CN108444715A - Bearing state diagnostic method, device, storage medium and electronic equipment - Google Patents
Bearing state diagnostic method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
This disclosure relates to a kind of bearing state diagnostic method, device, storage medium and electronic equipment, this method includes obtaining the vibration signal of bearing；Vibration signal is subjected to local mean value decomposition；Feature vector of one group of dimensionless group as the vibration signal is extracted from the vibration signal after local mean value decomposition；Feature vector is input in preset radial basis function neural network and is handled, one group of real output value is obtained；Range where the numerical value of each real output value in one group of real output value determines the Status Type of bearing.Since part mean decomposition method is just being suitable for this non-gaussian of vibration signal of wind power generating set, non-stationary and nonlinear signal, and use feature vector of the dimensionless group not influenced by factors such as bearing load and rotating speeds as condition diagnosing, and it is analyzed in conjunction with radial basis function neural network, it enables to diagnostic result more accurate, and makes use enhanced convenience in practical applications quick.
Description
Technical Field
The present disclosure relates to the field of bearing detection, and in particular, to a bearing state diagnosis method and apparatus, a storage medium, and an electronic device.
Background
The bearing is widely used in various fields, and a bearing structure is required to be used in a plurality of mechanical structures, for example, a fixed bearing is required to be used in a wind generating set. The health state detection and diagnosis of the wind generating set can be mainly judged according to the state of a bearing in the set. Generally, the state of the bearing in the wind turbine generator set is obtained by detecting and analyzing a vibration signal of the bearing, and the state of the wind turbine generator set is determined based on the state of the bearing.
In the initial stage of analysis and study, a time domain analysis method is often used for processing and analyzing a vibration signal of a bearing. However, since the natural characteristics of wind turbines are slow and non-stationary, and at least for large wind turbines, the vibration signals usually contain a large amount of noise and other disturbances, the results of the data are not very satisfactory, and therefore, in recent years, time domain analysis is hardly used for processing the vibration signals of the bearings.
Considering that when a bearing or a gear of a transmission system of a wind turbine fails, vibration is generated, and the frequency of the vibration depends on the rotating speed of a shaft, the type parameters of the bearing and the like, based on the characteristics of the vibration signal, the vibration signal can be better processed by using frequency domain analysis, such as Fast Fourier Transform (FFT), which is the most basic and commonly used method in the frequency domain analysis. However, there are multi-part coupled vibrations in the wind turbine, and noise interference of working vibration is also large, and vibration is generally non-gaussian, non-stationary and non-linear signal, and for these characteristics of vibration signal, the processing of vibration signal by fast fourier transform still has defects.
Therefore, researchers have proposed new time-frequency analysis methods, such as wavelet analysis, which can be applied to vibration signals with discrete and nonlinear signal characteristics, and can truly apply the time-frequency analysis to the vibration signals. However, in the actual application process, the wavelet analysis technology still has certain disadvantages. For example, the corresponding wavelet basis must be selected during the analysis process, but the wavelet basis is not dynamically changed but is a fixed function, so that the adaptability of the wavelet basis is still poor. When the wavelet bases are fixed, they cannot be changed in the whole analysis process, which easily causes the analysis effect of some frequency bands in the vibration signal to be poor.
Therefore, in view of the disadvantages of wavelet analysis, some researchers have proposed to decompose signals by using an Empirical Mode Decomposition (EMD) method to solve the problem of poor signal analysis effect on some frequency bands in wavelet analysis, however, the interference of this method on a certain specific signal is relatively large, and under certain conditions, this method may cause serious consequences such as end point and aliasing effect.
Disclosure of Invention
The purpose of the present disclosure is to provide a bearing state diagnosis method, device, storage medium, and electronic apparatus, which can greatly improve the accuracy of bearing state diagnosis.
In order to achieve the above object, the present disclosure provides a bearing condition diagnosis method, the method including:
acquiring a vibration signal of a bearing, wherein the vibration signal comprises an acceleration signal of the bearing in at least one direction;
performing local mean decomposition on the vibration signal;
extracting a group of dimensionless parameters from the vibration signal subjected to the local mean decomposition to be used as a feature vector of the vibration signal;
inputting the characteristic vector into a preset radial basis function neural network for processing to obtain a group of actual output values;
and determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
Optionally, the method further comprises:
and before the local mean decomposition is carried out on the vibration signal, carrying out smooth denoising processing on the vibration signal by using a low-pass filter.
