side and toward the center of the bearing body 10.
According to the invention, the bolt body 21 is obtained by modifying the original bolt for fixing the bearing, and the bolt for installing the acoustic monitoring unit 22 is not required to be additionally arranged, namely the original bolt of the bearing is utilized.
The embedded acoustic intelligent bearing can adopt the following monitoring and diagnosis method, as shown in fig. 4, a plane where an acoustic sensing bolt circular array (array for short) is located is assumed to be an x-y plane, a spherical coordinate system is adopted to represent the arrival direction of incident waves, and the origin O of the coordinate system is located at the center of the array. Assuming that there are D signal sources and the array consists of M omnidirectional array elements, the array received signal can be expressed as:
X(t)＝AS(t)+N(t) (1)
in formula (1), s (t) is a signal matrix, a is an array popularity matrix, and n (t) is an additive noise matrix;
specifically, the expression of the signal matrix s (t) is as follows:
specifically, the expression of the array prevalence matrix a is as follows:
specifically, the expression of the additive noise matrix n (t) is as follows:
N(t)＝[n1(t),n2(t),......,nM(t)]T (4)
in equations (2) to (4), i is the signal source number, t is time, and si(t) is the ith signal, which reaches the center of the array]TIs the transposition of the matrix;
z (t) is the complex envelope of the signal;
ω0＝2πf0is the phase of the signal, f0Is the natural frequency;
ξ＝krsinθ，k＝2π/λ0，λ0the wavelength is, r is the radius of the circular array, and theta is the included angle between the line from the sound source to the origin and the axis direction (the Z-axis direction in FIG. 3) of the intelligent bearing;
φi∈[0,2π]representing the included angle between the projection of the connecting line from the origin of the coordinate system to the ith sound source on the x-y plane and the x axis;
nm(t) denotes additive noise of the mth array element, M ═ 1, 2.
By a weight vector WHThe components of the array receiving signal vector X (k) on each array element are weighted and summed, and are denoised, so that space can be realizedFocusing, improving the signal-to-noise ratio, and obtaining a de-noised fusion signal y (k):
the noise fusion signal y (k) output by the method can be used for monitoring the state of the wind power large bearing and intelligently diagnosing the state of the wind power large bearing. It should be noted that, when the actual signal is switched to be processed, x (t) in the foregoing formula (1) is replaced by x (k), and the corresponding de-noised fusion signal is represented by y (k).
The noise fusion signal y (k) is the focus signal, the focus indicates that the signal enhances the direction of interest, while suppressing noise and interference; the denoising fusion signal y (k) has directivity, can roughly judge the fault direction and assists in bearing state monitoring and fault diagnosis.
In equation (5), the weight vector WHHaving different responses to signals from different directions, thereby forming different spatial beams, the weight vector WHThe determination may be made, but is not limited to, by Generalized Sidelobe Canceller (GSC), minimum distortion free response (MVDR), or Least Mean Square (LMS) algorithm.
In the description herein, reference to the description of the terms "preferred embodiment," "one embodiment," "some embodiments," "an example," "a specific example" or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. An embedded acoustic intelligent bearing is characterized in that the intelligent bearing comprises a bearing body and M embedded acoustic sensing bolts embedded in the bearing body, wherein M is an integer larger than or equal to 12, and the M embedded acoustic sensing bolts form an acoustic sensing bolt array;
the embedded acoustic perception bolt comprises a bolt body and an acoustic monitoring unit, wherein the bolt body can be connected with the bearing body to achieve a connection and fastening effect, the acoustic monitoring unit is embedded in the bolt body and comprises a sound sensor, a wireless data transmission module and a power supply module, the wireless data transmission module is connected with a signal output end of the sound sensor, and the power supply module supplies power to the sound sensor and the wireless data transmission module.
2. An embedded acoustic smart bearing according to claim 1 wherein the acoustic hole of the acoustic sensor is directed to the air side and to the center of the bearing body.
3. The embedded acoustic intelligent bearing of claim 1, wherein the head of the bolt body is provided with a groove, and the acoustic monitoring unit is embedded in the groove.
4. The embedded acoustic intelligent bearing of any one of claims 1-3, wherein M embedded acoustic sensing bolts are circumferentially spaced around the center of the bearing body to form a circular array of acoustic sensing bolts with radius r.
5. A monitoring and diagnosing method for the embedded acoustic intelligent bearing as claimed in claim 4, wherein, assuming that the plane where the acoustic monitoring units are located is an x-y plane, a spherical coordinate system is adopted to represent the arrival direction of incident waves, and the origin O of the coordinate system is located at the center of the bearing body;
assuming that there are D signal sources and the array consists of M omnidirectional array elements, the array received signal can be expressed as:
X(t)＝AS(t)+N(t) (1)
in formula (1), s (t) is a signal matrix, and its expression is as follows:
formula (1), a is an array prevalence matrix, and its expression is as follows:
equation (1), n (t) is an additive noise matrix, and its expression is as follows:
N(t)＝[n1(t),n2(t),......,nM(t)]T (4)
in equations (2) to (4), i is the signal source number, t is time, and si(t) is the ith signal, which reaches the center of the array]TIs the transposition of the matrix;
zi(t) is the complex envelope of the signal;
ω0＝2πf0is the phase of the signal; f. of0Is the natural frequency;
ξ＝krsinθ，k＝2π/λ0，λ0the wavelength is, r is the radius of the circular array, and theta is the included angle between the line from the sound source to the original point and the axis direction of the intelligent bearing;
φi∈[0,2π]representing the included angle between the projection of the connecting line from the origin of the coordinate system to the ith sound source on the x-y plane and the x axis;
nm(t) denotes additive noise of the mth array element, M ═ 1, 2.
6. The monitoring and diagnostic method of claim 5, wherein the weight vector W is used to determine the weight of the patientHCarrying out weighted summation on the components of the array receiving signal vector X (k) on each array element, and carrying out denoising treatment to obtain a denoising fusion signal y (k):
the output denoising fusion signal can be used for state monitoring and intelligent diagnosis of the wind power large bearing.
7. The method of claim 6, wherein the weight vector is determined by a generalized sidelobe canceller, a minimum undistorted response, or a least mean square algorithm.
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Cited By (1)
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