CN114528525B - Mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution - Google Patents
Mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution Download PDFInfo
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Abstract
The invention relates to a mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution, belongs to the technical field of wind turbine generator fault diagnosis, and provides a new blind deconvolution method, namely maximum weighted kurtosis blind deconvolution. The heavily weighted kurtosis has good robustness to single or a small amount of strong impact interference in a fault signal, and prior knowledge of a fault impact sequence to be recovered is not needed. Based on the method, the maximum weighted kurtosis blind deconvolution method can effectively solve the problem that a classical kurtosis maximization-based method tends to recover a single leading impact instead of a gear fault impact sequence, and meanwhile, compared with a common non-full 'blind' (relying on fault characteristic frequency prior), the method has stronger applicability in the aspect of industrial equipment gear fault diagnosis. And the application case in wind turbine fault diagnosis proves the effectiveness of the method for gear fault diagnosis.
Description
Technical Field
The invention belongs to the technical field of wind turbine generator system fault diagnosis, and particularly relates to a mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution.
Background
The wind turbine is the main equipment of the wind power plant, the price accounts for 74-82% of the total investment of the wind power plant, and the maintenance cost of the wind turbine becomes the main operation cost of the wind power plant due to high equipment failure rate and high maintenance cost caused by severe operation environment. Reducing maintenance cost of the wind turbine is an important way for improving economic benefits of wind power plant operation, and in order to effectively reduce maintenance cost of the wind turbine, wind power enterprises introduce technologies such as state monitoring, fault diagnosis and state maintenance in a dispute.
In a fault diagnosis link, a gear fault characteristic signal is usually weak under the influence of noise, a transmission path and the like, and the current fault diagnosis cannot effectively diagnose the gear fault of the wind turbine generator.
Therefore, at the present stage, a mechanical fault diagnosis method based on blind deconvolution of the maximum weighted kurtosis needs to be designed to solve the above problems.
Disclosure of Invention
The invention aims to provide a mechanical fault diagnosis method based on maximum weighted kurtosis blind deconvolution, which is used for solving the technical problems in the prior art, wherein in the fault diagnosis link, a gear fault characteristic signal is usually weak under the influence of noise, a transmission path and the like, and the current fault diagnosis cannot effectively diagnose the gear fault of a wind turbine generator.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the mechanical fault diagnosis method based on the maximum weighted kurtosis blind deconvolution comprises the following steps:
s1: inputting a measuring signal x, and randomly initializing a filter h coefficient;
s2: filtering the measurement signal x through a filter h to obtain a filtering signal s, and calculating the weighted kurtosis RK(s) of the filtering signal s;
s3: continuously updating the h coefficient of the filter by iterative solution of blind deconvolution of maximum weighted kurtosis;
s4: repeating the steps S2 and S3 to enable the weighted kurtosis RK (S) to reach the maximum and meet the iteration times;
s5: and the filtering signal s corresponding to the maximum weighted kurtosis RK(s) is the target filtering signal.
Further, in step S2, the step of calculating the weighted kurtosis RK (S) of the filtered signal S is as follows:
(1) M equal division is carried out on the filtering signal s to obtain each sub-section signal s m (M =1, …, M), M being any positive integer not exceeding the signal length;
(2) Calculating Kurt of each sub-section signal m ；
(3) For Kurt m The ascending order is arranged and expressed as a vector, namely: kurt is a very powerful tool asc ；
(4) Calculate Kurt m The weight of the sum, i.e.:
(5) To W m The descending order is performed, which is also expressed in vector form, i.e.: w is a group of desc ；
(6) Using rearranged weight directionQuantity W desc For rearranged Kurt vector asc Weighting to obtain the weighted kurtosis RK of the signal:
RK＝Kurt asc ·(W desc ) T (2)。
further, M is 4.
Further, step S3 specifically includes the following steps:
the maximum heavily weighted kurtosis blind deconvolution solved filter coefficients are represented as:
to obtain the maximum value of RK, the partial derivative of RK on the filter coefficients is made equal to 0, i.e.:
first of all, the first step is to,
wherein Kurt is θ For the kurtosis of theta stage filtering signal, the effective approximate solution of equation is obtained by iterative solution and mode of continuously updating filter coefficient, and the equation is utilized
Obtaining M filters, and weighting and summing the M filters to obtain an updated filter, wherein the process is as follows:
where N is the filter signal length, L is the filter length, x θ ，s θ Respectively corresponding to the theta-th section of the signal subsection and the filtered signal; thus, an updated filter is obtained:
a storage medium having stored thereon a computer program which, when executed, performs a method of mechanical fault diagnosis based on blind deconvolution of maximum heavily weighted kurtosis as described above.