Optionally, the dimensionless parameter includes: a peak factor, a form factor, a pulse factor, a margin factor, and a kurtosis factor.
Optionally, the status types include: normal, inner ring failure, outer ring failure, ball failure, and cage failure.
Optionally, the determining the state type of the bearing according to the range in which the numerical value of each actual output value in the group of actual output values is located includes:
and judging the state type corresponding to the actual output value with the value closest to the preset threshold value in the group of actual output values as the state type of the bearing.
Optionally, before the step of determining the state type of the bearing according to the range of the value of each actual output value in the set of actual output values, the method further includes:
judging whether an actual output value with a value within a preset range exists in the group of actual output values or not;
the determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values comprises:
and when the actual output values of which the numerical values are in the preset range do not exist in the group of actual output values, determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
Optionally, the method further comprises:
and returning to the step of acquiring the vibration signal of the bearing when the actual output value with the value within the preset range exists in the group of actual output values.
The present disclosure also provides a bearing condition diagnosis device, the device including:
the device comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a vibration signal of a bearing, and the vibration signal comprises an acceleration signal of the bearing in at least one direction;
the decomposition module is used for carrying out local mean decomposition on the vibration signal;
the characteristic vector extraction module is used for extracting a group of dimensionless parameters from the vibration signals decomposed by the decomposition module to be used as the characteristic vectors of the vibration signals;
the neural network processing module is used for inputting the characteristic vector into a preset radial basis function neural network for processing to obtain a group of actual output values;
and the state type determining module is used for determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
Optionally, the apparatus further comprises:
and the optimization module is used for performing smooth denoising processing on the vibration signal by using a low-pass filter before the decomposition module performs local mean decomposition on the vibration signal.
Optionally, the dimensionless parameter includes:
a peak factor, a form factor, a pulse factor, a margin factor, and a kurtosis factor.
Optionally, the status types include: normal, inner ring failure, outer ring failure, ball failure, and cage failure.
Optionally, the state type determining module is configured to:
and determining the state type corresponding to the actual output value with the value closest to the preset threshold value in the group of actual output values as the state type of the bearing.
Optionally, the apparatus further comprises:
the judging module is used for judging whether an actual output value of which the numerical value is in a preset range exists in the group of actual output values or not before the state type determining module determines the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values;
the state type determination module is to:
and when the actual output values of which the numerical values are in the preset range do not exist in the group of actual output values, determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
Optionally, the determining module is further configured to:
and when the actual output values of which the numerical values are in the preset range exist in the group of actual output values, triggering the acquisition module to acquire the vibration signals of the bearing.
The present disclosure also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the bearing condition diagnosing method described above.
The present disclosure also provides an electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the bearing diagnostic status method described above.
According to the technical scheme, the vibration signal of the bearing is obtained to diagnose the state of the bearing, the vibration signal is subjected to local mean decomposition, dimensionless parameters are extracted and used as characteristic vectors input into the radial basis function neural network, and finally the real state of the bearing is obtained by comparing and judging the result output by the radial basis function neural network. Thus, the local mean decomposition method is suitable for non-Gaussian, non-stationary and non-linear signals, and the vibration signal of the bearing of the wind generating set is the signal with the characteristic; dimensionless parameters in time domain analysis are selected as the characteristic vector of state diagnosis, so that the diagnosis result is not influenced by factors such as bearing load, rotating speed and the like, a relative standard value is not required to be considered, the previous data is not required to be referred, the influence of the absolute level of a vibration signal is avoided, and the diagnosis result is not influenced even if the measuring points at each time are different; the radial basis function neural network has the unique optimal approximation function and can solve the problem of local minimum, so that the diagnosis result is more accurate, and the radial basis function neural network has the characteristic of high learning convergence rate, so that the radial basis function neural network is more convenient and rapid to use in practical application.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a bearing condition diagnostic method according to an exemplary embodiment.
