CN113591241B - Slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA - Google Patents
Slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA Download PDFInfo
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Abstract
The invention relates to the technical field of slewing bearing fault diagnosis, in particular to a slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA, which comprises the following steps: 1) Determining an optimal parameter K in a VMD algorithm; 2) Decomposing an original signal by using a VMD optimized by an optimal parameter K, and selecting a component with the maximum kurtosis as an optimal component; 3) Optimizing the MOMEDA parameter T by using a GWO algorithm, searching the MOMEDA parameter T on the optimal component by taking the multi-point kurtosis as an fitness function, and taking the parameter T as the optimal parameter; 4) The MOMEDA optimized by the optimal parameter T is used for highlighting fault impact components in the optimal components and making an envelope spectrum; 5) Analyzing the envelope spectrum to obtain a diagnosis result; the invention combines the VMD with the MOMEDA, can effectively diagnose the fault characteristics of the low-speed heavy-load slewing bearing under the condition of strong background noise, and is suitable for further popularization and application.
Description
Technical Field
The invention relates to the technical field of slewing bearing fault diagnosis, in particular to a slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA.
Background
The slewing bearing is used as a key part of a plurality of large-scale machines, and the running state of the slewing bearing is directly related to the normal operation of the whole machine. The large slewing bearing has extremely severe working environment and is simultaneously subjected to axial force, radial force and overturning moment under normal operation conditions, so that faults are unavoidable in use. And once a fault occurs, the whole machine must be shut down for maintenance. In addition, the slewing bearing is large in size, slow in transportation and high in manufacturing cost, and replacement spare parts are not stored generally, so that the replacement period is prolonged, and huge economic loss is caused.
At present, fault diagnosis research at home and abroad is mainly focused on a medium-high speed bearing, and research on slewing bearings is relatively few. Therefore, diagnosis of the slewing bearing failure is a subject of practical research significance.
Disclosure of Invention
Aiming at the problems, the invention provides a slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA.
The invention is realized by the following technical scheme:
a slewing bearing fault diagnosis method based on VMD and self-adaptive MOMEDA comprises the following steps:
1) The maximum of the global IMF component kurtosis value is used as a preferential standard, and an optimal parameter K in a VMD algorithm is determined;
2) Decomposing an original signal by using a VMD optimized by an optimal parameter K, and selecting a component with the maximum kurtosis as an optimal component;
3) Optimizing the MOMEDA parameter T by using a GWO algorithm, searching the MOMEDA parameter T on the optimal component by taking the multi-point kurtosis as an fitness function, and taking the parameter T as the optimal parameter;
4) The MOMEDA optimized by the optimal parameter T is used for highlighting fault impact components in the optimal components and making an envelope spectrum;
5) And analyzing the envelope spectrum to obtain a diagnosis result.
Further, the step 1) specifically includes:
1.1 Initializing the decomposition number K=2;
1.2 Performing VMD decomposition by using the parameter, calculating kurtosis values of K components, and selecting the maximum kurtosis value under the K values for reservation;
1.3 Judging whether K is smaller than 10; if K is less than 10, k=k+1, returning to step 1.2); if K is more than or equal to 10, carrying out the next step;
1.4 Comparing the kurtosis value reserved under each K value, and taking the K value corresponding to the maximum kurtosis value as the optimal parameter.
Further, the step 3) specifically includes:
3.1 Initializing various parameters of the MOMEDA;
3.2 Setting population scale and maximum iteration times, and initializing wolf group positions;
3.3 Calculating the fitness function value of each wolf at the current position, and storing the first 3 wolves with the best fitness value as alpha, beta and delta wolves; the multi-point kurtosis is selected as a fitness function, and the expression is as follows:
3.4 Updating the position of the wolf according to formulas (13), (14);
X 1 ＝X α -A 1 ·D α
X 2 ＝X β -A 2 ·D β
X 3 ＝X δ -A 3 ·D δ (13)
wherein A is a synergistic coefficient vector, D α 、D β 、D δ Is the distance between alpha, beta, delta wolf and food, X α 、X β 、X δ Position vectors of alpha, beta and delta wolf;
3.5 Calculating the fitness value of all updated wolves, comparing the fitness value with the fitness function values of the current alpha, beta and delta wolves, and updating the alpha, beta and delta wolves if the result is good;
3.6 Repeating the steps 3.4) and 3.5) until the iteration is terminated, and outputting the optimal parameter T.
Further, in step 3.1), the optimizing range of the parameter Toptimizing is set as [ f ] s /f max f s /f min ]；
Wherein f s For sampling frequency f max For the maximum theoretical fault characteristic frequency of the slewing bearing, f min Is the minimum theoretical fault characteristic frequency of the slewing bearing.
