CN118090212A - Rolling bearing early fault detection method based on asynchronous entropy - Google Patents
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Abstract
The invention discloses a rolling bearing early fault detection method based on asynchronous entropy, which is used for determining sampling frequency of synchronous data acquisition, time delay tau and embedding dimension m of phase space reconstruction, similarity tolerance threshold r and fault threshold T; synchronous vibration data are collected by utilizing a vibration sensor and a rotating speed sensor; according to the preset time delay tau and the embedding dimension m, carrying out phase space reconstruction on the synchronous vibration data; centering each m-dimensional vector in the phase space by using a centering method; calculating the second moment of all m-dimensional vector data in the phase space after the centering processing, the distance between all tracks and the similarity between all m-dimensional and m+1-dimensional vectors; calculating an asynchronous entropy value; if the nonsynchronous entropy NSE is greater than the threshold T, it is considered that an early failure of the rolling bearing has occurred, whereas the rolling bearing is in a normal state. The invention can be used for detecting the early faults of the rolling bearing; and early failure of the rolling bearing can be detected more accurately and rapidly.
Description
Technical Field
The invention belongs to the technical field of fault diagnosis of rotary machinery, and particularly relates to a rolling bearing early fault detection method based on asynchronous entropy.
Background
Rotary machines have been widely used in a variety of fields. In the use process of the rotary machine, the parts of the rotary machine can be degraded, so that faults are caused. Studies have shown that: rolling bearings are one of the most vulnerable components in rotating machinery systems. The failure of the rolling bearing not only reduces the working efficiency and performance of the rotating machine, but also may cause damage to the entire rotating machine system and even serious casualties. The early fault detection technology of the rolling bearing can detect the early fault of the rolling bearing at the initial stage of the fault, and further provides a basis for the implementation of the next fault diagnosis or fault-tolerant control, so that the fault and the damage caused by the fault are avoided. Therefore, early failure detection of rolling bearings has attracted considerable attention from researchers and engineers.
Currently, in order to reliably detect an early failure of a rolling bearing and to avoid the hazard thereof, various rolling bearing early failure detection methods have been proposed. However, the accuracy of early failure recognition by these detection methods needs to be further improved. Entropy-based detection methods have proven to be effective early failure detection methods. Entropy characterizes the complexity of nonlinear signals. When the rolling bearing fails early, the corresponding failure frequency occurs in the vibration signal. Although the magnitudes of these failure frequencies are relatively small, the occurrence of the failure frequencies may result in a change in the dynamic characteristics of the vibration signal, thereby causing a change in the entropy of the vibration signal. Accordingly, an early failure can be identified from a change in the entropy value of the vibration signal. In the prior study, fractional order scatter entropy is applied to characterize early faults of the rolling bearing, and although the method can identify the early faults of the rolling bearing, phase space reconstruction is needed for signals. Currently, there is no standard method to determine the time delay and embedding dimension of phase space reconstruction. Improper time delay and embedding dimensions can reduce the reliability of early failure detection of the rolling bearing. Sliding dispersion entropy has also been studied as a fault feature. The sliding scatter entropy also requires a prior determination of the time delay and embedding dimension of the phase space reconstruction. Therefore, the failure detection method based on the sliding dispersion entropy cannot reliably detect a failure, especially for a variable-rotation-speed rotating machine.
The normal operating signal of a rolling bearing can be divided into two parts: normal vibration components and noise components. The normal vibration component can also be regarded as a synchronization component synchronized with the rotational speed. The malfunction signal contains a failure component in addition to a normal vibration component and a noise component. The failure component may be regarded as an unsynchronized component that is not synchronized with the rotational speed. The core of the fault detection is to detect the fault component (or asynchronous component) present in the signal. From the components in the fault signal, it can be seen that both the normal vibration component (or synchronization component) and the noise component affect the fault detection based on entropy techniques. To eliminate the effect of noise components on fault diagnosis, some researchers have devised rotary machine fault diagnosis methods based on symbol fuzzy entropy (symbolic fuzzy entropy, SFE). The method adopts a symbol dynamic filtering technology to reduce the influence of noise on early fault diagnosis and reduce the calculation complexity. Although the SFE-based rotating machine fault diagnosis method is capable of detecting some early faults, the method may not reliably complete the detection of the early faults of the rolling bearing under the influence of the synchronization component. In order to be able to reliably detect an early failure of the rolling bearing, it is necessary to reduce the influence of the normal vibration component and the noise component in the signal on the early failure detection. The applicant has found through literature investigation that a method for detecting early failure of a rolling bearing in consideration of the problem has not been found yet. In view of the problem, the invention provides a rolling bearing early failure detection method based on asynchronous entropy, which aims to reliably detect the rolling bearing early failure.