FIG. 2 is a flow chart illustrating a bearing condition diagnostic method according to yet another exemplary embodiment.
FIG. 3 is a flow chart illustrating a bearing condition diagnostic method according to yet another exemplary embodiment.
Fig. 4 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to an exemplary embodiment.
Fig. 5 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to still another exemplary embodiment.
Fig. 6 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to still another exemplary embodiment.
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 8 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
Fig. 1 illustrates a bearing condition diagnosis method according to an exemplary embodiment, as shown in fig. 1, the method including steps 101 to 105. The bearing may be any bearing that requires condition diagnosis in any machine, for example, a bearing used in a wind turbine generator system.
In step 101, a vibration signal of a bearing is acquired, wherein the vibration signal comprises an acceleration signal of the bearing in at least one direction. For example, the vibration signal may be an acceleration signal a in the X-direction in a three-dimensional coordinate system on the bearingxOr acceleration signal a in the Y directionyOr an acceleration signal a in the Z directionz(ii) a In addition, the vibration signal may be an acceleration vector a obtained by synthesizing acceleration signals in two directions of an X direction and a Y direction in a three-dimensional coordinate system on the bearing, wherein the acceleration signals are obtained by combining the acceleration signals in the two directions of the X direction and the Y directionOr an acceleration vector a obtained by synthesizing acceleration signals in two directions of an X direction and a Z direction in a three-dimensional coordinate system on the bearing, whereinThe acceleration vector a can also be obtained by synthesizing acceleration signals in two directions of Y direction and Z direction in a three-dimensional coordinate system on the bearing, whereinIn addition, the vibration signal can also be an acceleration vector a synthesized by acceleration signals in three directions of an X direction, a Y direction and a Z direction in a three-dimensional coordinate system on the bearing, wherein,wherein, the three-dimensional coordinate system on the bearing can be established according to the installation position of the bearingThe coordinate system of (2) may also be a coordinate system established by other reference objects, and the coordinate system is not limited in the present disclosure as long as it is sufficient to acquire an acceleration signal of the bearing in at least one direction in space.
Wherein, the vibration signal can be obtained by any one of a three-axis acceleration sensor, a two-axis acceleration sensor or an acceleration sensor mounted on the bearing.
In step 102, the vibration signal is subjected to local mean decomposition.
Local Mean Decomposition (LMD) is a method for processing non-stationary signals, which can decompose signals, decompose the signals into signals with different envelopes and frequency modulations under various scales, and multiply the two signals to obtain a Product Function (PF), and the instantaneous frequency of the Product Function has physical significance, so that the analysis of complex non-stationary signals can be effectively realized. That is, the local mean decomposition is a process of gradually decomposing a high frequency signal into a low frequency signal. The method utilizes a specific method to convert a very complex signal containing a plurality of different components into a single component step by step, and facilitates the subsequent further analysis of the signal. The main process is as follows:
(1) firstly, the derivation operation is completed on the signal x (t) needing to be subjected to local mean decomposition, so as to obtain the signal n of all extreme points of the signal x (t)i(i ═ 1,2,3, Λ), where t is the time variable in signal x (t).