An electronic device comprises a processor and a memory, wherein the memory is used for storing executable commands of the processor, and the processor executes the executable commands to realize the mechanical fault diagnosis method based on the maximum weighted kurtosis blind deconvolution.
Compared with the prior art, the invention has the beneficial effects that:
one of the benefits of the present solution is to provide a new blind deconvolution method, i.e., blind deconvolution with maximum weighted kurtosis. The heavily weighted kurtosis has good robustness to single or a small amount of strong impact interference in a fault signal, and prior knowledge of a fault impact sequence to be recovered is not needed. Based on the method, the maximum weighted kurtosis blind deconvolution method can effectively solve the problem that a classical kurtosis maximization-based method tends to recover a single leading impact instead of a gear fault impact sequence, and meanwhile, compared with a common non-full 'blind' (relying on fault characteristic frequency prior), the method has stronger applicability in the aspect of industrial equipment gear fault diagnosis. And the application case in wind turbine fault diagnosis proves the effectiveness of the method for gear fault diagnosis.
Drawings
FIG. 1 is a schematic diagram of a measurement signal and a filtering signal of a gearbox of a wind power transmission system according to an embodiment of the application.
Fig. 2 is a schematic diagram illustrating a process of calculating a weighted kurtosis of a filtered signal according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of a method according to the present embodiment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 3 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 2, the process of calculating the weighted kurtosis of the filtered signal is as follows:
(1) M equal division is carried out on the filtering signal s to obtain each sub-section signal s m (m＝1,…,M)。
(2) Calculating Kurt of each sub-section signal m 。
(3) For Kurt m The ascending order is arranged and expressed as a vector, namely: kurt is a very powerful tool asc 。
(4) Calculate Kurt m The weight of the sum, i.e.:
(5) To W m The descending order is performed, which is also expressed in vector form, i.e.: w is a group of desc 。
(6) Using the rearranged weight vector W desc For rearranged Kurt vector asc Weighting to obtain the weighted kurtosis RK of the signal:
RK＝Kurt asc ·(W desc ) T (2)
the value of M does not depend on a specific formula, theoretically, any positive integer not exceeding the signal length can enable the algorithm to normally run, but each sub-integerThe length of the segment signal cannot be too small, otherwise the Kurt of the sub-segment signal m The physical significance will be lost. Analysis of multiple sets of simulation signals and actual gear vibration signals shows that most of data analysis can achieve ideal results when M =4, so that the value of M is recommended to be 4 when the method is used.
Based on this, the blind deconvolution of the maximum heavily weighted kurtosis to solve FIR filter coefficients can be represented as a maximization problem as follows:
to find the maximum value of RK, let RK equal 0 for the partial derivative of the filter coefficients, i.e.:
first of all, the first step is to,
wherein Kurt is θ For the kurtosis of the filtered signal in section θ, it can be seen that equation (4) is highly nonlinear and difficult to solve directly. Therefore, an effective approximate solution to the equation is solved by iteratively solving and continuously updating the filter coefficients. In addition, to simplify the operation, use is made of
Obtaining M filters, and weighting and summing the M filters to obtain an updated filter, wherein the process comprises the following steps:
where N is the filter signal length, L is the filter length, x θ ，s θ Respectively corresponding to the theta-th section of the signal subsection and the filtered signal. Thus, an updated filter is obtained:
as shown in fig. 3, in summary, the overall flow of the proposed method is as follows:
(1) Input measurement signal x, specify parameters M =4, randomly initialize filter h coefficients, such as:
(2) And filtering the measurement signal x through a filter h to obtain a filtering signal s, and calculating the weighted kurtosis RK(s) of the filtering signal according to a formula (2).
(3) The filter h coefficients are updated according to equation (9).
(4) Repeating steps 2 and 3 maximizes RK(s) and satisfies the number of iterations.
(5) And 4, the filtering signal corresponding to the maximum RK(s) in the step 4 is the target filtering signal.