Disclosure of Invention
The invention aims to: the invention provides a rolling bearing early fault detection method based on asynchronous entropy, which can reliably detect the rolling bearing early fault.
The technical scheme is as follows: the invention relates to a rolling bearing early fault detection method based on asynchronous entropy, which specifically comprises the following steps:
(1) Determining sampling frequency of synchronous data acquisition, time delay tau and embedding dimension m of phase space reconstruction, similarity tolerance threshold r and fault threshold T;
(2) Synchronous vibration data are collected by utilizing a vibration sensor and a rotating speed sensor;
(3) According to the preset time delay tau and the embedding dimension m, carrying out phase space reconstruction on the synchronous vibration data;
(4) Centering each m-dimensional vector in the phase space by using a centering method;
(5) Calculating second moments of all m-dimensional vector data in the phase space after the centralization treatment;
(6) Calculating the distance between the tracks based on the phase space after the centering treatment;
(7) Calculating the similarity between all m-dimensional vectors based on the second moment and the distance between m-dimensional vectors in the phase space;
(8) Repeating the steps (3) to (7), reconstructing the synchronous vibration data into a (m+1) -dimensional phase space, and carrying out centering treatment on each m-dimensional vector in the (m+1) -dimensional phase space; calculating the second moment of all (m+1) dimensional vector data in the (m+1) dimensional phase space; calculating the distance between the (m+1) dimensional vectors after the centering treatment and the similarity between the (m+1) dimensional vectors;
(9) Calculating an asynchronous entropy NSE;
(10) And identifying early faults of the rolling bearing based on the calculated non-synchronous entropy NSE and a set fault threshold T, and if the non-synchronous entropy NSE is larger than the threshold T, considering that the early faults of the rolling bearing occur, otherwise, the rolling bearing is in a normal state.
Further, the time delay tau in the step (1) is set according to the running period of each rotation of the rotating shaft; the embedding dimension m is determined from the early failure characteristics of the rolling bearing.
Further, the implementation process of the step (2) is as follows:
taking the rotating speed of the rotating shaft as a reference signal, and synchronously acquiring vibration data in the working process of the rolling bearing; the tachometer is used for measuring a rotating shaft rotating speed signal, and vibration data obtained in the working process of the rolling bearing are displacement data, speed data or acceleration data; the obtained synchronization data are expressed as:
Y＝{y(1),y(2),…,y(i),…,y(kM)}
wherein M is the number of acquisition points per revolution, and k is the total revolution of acquisition signals.
Further, the implementation process of the step (3) is as follows:
Phase space reconstruction of synchronous vibration data according to the time delay tau and the embedding dimension m set in the step (1) Wherein m-dimensional embedded vector/>Expressed as:
further, the implementation process of the step (4) is as follows:
for each m-dimensional vector in the phase space, carrying out centering treatment on the m-dimensional vector by using a centering treatment method, and ensuring the average value of each dimensional vector to be 0; thus, the vector is embedded Is centered as:
Wherein, For embedding vector/>The average of (2) is shown as follows:
Further, the implementation process of the step (5) is as follows:
Calculating the mean value of the second moment of all m-dimensional vector data in the phase space after the centralization treatment The following formula is shown:
Wherein, Representing the second moment of the ith m-dimensional vector data in the phase space after the centering process, as shown in the following formula:
Wherein, Represents the ith m-dimensional vector/>, after the centering process(K-1) th data in (a) is obtained.
Further, the implementation process of the step (6) is as follows:
For m-dimensional vectors in the phase space after the centering process, calculating the m-dimensional vectors And/>Mutual distance between:
Wherein, And/>Representing the m-dimensional vectors after the i and j-th centering processes in the phase space, respectively.