Further, in the step 3.2), the population size of the wolf group is 10-50, the maximum iteration number is 10-50, and 10-50 positions are randomly generated in the optimizing range to serve as initial positions of the wolf group.
Further, the step 5) specifically includes:
5.1 Acquiring theoretical fault characteristic frequency of each part of the slewing bearing;
5.2 Obtaining a fault frequency from the envelope spectrum;
5.3 Comparing the obtained fault frequency in the envelope spectrum with the theoretical fault characteristic frequency of each part of the slewing bearing, wherein the part with the closest frequency is the fault part.
The method has certain universality under the condition of the same hardware basis, and therefore, the invention also provides a computer-readable storage medium, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the storage medium, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by a processor to realize the slewing bearing fault diagnosis method based on the VMD and the adaptive MOMEDA.
VMD principle and algorithm flow
The VMD decomposes the original signal f into K IMF components, ensures that the decomposition sequence is a modal component with limited bandwidth of the center frequency, and simultaneously, the sum of the estimated bandwidths of all the modes is minimum, and the constraint condition is that the sum of all the modes is equal to the original signal. The constraint variation model can be described as:
wherein: k is the total number of decomposed IMFs, u k 、ω k The k-th IMF and the center frequency after decomposition are respectively corresponding, and delta (t) is a dirac function and is a convolution operator.
A quadratic penalty factor alpha and Lagrange multiplicators lambda are introduced. Converting the constraint variation problem into an unconstrained variation problem, and obtaining a generalized Lagrange expression as follows:
the minimization problem of the variation objective function is converted into the generalized Lagrange saddle point problem, the optimization of the alternative direction multiplier iterative algorithm is utilized to obtain each modal component and the central frequency, and u is iterated k 、ω k And lambda is expressed as follows:
wherein: gamma is noise tolerance, meets the fidelity requirement of signal decomposition, andrespectively correspond to->u i Fourier transforms of (t), f (t) and λ (t).
For visual inspection, the VMD major iterative solution process is given as shown in fig. 10.
MOMEDA principle and algorithm flow
Assuming that the fault impact signal is y, the system impact response function is h, and the environmental noise is e, the original signal x collected when the fault occurs can be expressed as:
x＝h*y+e (5)
the essence of MOMEDA is to find the optimal filter f in a way that does not use iteration, so that the fault impact signal y is maximally restored. The algorithm provides a new index multipoint D-norm on the basis of the D-norm, and the new index multipoint D-norm is shown as the following formula:
the original minimum entropy deconvolution problem is converted into:
wherein T is a target vector, and is determined by a deconvolution period T, and when the deconvolution period coincides with a fault period, the multi-point D-norm reaches the maximum.
Solving equation (8) is equivalent to solving the equation:
finally, f is calculated and deduced to be:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the finally output fault impact signal may be expressed as:
compared with the prior art, the invention has the following beneficial effects:
the invention combines the VMD with the MOMEDA, and can effectively diagnose the fault characteristics of the low-speed heavy-load slewing bearing under the condition of strong background noise; compared with the existing VMD-MED method, the method has better performance in fault feature extraction, and is suitable for further popularization and application.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flowchart of the GWO optimized MOMEDA algorithm;
FIG. 3 is a time domain diagram and a frequency domain diagram of a slewing bearing vibration signal;
FIG. 4 is a graph of the maximum kurtosis values for VMD at different modal parameters K;
FIG. 5 is a VMD optimal component envelope spectrum;
FIG. 6 is an EMD optimal component envelope spectrum;
FIG. 7 is a GWO optimization process;
FIG. 8 is an envelope spectrum of the optimal component after MOMEDA processing;
FIG. 9 is an envelope spectrum of the optimal component after MED processing;
FIG. 10 is a schematic diagram of a VMD algorithm;
fig. 11 is a schematic diagram of a sensor arrangement.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
Referring to fig. 1, a method for diagnosing a slewing bearing fault based on a VMD and an adaptive MOMEDA includes:
1) The maximum of the global IMF component kurtosis value is used as a preferential standard, and an optimal parameter K in a VMD algorithm is determined; the method specifically comprises the following steps:
1.1 Initializing the decomposition number K=2;
1.2 Performing VMD decomposition by using the parameter, calculating kurtosis values of K components, and selecting the maximum kurtosis value under the K values for reservation;
1.3 Judging whether K is smaller than 10; if K is less than 10, k=k+1, returning to step 1.2); if K is more than or equal to 10, carrying out the next step;
1.4 Comparing the kurtosis value reserved under each K value, and taking the K value corresponding to the maximum kurtosis value as the optimal parameter.