(2) Calculating every two adjacent extreme points n in all the extreme pointsiAnd ni+1Average value m ofiAnd an envelope estimation value aiWherein the average value miAnd an envelope estimate aiThe calculation formula of (a) is as follows:
and
obtaining the average value miAnd an envelope estimate aiThen, each average value m obtained is expressed by a straight lineiAnd an envelope estimation value aiRespectively connected, and then smoothing the two connected graphs by using a moving average method to obtain a local mean function m of the signal x (t)11(t) and an envelope estimation function a11(t)。
(3) The local mean function m11(t) separating from the signal x (t) to obtain h11(t) signal, formula:
h11(t)＝x(t)-m11(t)。
(4) using the h obtained11(t) division of the signal by an envelope estimation function a11(t) to h11(t) signal conditioning to obtain conditioned signal s11(t), the formula is as follows:
calculating a signal s11Envelope estimation function a of (t)12(t) when the envelope estimation function a12(t) satisfies a12When (t) is 1, signal s11(t) is a pure frequency modulated signal; when the envelope estimation function a12(t) does not satisfy a12When (t) is 1, the signal s is transmitted11(t) repeating the above steps again as the original signal x (t) until the signal s1n(t) is a pure frequency-modulated signal, i.e. s is 1. ltoreq. s1nWhen (t) is less than or equal to 1, stopping circulation, and at the moment, the envelope estimation function a of the loop is estimated1(n+1)Satisfies a1(n+1)1, the specific calculation formula is as follows:
and
(5) calculating the product of all envelope estimation functions to obtain envelope signal a1(t), the calculation formula is as follows:
(6) envelope signal a1(t) by the pure FM signal s1n(t) obtaining a first PF component, wherein a specific calculation formula is as follows:
PF1＝a1(t)s1n(t)。
(7) PF the first component1Separating from the signal x (t) to obtain a new signal c1(t) and combining the signal c1(t) repeating the above steps as the original signal, and repeating the above steps m times until the signal cm(t) is less than or equal to 1, thus completing the decomposition of the signal x (t) into m PF components and cmThe sum, as shown in the following equation:
wherein c ism(t) is a monotonic function that indicates the trend of the signal.
At present, signals are often decomposed by using an Empirical Mode Decomposition (EMD) method, but compared with the local mean Decomposition method, the length of an unknown envelope near the end points of the signals of the local mean Decomposition is shorter than that of the method of the Empirical Mode Decomposition, which is helpful for improving the precision of the Decomposition processing; when some special signals appear in the processed signals, the decomposition result obtained by the local mean decomposition is not interfered by the endpoint effect, for example, when the processed signals are signals with extreme endpoints; in addition, the diffusion speed of the endpoint effect of the local mean decomposition is slower than that of the empirical mode decomposition, so the accuracy of the local mean decomposition is higher than that of the empirical mode decomposition.
In step 103, a set of dimensionless parameters is extracted from the vibration signal after the local mean decomposition as a feature vector of the vibration signal. The feature vector can reflect the parameters of the bearing state. Because the vibration signal has stronger randomness, the parameters in the vibration signal are subjected to statistical calculation and are converted into the characteristic vectors capable of reflecting the state of the bearing, so that the state of the bearing is analyzed and diagnosed, and the current state of the bearing can be reflected more accurately.
The dimensionless parameter is one type of time domain characteristic parameter, the time domain characteristic parameter also comprises a dimensionless parameter, but the dimensionless parameter index depends on historical data, is sensitive to the change of factors such as load, rotating speed and the like, and is not suitable for a vibration signal of a bearing in a wind generating set, so the dimensionless parameter is selected as a characteristic vector for representing the state of the bearing. The dimensionless parameter index is not influenced by factors such as load, rotating speed and the like, does not need to consider relative standard values, does not need to be compared with the conventional data, and is not influenced by the absolute level of a signal.
The dimensionless parameter has a stronger sensitivity in an early stage of a bearing failure, but the effect is not stable, and thus, in the bearing state diagnosis method according to an exemplary embodiment of the present disclosure, it is combined with a local mean decomposition method to analyze a vibration signal of the bearing.
In step 104, the feature vectors are input into a preset radial basis function neural network for processing, so as to obtain a set of actual output values.
The preset Radial basis function neural network is a Radial Basis Function (RBF) neural network trained in advance, and the training process of the Radial basis function neural network can be as follows:
(1) and acquiring the vibration signals of the bearing with the determined state type from a database, wherein the database can be any database containing the vibration signals of the bearing of the wind generating set, and the source of the training data is not limited.
(2) And carrying out local mean decomposition on the vibration signal, and then extracting dimensionless parameters from the signal subjected to the local mean decomposition to be used as the characteristic vector of the vibration signal.
(3) Coding the possible state types by using a coding mode, for example, the bearing works normally and is represented by [1, 0, 0, 0 ]; inner ring failure is represented by [0, 1, 0, 0 ]; the outer ring fault is represented by [0, 0, 1, 0 ]; ball failures are represented by [0, 0, 0, 1 ].