Further, the implementation process of the step (7) is as follows:
According to the calculated mutual distance between m-dimensional vectors in the phase space Calculating the similarity between m-dimensional vectors in the phase space:
Where n is the gradient of the similarity margin boundary and r is the similarity threshold.
Further, the similarity between the (m+1) -dimensional vectors of the phase space in the step (8) is:
Further, the implementation process of the step (9) is as follows:
According to the calculated similarity And/>The unsynchronized entropy NSE is calculated as follows:
NSE＝ln(Φ(m))-ln(Φ(m+1))
Wherein:
when the asynchronous entropy is larger, the complexity of the signals is higher, the asynchronous component is obvious, and the rolling bearing is in a fault state; otherwise, the complexity of the signal is lower, the asynchronous component is not obvious or absent, and the rolling bearing is in a normal state.
The beneficial effects are that: compared with the prior art, the invention has the beneficial effects that: the asynchronous entropy provided by the invention eliminates the influence of synchronous components and partial noise on early failure detection when the early failure of the rolling bearing is identified, and highlights the influence of failure components (asynchronous components) in signals on the entropy; according to the invention, the time delay of phase space reconstruction is determined according to the running period of each rotation of the rotating shaft, and the embedding dimension of the phase space reconstruction is determined according to the fault characteristics, so that the asynchronous entropy can be self-adapted to the rotating speed of the rotating machine; the rolling bearing early fault detection method based on the asynchronous entropy can effectively detect the occurrence of the rolling bearing early fault; compared with the traditional entropy, the designed asynchronous entropy is more sensitive to the early failure of the rolling bearing, and can eliminate the influence of the change of the rotating speed of the main shaft on the early failure detection of the rotating mechanical bearing; in addition, the required computational complexity is small compared to conventional fuzzy entropy.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the component signals simulating an early failure of a rolling bearing;
FIG. 3 is a composite signal simulating an early failure of a rolling bearing;
FIG. 4 shows entropy values of the method of the present invention and the conventional method in a fault state and a health state;
fig. 5 shows entropy differences between the method of the present invention and the conventional method in a fault state and a health state.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention provides a rolling bearing early fault detection method based on asynchronous entropy, which is shown in figure 1 and specifically comprises the following steps:
step 1: a priori knowledge, namely the sampling frequency of synchronous data acquisition, the time delay tau and embedding dimension m of phase space reconstruction, a similarity tolerance threshold r and a fault threshold T are determined.
The sampling frequency determines the amount of data acquired per revolution of the rotating shaft. In order to obtain a sufficient data acquisition amount M per rotation period of the rotating shaft, the sampling frequency is adaptively set toWhere n represents the rotational speed (r/min). In this embodiment, the amount of data acquired per revolution of the axis of rotation is M, and the time delay τ for phase space reconstruction is set to the time taken to acquire M data points. The embedding dimension is set to 5 and the similarity threshold r is set to 0.15 according to the bearing failure characteristics.
Step 2: and the synchronous vibration data are acquired by using the vibration sensor and the rotation speed sensor by taking the rotation speed of the main shaft as a reference.
And synchronously acquiring vibration data in the working process of the rolling bearing by taking the rotating speed of the rotating shaft as a reference signal. A tachometer may be used to measure the rotating shaft rotational speed signal. The obtained vibration data in the working process of the rolling bearing can
Is displacement data, velocity data or acceleration data. The obtained synchronization data can be expressed as:
Y＝{y(1),y(2),…,y(i),…,y(kM)}。
step 3: and carrying out phase space reconstruction on the synchronous vibration data according to the preset time delay tau and the embedding dimension m.
Phase space reconstruction of synchronous vibration data according to the embedding dimension and time delay set in step (1)Wherein the embedded vector/>Is an m-dimensional vector expressed as:
Step 4: and (5) respectively carrying out centering treatment on the m-dimensional vectors in the phase space by using a centering treatment method.
And (3) centering all m-dimensional embedded vectors in the phase space by using a centering processing method to ensure that the average value of each-dimensional vector is 0. Thus, m-dimensional embedded vectorsIs centered as:
Wherein, For embedding vector/>The average of (2) is shown as follows:
it should be noted that the purpose of the m-dimensional embedded vector centering processing in the phase space is to filter out or reduce the influence of the normal component and the noise component in the vibration signal on the early failure detection.