2) Decomposing an original signal by using a VMD optimized by an optimal parameter K, and selecting a component with the maximum kurtosis as an optimal component;
3) Optimizing the MOMEDA parameter T by using a GWO algorithm, and searching the MOMEDA parameter T on the optimal component by taking the multi-point kurtosis as an fitness function, wherein the parameter T is the most optimal parameter; the method specifically comprises the following steps (shown in the accompanying figure 2):
3.1 Initializing various parameters of the MOMEDA; wherein, the length of the filter is set to be L, and the rectangular window is [ L1 ]]The optimizing range of the parameter T is [ f ] s /f max f s /f min ]；f s For sampling frequency f max For the maximum theoretical fault characteristic frequency of the slewing bearing, f min Is the minimum theoretical fault characteristic frequency of the slewing bearing. The filter length L needs to be determined according to the actually selected data sampling frequency and the sampling object frequency, and too short will affect the data processing effect, and too long will increase the calculation time.
3.2 Setting population scale and maximum iteration times, and initializing wolf group positions; the population size of the wolf optimization algorithm is generally between 10 and 50. In addition, if the maximum iteration number is set to be too small, the local optimal solution is easy to fall into; if the setting is too large, the algorithm running time is increased, and the efficiency is reduced. In this embodiment, the population size of the wolf group is set to be 20, the maximum iteration number is 20, and 20 positions are randomly generated in the optimizing range to serve as the initial position of the wolf group.
3.3 Calculating the fitness function value of each wolf at the current position, and storing the first 3 wolves with the best fitness value as alpha, beta and delta wolves; the multi-point kurtosis is selected as a fitness function, and the expression is as follows:
3.4 Updating the position of the wolf according to formulas (13), (14);
X 1 ＝X α -A 1 ·D α
X 2 ＝X β -A 2 ·D β
X 3 ＝X δ -A 3 ·D δ (13)
wherein A is a synergistic coefficient vector, D α 、D β 、D δ Is the distance between alpha, beta, delta wolf and food, X α 、X β 、X δ Position vectors of alpha, beta and delta wolf;
3.5 Calculating the fitness value of all updated wolves, comparing the fitness value with the fitness function values of the current alpha, beta and delta wolves, and updating the alpha, beta and delta wolves if the result is good;
3.6 Repeating the steps 3.4) and 3.5) until the iteration is terminated, and outputting the optimal parameter T.
4) The MOMEDA optimized by the optimal parameter T is used for highlighting the optimal component fault impact component and making an envelope spectrum;
5) Analyzing the envelope spectrum to obtain a diagnosis result; the method specifically comprises the following steps:
5.1 Acquiring theoretical fault characteristic frequency of each part of the slewing bearing;
5.2 Obtaining a fault frequency from the envelope spectrum;
5.3 Comparing the obtained fault frequency in the envelope spectrum with the theoretical fault characteristic frequency of each part of the slewing bearing, wherein the part with the closest frequency is the fault part.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Examples of the embodiments
i) Vibration signal acquisition for slewing bearing
In order to prove the effectiveness and superiority of the proposed method, the slewing bearing vibration signal data collected on site are used for verification. The slewing bearing vibration data collected by the implementation example are derived from a portal crane of a port. And an acceleration sensor is adopted for vibration signal data acquisition. Since the slewing bearing receives both radial and axial forces during normal operation, the stations are arranged in both directions. The specific arrangement mode is shown in fig. 11 (the arrow mark in fig. 11 is the measurement point of the acceleration sensor).