(4) Creating a radial basis function neural network by using a newrd function in MATLAB, and inputting the acquired feature vector of the vibration signal of the bearing with the determined state type into the radial basis function neural network for training, wherein the parameters of the radial basis function neural network are as follows: the Spread is 0.7 and the target error is 0.0001. Setting other parameters in the radial basis function neural network by using default parameters; and when the precision of the radial basis function neural network reaches the target error, finishing the training of the radial basis function neural network.
The number of actual output values in the set of actual output values represents the number of state types of the bearing, i.e. each actual output value in the set of actual output values represents a state type of the bearing.
And inputting the obtained feature vector of the vibration signal in the trained preset radial basis function neural network to obtain a group of corresponding output values representing the state of the bearing. The radial basis function neural network has the advantages that the radial basis function neural network is simple to create, short in training time, good in overall training effect, good in stability and small in fluctuation, and due to the characteristics of the radial basis function neural network, the problem that the characteristic vector is small in local area can not occur when the radial basis function neural network is used for analyzing the characteristic vector, the radial basis function neural network has good approximation capacity, and therefore the performance of the bearing state diagnosis method can be further improved.
In step 105, the state type of the bearing is determined according to the range in which the value of each actual output value in the set of actual output values is located.
Because each actual output value in the group of actual output values represents a state type of the bearing, the state type of the bearing represented by the vibration signal can be determined according to the range of the numerical value of each actual output value in the group of actual output values.
The range of the value of each actual output value in the set of actual output values can also be used to determine the accuracy of the set of actual output values, for example, when the value is in the preset range, it may indicate that the radial basis function neural network has a wrong determination, and the result is invalid.
According to the technical scheme, the vibration signal of the bearing is obtained to diagnose the state of the bearing, the vibration signal is subjected to local mean decomposition, dimensionless parameters are extracted and used as characteristic vectors input into the radial basis function neural network, and finally the real state of the bearing is obtained by comparing and judging the result output by the radial basis function neural network. Thus, the local mean decomposition method is suitable for non-Gaussian, non-stationary and non-linear signals, and the vibration signal of the bearing of the wind generating set is the signal with the characteristic; dimensionless parameters in time domain analysis are selected as the characteristic vector of state diagnosis, so that the diagnosis result is not influenced by factors such as bearing load, rotating speed and the like, a relative standard value is not required to be considered, the previous data is not required to be referred, the influence of the absolute level of a vibration signal is avoided, and the diagnosis result is not influenced even if the measuring points at each time are different; the radial basis function neural network has the unique optimal approximation function and can solve the problem of local minimum, so that the diagnosis result is more accurate, and the radial basis function neural network has the characteristic of high learning convergence rate, so that the radial basis function neural network is more convenient and rapid to use in practical application.
In one possible embodiment, the method may further include: and before the local mean decomposition is carried out on the vibration signal, carrying out smooth denoising treatment on the vibration signal. The vibration signal may be subjected to smoothing and denoising processing by a wavelet packet decomposition method, and may also be subjected to smoothing and denoising processing according to a low-pass filter shown in step 201 in fig. 2, for example.
Fig. 2 is a flowchart illustrating a bearing condition diagnosis method according to still another exemplary embodiment of the present disclosure, which includes a step 201 before the step 102, in addition to the steps 101 to 105 illustrated in fig. 1, as illustrated in fig. 2.
In step 201, a low-pass filter is used to perform a smoothing and denoising process on the vibration signal. Wherein, the low-pass filter can be: any one of a Butterworth low-pass filter, a chebyshev low-pass filter, and a moving average filter.
Through the technical scheme, after the vibration signal of the bearing is obtained, the vibration signal is subjected to smooth denoising treatment, and part of noise in the vibration signal can be filtered, so that the influence of the noise in the vibration signal in the subsequent signal analysis and judgment process is reduced, and the bearing state diagnosis result is more accurate.
In one possible embodiment, the dimensionless parameter includes: a peak factor, a form factor, a pulse factor, a margin factor, and a kurtosis factor.
Wherein, the calculation formula of the peak value factor is as follows:
cfis the crest factor, XpRepresenting a peak parameter, X, in a dimensional parameterrmsThe root mean square value parameter in the dimensional parameter is represented. Defects such as surface flaking, wear, marks and depressions, which are discrete defects, can occur on the bearing components. The total energy of the pulse waveform formed by such defects is not very large, but the peak value of the waveform is very obvious. The crest factor is therefore suitable for the diagnosis of such faults.