Step 5: calculating second moment average value of all embedded vector data in m-dimensional phase space after centralization treatmentCalculating the mean value/>, of the second moments of all embedded vector data in m-dimensional phase space after centralization treatmentThe following formula is shown:
Wherein, Representing the second moment of the ith embedded vector data in the m-dimensional phase space after the centering process, as shown in the following formula:
Wherein, Represents the ith m-dimensional vector/>, after the centering process(K-1) th data in (a) is obtained.
Step 6: and calculating the distance between the m-dimensional embedded vectors based on the m-dimensional phase space after the centering processing.
For m-dimensional embedded vectors in the phase space after the centering process, calculating m-dimensional vectorsAnd/>Mutual distance between:
Wherein, And/>Representing m-dimensional vectors after the i-th and j-th centering processes in the m-dimensional phase space, respectively.
Step 7: and (3) calculating the similarity between all m-dimensional embedded vectors based on the second moment obtained in the step (5) and the distance between the m-dimensional phase space embedded vectors obtained in the step (6).
Mutual distance between m-dimensional embedded vectors obtained according to step 6And calculating the similarity between the embedded vectors of the m-dimensional phase space, wherein the similarity is shown in the following formula:
where n is the gradient of the similarity margin boundary, where n=2, and r is the similarity threshold.
Step 8: repeating the steps 3 to 7, reconstructing the synchronous vibration data into a (m+1) -dimensional phase space, and carrying out centering treatment on each embedded vector in the (m+1) -dimensional phase space; calculating the second moment of all embedded vector data in the (m+1) dimensional phase space after the centering treatment; and calculating the distance between the embedded vectors after the centering processing and the similarity between the embedded vectors.
Embedding vectorsExpressed as:
Embedding vectors Is centered as:
Wherein, For embedding vector/>The average of (2) is shown as follows:
computing the average second moment of all embedded vector data in phase space The following formula is shown:
Wherein, Representing the second moment of the ith embedded vector data in the (m+1) dimensional phase space after the centering process, as shown in the following formula:
Wherein, Represents the i (m+1) -th dimension vector/>, after the centering process(K-1) th data in (a) is obtained.
For embedded vectors in the (m+1) dimensional phase space after the centering process, the mutual distance between them is calculated:
Wherein, And/>Representing the (m+1) dimensional vectors after the i and j-th centering processes in the phase space, respectively. Similarity/>, between embedded vectors in (m+1) -dimensional phase spaceThe following formula is shown:
Step 9: and calculating an asynchronous entropy value.
Calculating the similarity according to the above stepsAnd/>The unsynchronized entropy NSE is calculated as follows:
NSE＝ln(Φ(m))-ln(Φ(m+1))
Wherein:
When the nonsynchronous entropy is larger, the complexity of the signals is higher, the nonsynchronous component (fault component) is obvious, and the rolling bearing is in a fault state. When the asynchronous entropy is smaller, the complexity of the signal is lower, the asynchronous component (fault component) is not obvious or absent, and the rolling bearing is in a normal state.
Step 10: early faults of the rolling bearing are identified based on the calculated non-synchronous entropy NSE and a set fault threshold T, and if the non-synchronous entropy NSE is smaller than the threshold T, the rolling bearing is considered to be in a healthy state or in a normal state. Otherwise, it is indicated that an early failure of the rolling bearing has occurred.
Setting a sampling frequency of synchronous data acquisition, wherein the sampling frequency is used for ensuring that the data amount acquired in each rotation of the rotary machine is an integer M. The data amount m=50 acquired per rotation of the rotary machine of the present embodiment. Since the rotating speed is 600r/min for the large gear of the speed reducer, the sampling frequency of the vibration sensor is set to 9000Hz. The time delay of the phase space reconstruction is determined according to the rotation period, and thus is set to 50 sampling intervals. The rolling bearing weak chatter can be generally regarded as periodic bifurcation or Hopf bifurcation, and therefore, the present embodiment sets the embedding dimension m of the phase space reconstruction to 5. The similarity threshold r is set to 0.15.