The method is researched and verified through the wind power transmission system fault diagnosis application.
This data comes from vibration monitoring of a wind turbine drive train. In the signal acquisition process, the sampling frequency is 97656Hz, and the rotating speed of the output pinion shaft of the gearbox is 1800rpm measured by the tachometer. Intercepting the signal length 51200 points, as shown in fig. 1 (a), the gear fault information is completely masked.
The filtered signal of the method is shown in fig. 1 (b). The frequency of the periodic impact sequence recovered by the method is 29.6Hz (1/0.0338 Hz), and is very close to the theoretical fault characteristic frequency (30 Hz) of the pinion. Therefore, it can be preliminarily considered that the pinion gear has a failure. And a certain gear tooth of the pinion is found to be damaged in the subsequent shutdown and unpacking detection, so that the effectiveness of the method is verified.
In summary, the present solution provides a new blind deconvolution method, i.e., a blind deconvolution with the maximum weighted kurtosis. The heavily weighted kurtosis has good robustness to single or a small amount of strong impact interference in a fault signal, and prior knowledge of a fault impact sequence to be recovered is not needed. Based on the method, the maximum weighted kurtosis blind deconvolution method can effectively solve the problem that a classical kurtosis maximization-based method tends to recover a single leading impact instead of a gear fault impact sequence, and meanwhile, compared with a common non-full 'blind' (relying on fault characteristic frequency prior), the method has stronger applicability in the aspect of industrial equipment gear fault diagnosis. And the application case in wind turbine fault diagnosis proves the effectiveness of the method for gear fault diagnosis.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (4)
1. The mechanical fault diagnosis method based on the maximum weighted kurtosis blind deconvolution is characterized by comprising the following steps of:
s1: inputting a measuring signal x, and randomly initializing a filter h coefficient;
s2: filtering the measurement signal x through a filter h to obtain a filtering signal s, and calculating the weighted kurtosis RK(s) of the filtering signal s;
s3: continuously updating the h coefficient of the filter by iterative solution of blind deconvolution of the maximum weighted kurtosis;
s4: repeating the steps S2 and S3 to enable the weighted kurtosis RK (S) to reach the maximum and meet the iteration number;
s5: the filtering signal s corresponding to the maximum weighted kurtosis RK(s) is the target filtering signal;
in step S2, the step of calculating the weighted kurtosis RK (S) of the filtered signal S is as follows:
(1) M is equally divided to the filtering signal s to obtain eachSub-section signal s m Wherein M =1, …, M is any positive integer not exceeding the signal length;
(2) Calculating Kurt of each sub-section signal m ；
(3) For Kurt m The ascending order is arranged and expressed as a vector, namely: kurt is a very powerful tool asc ；
(4) Calculate Kurt m The weight of the sum, i.e.:
(5) To W m The descending order is performed, which is also expressed in vector form, i.e.: w desc ；
(6) Using rearranged weight vector W desc For rearranged kurtosis vector Kurt asc And weighting to obtain the weighted kurtosis RK of the signal:
RK＝Kurt asc ·(W desc ) T (2)；
the step S3 is specifically as follows:
the maximum weighted kurtosis blind deconvolution solved filter coefficient is expressed as:
to find the maximum value of RK, let RK equal 0 for the partial derivative of the filter coefficients, i.e.:
first of all, when a user wants to use the apparatus,
wherein Kurt is θ For the kurtosis, pass, of the theta-th filtered signalSolving the effective approximate solution of the equation by over-iterative solution and continuously updating the filter coefficient, and utilizing
Obtaining M filters, and weighting and summing the M filters to obtain an updated filter, wherein the process is as follows:
where N is the filter signal length, L is the filter length, x θ ，s θ Respectively corresponding to the theta-th section of the signal subsection and the filtered signal; thus, an updated filter is obtained:
2. the method of claim 1, wherein M is 4.
3. A storage medium having stored thereon a computer program which, when executed, performs a method of diagnosing a mechanical failure based on blind deconvolution of maximum weighted kurtosis as claimed in claim 1 or 2.
4. An electronic device, comprising a processor and a memory, wherein the memory is used for storing executable commands of the processor, and the processor executes the executable commands to realize the method for diagnosing the mechanical fault based on the blind deconvolution of the maximum weighted kurtosis as claimed in claim 1 or 2.
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