The formula for calculating the form factor is:
the Ws is a wave form factor,representing the mean parameter of the dimensional parameters. If the form factor is too large, the roller can be indicated to have a pitting phenomenon; if the form factor is too small, it indicates that the roller may be worn.
The formula for calculating the pulse factor is as follows:
i is a pulse factor, which is a statistical indicator for determining whether there is a strong signal impact in the signal.
The margin factor is calculated by the formula:
l is a margin factor by which the mechanical device can express its damage strength. If the gap is enlarged due to friction, the rms value increases faster than the mean value, and the margin factor increases.
The kurtosis factor is calculated as:
k is a kurtosis factor, xiRepresenting the vibration signal, i 1, 2. The kurtosis factor is an indicator of the sensitivity of the signal to the characteristics of the mid-pulse. Wherein,called kurtosis value, when the kurtosis value is 3, the normal bearing conforming to the normal distribution rule can be achieved, namely, the state of the bearing is normal. When the kurtosis value of the vibration signal of the bearing is higher than 4, the bearing is characterized to be damaged to some extent.
In a possible implementation, the state type corresponding to the actual output value includes: normal, inner ring failure, outer ring failure, ball failure, and cage failure.
Bearings are the most used component in machinery. The structure of the bearing can be divided into four parts, namely an outer ring, a rolling body, a retainer and an inner ring. The inner ring and the shaft are matched with each other to fix the bearing, provide a half of a motion track of the rolling body and transmit load; the outer ring and the bearing seat are matched with each other to fix the bearing, provide the other half of the motion track of the rolling body and also transmit load; however, the cage can connect all the rolling elements to one, so that the rolling elements are stressed equally; the rolling bodies are pressed by the load transmitted by the inner ring or the outer ring, so that the bearing keeps rotating.
In general, there are many factors that can lead to bearing failure in a machine, such as: exceeding the bearable degree, improper lubrication method, handling problems during operation and shutdown maintenance, electric corrosion, improper methods during installation and disassembly and the like, wherein each factor can cause damage of the bearing with different degrees, and different factors have certain differences on damage characteristics caused by the bearing. Several bearing failures that commonly occur are fatigue failures, wear failures, corrosion failures, fracture failures, and the like. In any case, one or more of the inner ring, the outer ring, the rolling bodies and the retainer in the bearing are failed, so that the bearing cannot be normally used.
Therefore, in the bearing condition diagnosis method shown according to the present exemplary embodiment, four most significant failure sources of the inner ring, the outer ring, the balls, and the cage of the bearing can be diagnosed, ensuring that when a bearing failure is diagnosed, a specific failure location can be accurately diagnosed.
In a possible embodiment, the determining the state type of the bearing according to the range of the value of each actual output value in the group of actual output values includes: and determining the state type corresponding to the actual output value with the value closest to the preset threshold value in the group of actual output values as the state type of the bearing. Wherein the preset threshold may be, for example, 1. That is, the state type corresponding to the actual output value whose value is closest to 1 among the set of actual output values is determined as the state type of the bearing. For example, when the bearing state corresponding to the actual output value includes normal, inner race failure, outer race failure, ball failure and cage failure, there should be five standard data in the set of actual output values that respectively characterize the actual output values of the above-mentioned five different bearing states, for example, the five bearing states can be represented as [1, 0, 0, 0, 0], [0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1], that is, in the set of actual output values, the bearing state corresponding to the first bit data is normal, the bearing state corresponding to the second bit data is an inner ring fault, the bearing state corresponding to the third bit data is an outer ring fault, the bearing state corresponding to the fourth bit data is a ball fault, and the bearing state corresponding to the fifth bit data is a cage fault. Then, for example, when the second number of a set of actual output values output by the radial basis function neural network is closest to 1, then the set of actual output values characterizes the bearing condition as an inner race fault.