To highlight the high recognition of early failure of rolling bearings according to the present invention, rolling bearing simulation signals were constructed and tested according to existing references. The constructed simulation signal includes four components, as follows:
x(t)＝x1(t)+x2(t)+x3(t)+x4(t)
t1＝mod(t,1/f1)
x2(t)＝0.6sin(2πf2t)
x3(t)＝sin(2πf3t)
x4(t)＝0.4rand(n,1),n＝length(t)
Wherein x 1 (t) is the modulation of the fault signal of the outer ring of the bearing at the simulated large gear and the resonance signal of the system structure; x 2 (t) and x 3 (t) are periodic harmonic signal components; x 4 (t) is the analog noise signal; α is the decay frequency (related to the damping characteristics of the system), α=400; f 1 is structural resonant frequency, f 1＝800Hz;f2 is meshing frequency of a high-speed gear, f 2＝180Hz;f3 is meshing frequency of a low-speed gear, f 3＝60Hz,fB is fault characteristic frequency, and f B =33 Hz; mod (·) is a remainder function; rand (·) is a random function; length (·) is the function of the array length.
The individual partial signal waveforms of the simulation signal are shown in fig. 2, and the composite signal waveform is shown in fig. 3. In this embodiment, the length of the extracted analog signal is 3000 pieces of data, and the data acquisition amount m=50 in each rotation of the rotating shaft at the bearing is detected. The time delay τ=50 of the phase space is set according to the data acquisition amount m=50 per rotation of the rotation shaft. Early failure of the bearing can be seen as a bifurcation phenomenon in a nonlinear system. Thus, the phase space embedding dimension is set to m=5.
And carrying out phase space reconstruction on the synchronous vibration data according to the phase space time delay tau=50 and the embedding dimension m=5. And (3) carrying out centering processing on each 5-dimensional vector in the phase space, calculating the distance between each 5-dimensional vector, and carrying out centering processing on the synchronous vibration data after phase space reconstruction by using a centering processing method, so as to ensure that the average value of each 5-dimensional vector is 0. Based on the 5-dimensional phase space after the centering process, the distance between the 5-dimensional vectors is calculated. The similarity between the 5-dimensional phase space vectors is calculated based on the average second moment and the distance between the 5-dimensional phase space vectors.
The embedding dimension is updated to the (m+1) dimension, i.e., 6 dimensions. And reconstructing the phase space of the synchronous vibration data according to the time delay tau=50 and the embedding dimension m=6 of the phase space, and carrying out centering processing on the reconstructed synchronous vibration data. After the centering, the distance between the 6-dimensional vectors is calculated, and then the similarity between the 6-dimensional vectors is calculated. The similarity between the vectors obtained when the embedding dimensions m=5 and m=6 is calculated, and the non-synchronization entropy NSE is calculated. The entropy results for the fault condition and the health condition are shown in fig. 4. The entropy differences between the fault condition and the health condition are shown in fig. 5, wherein the star-shaped sign line represents the entropy difference between the fault obtained by the method and the health condition, and the triangle sign line represents the entropy difference between the fault obtained by the traditional fuzzy entropy method and the health condition. Compared with the traditional method, the method has the advantage that 106% of entropy value difference is optimized on average. In terms of time complexity, the present invention takes 0.084 seconds on average per calculation of entropy values, whereas the conventional fuzzy entropy method takes 0.113 seconds on average. Compared with the traditional fuzzy entropy method, the calculation speed is improved by about 25%.
In summary, compared with the traditional fuzzy entropy-based fault detection method, the method provided by the invention has the advantages that the entropy value difference between the fault state and the health state is more obvious and the fault is more sensitive, so that the method can be used for detecting the early faults of the rolling bearing. In addition, from the aspect of time complexity, the time complexity of calculating the entropy value is superior to that of the traditional fuzzy entropy method, and the calculation cost is remarkably saved; the invention can detect the early failure of the rolling bearing more reliably and quickly.
The foregoing is merely exemplary of the present invention and is not intended to limit the present invention. All equivalents and alternatives falling within the spirit of the invention are intended to be included within the scope of the invention. What is not elaborated on the invention belongs to the prior art which is known to the person skilled in the art.