The idle load and constant rotating speed are kept at 0.92r/min during data acquisition, and the sampling frequency is 400Hz.
The slewing bearing is 132.50.4000 in type and three rows of rollers, and the outer ring is fixed and the inner ring rotates during operation. The theoretical fault characteristic frequency can be calculated according to the following calculation formula:
wherein f i 、f o 、f r The theoretical fault characteristic frequencies of an inner ring, an outer ring and rolling bodies are respectively shown, z is the number of the rolling bodies, D is the diameter of a roller, D is the intermediate diameter of a slewing bearing, alpha is a contact angle, and f s Is frequency conversion.
Substituting the slewing bearing parameters into formulas (15), (16) and (17) can obtain the theoretical fault characteristic frequency of each part, as shown in table 1.
TABLE 1 theoretical failure characteristic frequency,
ii) Signal processing and analysis
Fig. 3 is a time domain waveform and spectrum diagram of the acquired in-situ slewing bearing vibration signal. From the time domain waveform of fig. 3 (a), it can be observed that there is a significant impact component in the periodic waveform; whereas in the spectrogram of fig. 3 (b), the fault characteristic frequency cannot be visually distinguished.
The method provided by the invention is used for analyzing the vibration signal of the slewing bearing. The parameter K in the VMD algorithm is first determined. The range of K is set to [2,10], and the maximum kurtosis value at each K value is calculated as shown in FIG. 4. As can be seen from FIG. 4, when K is equal to 6, the kurtosis value reaches a maximum, and the IMF component corresponding to the kurtosis value is taken as the optimal component. The envelope spectrum of the optimal component is shown in fig. 5. From the figure, the failure frequency (2.985 Hz) and the frequency doubler and quadruple can be observed. In comparison with the theoretical failure frequency shown in table 1, the failure frequency was found to be close to the failure frequency of the row outer ring in the slewing bearing. Due to manufacturing and installation errors of the slewing bearing and relative sliding in the operation process, the actual fault frequency and the theoretical fault frequency may deviate. Therefore, the damage of the middle-row outer ring of the slewing bearing can be primarily judged.
To further demonstrate the superiority of the VMD decomposition method, EMD decomposition was performed on the original signal, and envelope spectrum analysis was also performed with the maximum kurtosis component selected, as shown in fig. 6. It can be seen that the frequency components in the envelope spectrum of the optimal component obtained by EMD are disordered, and the prominent fault frequency and its frequency multiplication cannot be found out from them.
To avoid misdiagnosis, MOMEDA algorithm is used to further analyze the optimal components after VMD decomposition. First, calculate the optimizing range f of the parameter T s /f max f s /f min ]Substituting data to obtain [129,652 ]]. Secondly, setting the filter length L to 1500 according to the selected field sampling frequency and slewing parameters of the slewing bearing. Further, the MOMEDA parameter T is optimized by using the GWO algorithm, and the whole optimizing process is as shown in fig. 7, so as to obtain an optimal parameter t= 136.0331. Finally, obtaining the envelope of the optimal component after MOMEDA processingThe spectrum is shown in FIG. 8. The fault frequency of the middle row outer ring and frequency doubling, three times, … and six times can be obviously observed from the figure. Therefore, the failure of the outer ring in the slewing bearing can be judged, and the effectiveness of the method provided by the invention is also verified.
To further demonstrate the superiority of the proposed method, the optimal component after VMD decomposition is subjected to MED processing and an envelope spectrum is made (as shown in fig. 9). The filter length in the MED algorithm is the same as that of the MOMEDA, i.e., l=1500. The failure characteristic frequency of the middle row outer ring can be observed from fig. 9, but many mixed frequencies exist around it, and the frequency multiplication component is not prominent enough. The effectiveness and superiority of the VMD combined with the self-adaptive MOMEDA algorithm in fault feature extraction of the low-speed heavy-load slewing bearing are verified.
The results in summary show that:
(1) Under the environment of low speed and heavy load and strong background noise, the fault feature extraction capability of the VMD method is better than that of the EMD method.
(2) The MOMEDA algorithm optimized by GWO is adopted, so that the optimal parameter T can be adaptively found, and the fault impact component can be effectively highlighted under the optimal parameter T.