Fig. 3 is a flowchart illustrating a bearing condition diagnosis method according to still another exemplary embodiment, which includes step 301 before step 105, in addition to step 101 to step 105 illustrated in fig. 1, as illustrated in fig. 3.
In step 301, it is determined whether there is an actual output value in the set of actual output values whose value is within a preset range. If yes, go to step 101; if not, go to step 105.
When it is determined that there is no actual output value in the set of actual output values whose value is within the preset range, the process goes to step 105, and the state type of the bearing is determined according to the range in which the value of each actual output value in the set of actual output values is located.
And when the actual output value with the value within the preset range exists in the group of actual output values, returning to the step of acquiring the vibration signal of the bearing, namely step 101.
The preset range may be set to, for example, (0.1, 0.8). When any one of the actual output values in the set of actual output values is within the range of (0.1, 0.8), it indicates that the set of actual output values is invalid, and it can be determined that the bearing state diagnosis has failed, and the true state of the bearing cannot be determined according to the set of actual output values, so the method returns to step 101, and the vibration signal of the bearing is acquired again and analyzed again. And when the value of none of the actual output values in the set of actual output values is within the range of (0.1, 0.8), it indicates that the set of actual output values is valid, and the bearing state can be accurately diagnosed according to the actual output values, so the step of analyzing and judging the set of actual output values in step 105 is performed to obtain the required bearing state.
In the following, a specific set of experimental data is given to describe a bearing condition diagnosis method shown according to an exemplary embodiment of the present disclosure.
Four groups of vibration signals are selected from any database to be used as test samples, and the bearing states are represented as normal, inner ring faults, outer ring faults and ball faults respectively. After the smoothing and denoising processing, local mean decomposition is performed on the vibration signal, and 5 dimensionless parameters, namely a peak factor, a form factor, a pulse factor, a margin factor and a kurtosis factor, are extracted from the vibration signal to form a feature vector of the vibration signal, wherein the feature vector is shown in table 1. The data in table 1 are input into the trained radial basis function neural network, and four sets of output values as shown in table 2 are obtained.
TABLE 1
TABLE 2
As can be seen from the data in table 2, the diagnostic effect of the bearing condition diagnostic method shown according to the exemplary embodiment of the present disclosure is very accurate.
Fig. 4 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to an exemplary embodiment of the present disclosure, the apparatus including, as shown in fig. 4: an obtaining module 10, configured to obtain a vibration signal of a bearing, where the vibration signal includes an acceleration signal of the bearing in at least one direction; a decomposition module 20, configured to perform local mean decomposition on the vibration signal; a feature vector extraction module 30, configured to extract a set of dimensionless parameters from the vibration signal decomposed by the decomposition module as feature vectors of the vibration signal; the neural network processing module 40 is configured to input the feature vector into a preset radial basis function neural network for processing, so as to obtain a set of actual output values; and the state type determining module 50 is configured to determine the state type of the bearing according to a range in which a numerical value of each actual output value in the group of actual output values is located.
Fig. 5 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to still another exemplary embodiment of the present disclosure, the apparatus further including, as shown in fig. 5: an optimization module 60, configured to perform a smoothing and denoising process on the vibration signal by using a low-pass filter before the decomposition module 20 performs local mean decomposition on the vibration signal.
In one possible embodiment, the dimensionless parameter includes: a peak factor, a form factor, a pulse factor, a margin factor, and a kurtosis factor.
In a possible implementation, the state type corresponding to the actual output value includes: normal, inner ring failure, outer ring failure, ball failure, and cage failure.
In one possible implementation, the state type determination module 50 is configured to: and determining the state type corresponding to the actual output value with the value closest to the preset threshold value in the group of actual output values as the state type of the bearing.
Fig. 6 is a block diagram schematically illustrating a structure of a bearing condition diagnosis apparatus according to still another exemplary embodiment of the present disclosure, the apparatus further including, as shown in fig. 6: a judging module 70, configured to judge whether there is an actual output value of which the value is within a preset range in the set of actual output values before the state type determining module 50 determines the state type of the bearing according to the range in which the value of each actual output value in the set of actual output values is located.