Claims (10)
1. The rolling bearing early fault detection method based on the asynchronous entropy is characterized by comprising the following steps of:
(1) Determining sampling frequency of synchronous data acquisition, time delay tau and embedding dimension m of phase space reconstruction, similarity tolerance threshold r and fault threshold T;
(2) Synchronous vibration data are collected by utilizing a vibration sensor and a rotating speed sensor;
(3) According to the preset time delay tau and the embedding dimension m, carrying out phase space reconstruction on the synchronous vibration data;
(4) Centering each m-dimensional vector in the phase space by using a centering method;
(5) Calculating second moments of all m-dimensional vector data in the phase space after the centralization treatment;
(6) Calculating the distance between the tracks based on the phase space after the centering treatment;
(7) Calculating the similarity between all m-dimensional vectors based on the second moment and the distance between m-dimensional vectors in the phase space;
(8) Repeating the steps (3) to (7), reconstructing the synchronous vibration data into a (m+1) -dimensional phase space, and carrying out centering treatment on each m-dimensional vector in the (m+1) -dimensional phase space; calculating the second moment of all (m+1) dimensional vector data in the (m+1) dimensional phase space; calculating the distance between the (m+1) dimensional vectors after the centering treatment and the similarity between the (m+1) dimensional vectors;
(9) Calculating an asynchronous entropy NSE;
(10) And identifying early faults of the rolling bearing based on the calculated non-synchronous entropy NSE and a set fault threshold T, and if the non-synchronous entropy NSE is larger than the threshold T, considering that the early faults of the rolling bearing occur, otherwise, the rolling bearing is in a normal state.
2. The method for detecting early failure of a rolling bearing based on asynchronous entropy according to claim 1, wherein the time delay τ in the step (1) is set according to a period of operation per rotation of the rotating shaft; the embedding dimension m is determined from the early failure characteristics of the rolling bearing.
3. The method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (2) is as follows:
taking the rotating speed of the rotating shaft as a reference signal, and synchronously acquiring vibration data in the working process of the rolling bearing; the tachometer is used for measuring a rotating shaft rotating speed signal, and vibration data obtained in the working process of the rolling bearing are displacement data, speed data or acceleration data; the obtained synchronization data are expressed as:
Y＝{y(1),y(2),…,y(i),…,y(kM)}
wherein M is the number of acquisition points per revolution, and k is the total revolution of acquisition signals.
4. The method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (3) is as follows:
Phase space reconstruction of synchronous vibration data according to the time delay tau and the embedding dimension m set in the step (1) Wherein m-dimensional embedded vector/>Expressed as:
5. the method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (4) is as follows:
for each m-dimensional vector in the phase space, carrying out centering treatment on the m-dimensional vector by using a centering treatment method, and ensuring the average value of each dimensional vector to be 0; thus, the vector is embedded Is centered as:
Wherein, For embedding vector/>The average of (2) is shown as follows:
6. the method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (5) is as follows:
Calculating the mean value of the second moment of all m-dimensional vector data in the phase space after the centralization treatment The following formula is shown:
Wherein, Representing the second moment of the ith m-dimensional vector data in the phase space after the centering process, as shown in the following formula:
Wherein, Represents the ith m-dimensional vector/>, after the centering process(K-1) th data in (a) is obtained.
7. The method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (6) is as follows:
For m-dimensional vectors in the phase space after the centering process, calculating the m-dimensional vectors And/>Mutual distance between:
Wherein, And/>Representing the m-dimensional vectors after the i and j-th centering processes in the phase space, respectively.
8. The method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (7) is as follows:
According to the calculated mutual distance between m-dimensional vectors in the phase space Calculating the similarity between m-dimensional vectors in the phase space:
Where n is the gradient of the similarity margin boundary and r is the similarity threshold.
9. The method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the similarity between (m+1) -dimensional vectors of the phase space in step (8) is:
10. the method for detecting early failure of rolling bearing based on asynchronous entropy according to claim 1, wherein the implementation process of the step (9) is as follows:
According to the calculated similarity And/>The unsynchronized entropy NSE is calculated as follows:
NSE＝ln(Φ(m))-ln(Φ(m+1))
Wherein:
when the asynchronous entropy is larger, the complexity of the signals is higher, the asynchronous component is obvious, and the rolling bearing is in a fault state; otherwise, the complexity of the signal is lower, the asynchronous component is not obvious or absent, and the rolling bearing is in a normal state.
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