(3) Compared with the VMD-MED method, the method has better performance in fault feature extraction.
(4) By adopting the method, the fault diagnosis of the low-speed heavy-load slewing bearing can be effectively realized.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations may be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The slewing bearing fault diagnosis method based on the VMD and the self-adaptive MOMEDA is characterized by comprising the following steps of:
1) The maximum of the global IMF component kurtosis value is used as a preferential standard, and an optimal parameter K in a VMD algorithm is determined;
2) Decomposing an original signal by using a VMD optimized by an optimal parameter K, and selecting a component with the maximum kurtosis as an optimal component;
3) Optimizing the MOMEDA parameter T by using a GWO algorithm, searching the MOMEDA parameter T on the optimal component by taking the multi-point kurtosis as an fitness function, and taking the parameter T as the optimal parameter; the method specifically comprises the following steps:
3.1 Initializing various parameters of the MOMEDA;
3.2 Setting population scale and maximum iteration times, and initializing wolf group positions;
3.3 Calculating the fitness function value of each wolf at the current position, and storing the first 3 wolves with the best fitness value as alpha, beta and delta wolves; the multi-point kurtosis is selected as a fitness function, and the expression is as follows:
3.4 Updating the position of the wolf according to formulas (13), (14);
X 1 ＝X α -A 1 ·D α
X 2 ＝X β -A 2 ·D β
X 3 ＝X δ -A 3 ·D δ (13)
wherein A is a synergistic coefficient vector, D α 、D β 、D δ Is the distance between alpha, beta, delta wolf and food, X α 、X β 、X δ Position vectors of alpha, beta and delta wolf;
3.5 Calculating the fitness value of all updated wolves, comparing the fitness value with the fitness function values of the current alpha, beta and delta wolves, and updating the alpha, beta and delta wolves if the result is good;
3.6 Repeating the steps 3.4) and 3.5) until the iteration is ended, and outputting the optimal parameter T;
4) The MOMEDA optimized by the optimal parameter T is used for highlighting fault impact components in the optimal components and making an envelope spectrum;
5) Analyzing the envelope spectrum to obtain a diagnosis result; the method specifically comprises the following steps:
5.1 Acquiring theoretical fault characteristic frequency of each part of the slewing bearing;
5.2 Obtaining a fault frequency from the envelope spectrum;
5.3 Comparing the obtained fault frequency in the envelope spectrum with the theoretical fault characteristic frequency of each part of the slewing bearing, wherein the part with the closest frequency is the fault part.
2. The method for diagnosing a slewing bearing failure based on a VMD and an adaptive MOMEDA according to claim 1, wherein the step 1) specifically comprises:
1.1 Initializing the decomposition number K=2;
1.2 Performing VMD decomposition by using the parameter, calculating kurtosis values of K components, and selecting the maximum kurtosis value under the K values for reservation;
1.3 Judging whether K is smaller than 10; if K is less than 10, k=k+1, returning to step 1.2); if K is greater than or equal to 10, carrying out the next step;
1.4 Comparing the kurtosis value reserved under each K value, and taking the K value corresponding to the maximum kurtosis value as the optimal parameter.
3. The method for diagnosing a slewing bearing failure based on VMD and adaptive MOMEDA as recited in claim 1, wherein the parameter Toptimizing range is set to [ f ] in step 3.1) s /f max f s /f min ]；
Wherein f s For sampling frequency f max For the maximum theoretical fault characteristic frequency of the slewing bearing, f min Is the minimum theoretical fault characteristic frequency of the slewing bearing.
4. The method for diagnosing a slewing bearing fault based on a VMD and an adaptive MOMEDA according to claim 1, wherein the wolf group population in step 3.2) has a size of 10-50 and a maximum number of iterations of 10-50, and 10-50 positions are randomly generated as initial wolf group positions within an optimizing range.
5. A computer-readable storage medium, characterized by: at least one instruction, at least one program, a code set, or an instruction set stored in the storage medium, where the at least one instruction, at least one program, a code set, or an instruction set is loaded by a processor and executed to implement the VMD and adaptive MOMEDA-based slewing bearing fault diagnosis method according to one of claims 1 to 4.
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