The state type determination module 50 is configured to: and when the actual output values of which the numerical values are in the preset range do not exist in the group of actual output values, determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
In a possible implementation, the determining module 70 is further configured to: and when the actual output values of which the numerical values are in the preset range exist in the group of actual output values, triggering the acquisition module to acquire the vibration signals of the bearing.
Fig. 7 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the bearing condition diagnosis method. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 705 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above-described bearing condition diagnosis method.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the bearing condition diagnosis method described above. For example, the computer readable storage medium may be the memory 702 described above including program instructions that are executable by the processor 701 of the electronic device 700 to perform the bearing condition diagnostic method described above.
Fig. 8 is a block diagram illustrating an electronic device 800 in accordance with an example embodiment. For example, the electronic device 800 may be provided as a server. Referring to fig. 8, an electronic device 800 includes a processor 822, which may be one or more in number, and a memory 832 for storing computer programs executable by the processor 822. The computer programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processor 822 may be configured to execute the computer program to perform the bearing condition diagnosis method described above.
Additionally, the electronic device 800 may also include a power component 826 and a communication component 850, the power component 826 may be configured to perform power management of the electronic device 800, and the communication component 850 may be configured to enable communication, e.g., wired or wireless communication, of the electronic device 800. The electronic device 800 may also include input/output (I/O) interfaces 858. The electronic device 800 may operate based on an operating system stored in the memory 832, such as Windows Server, Mac OSXTM, UnixTM, LinuxTM, and the like.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions, which when executed by a processor, implement the steps of the bearing condition diagnosis method described above. For example, the computer readable storage medium may be the memory 832 including program instructions described above that are executable by the processor 822 of the electronic device 800 to perform the bearing condition diagnostic method described above.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the functional module, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure. It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, various possible combinations will not be separately described in this disclosure.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. A method of diagnosing a condition of a bearing, the method comprising:
acquiring a vibration signal of a bearing, wherein the vibration signal comprises an acceleration signal of the bearing in at least one direction;
performing local mean decomposition on the vibration signal;
extracting a group of dimensionless parameters from the vibration signal subjected to the local mean decomposition to be used as a feature vector of the vibration signal;
inputting the characteristic vector into a preset radial basis function neural network for processing to obtain a group of actual output values;
and determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
2. The method of claim 1, further comprising:
and before the local mean decomposition is carried out on the vibration signal, carrying out smooth denoising processing on the vibration signal by using a low-pass filter.
3. The method of claim 1, wherein the dimensionless parameter comprises: a peak factor, a form factor, a pulse factor, a margin factor, and a kurtosis factor.
4. The method of claim 1, wherein the status types comprise: normal, inner ring failure, outer ring failure, ball failure, and cage failure.
5. The method of claim 1, wherein determining the type of condition of the bearing based on the range in which the value of each actual output value in the set of actual output values lies comprises:
and determining the state type corresponding to the actual output value with the value closest to the preset threshold value in the group of actual output values as the state type of the bearing.
6. The method according to any of claims 1-5, wherein prior to the step of determining the condition type of the bearing from the range in which the value of each actual output value of the set of actual output values lies, the method further comprises:
judging whether an actual output value with a value within a preset range exists in the group of actual output values or not;
the determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values comprises:
and when the actual output values of which the numerical values are in the preset range do not exist in the group of actual output values, determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
7. The method of claim 6, further comprising:
and returning to the step of acquiring the vibration signal of the bearing when the actual output value with the value within the preset range exists in the group of actual output values.
8. A bearing condition diagnosis device characterized by comprising:
the device comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a vibration signal of a bearing, and the vibration signal comprises an acceleration signal of the bearing in at least one direction;
the decomposition module is used for carrying out local mean decomposition on the vibration signal;
the characteristic vector extraction module is used for extracting a group of dimensionless parameters from the vibration signals decomposed by the decomposition module to be used as the characteristic vectors of the vibration signals;
the neural network processing module is used for inputting the characteristic vector into a preset radial basis function neural network for processing to obtain a group of actual output values;
and the state type determining module is used for determining the state type of the bearing according to the range of the numerical value of each actual output value in the group of actual output values.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the bearing condition diagnosing method as claimed in any one of claims 1 to 7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the bearing condition diagnosing method of any one of claims 1 to 7.
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