CN109973325B

CN109973325B - Method and apparatus for identifying abnormal vibration

Info

Publication number: CN109973325B
Application number: CN201711384895.3A
Authority: CN
Inventors: 张凯; 周杰; 魏浩
Original assignee: Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
Current assignee: Beijing Goldwind Science and Creation Windpower Equipment Co Ltd
Priority date: 2017-12-20
Filing date: 2017-12-20
Publication date: 2020-09-29
Anticipated expiration: 2037-12-20
Also published as: CN109973325A

Abstract

A method and apparatus for identifying abnormal vibration are provided, the method including: acquiring operation data of a preset component of a wind generating set in a plurality of time periods, wherein the operation data comprises vibration acceleration data of the preset component and rotating speed data related to the preset component; respectively carrying out frequency domain conversion on the vibration acceleration data of the predetermined component in the plurality of time periods to obtain a plurality of acceleration frequency spectrums corresponding to each other; determining a frequency value for abnormal vibration analysis in each acceleration frequency spectrum; and determining whether the predetermined component has abnormal vibration or not based on the determined frequency value for the abnormal vibration analysis in each acceleration frequency spectrum and the rotation speed data related to the predetermined component in each time period. By adopting the method and the equipment for identifying the abnormal vibration, the preset component with the abnormal vibration in the wind generating set can be timely and accurately positioned, and powerful support is provided for effectively evaluating the vibration state of the preset component.

Description

Method and apparatus for identifying abnormal vibration

Technical Field

The present invention relates to the field of wind power generation technology, and more particularly, to a method and apparatus for identifying abnormal vibration.

Background

Wind generating sets are generally arranged in remote wind power plants, and the reliability and stability of the operation of main components in the wind generating sets are of great importance. In order to ensure that the equipment can run safely, stably, for a long period and at full load, the running state of the equipment needs to be known in time, faults are prevented, accidents are avoided, the running period of the equipment is prolonged, the maintenance time is shortened, the production potential of the equipment is maximally excavated, the trouble is prevented in time, and the running working conditions of main parts in the wind generating set are controlled safely, stably, in variable periods and continuously changed loads in time.

In long-term operation, due to the special geographical position and the operation environment of the wind power plant and the concealment of the problems, site workers cannot find potential safety hazards (such as unobvious abnormal vibration) of main components in the wind generating set in the power generation process in time. The fundamental wave frequency and the frequency multiplication vibration of the wind driven generator are common vibration forms, and can cause damage to the wind driven generator and even the whole wind driven generator set in different degrees, and the wind driven generator fails in serious cases.

The existing fundamental frequency identification method is complex in general algorithm, needs a large amount of calculation and signal analysis, and is low in efficiency when used for identifying mass data.

Disclosure of Invention

An object of an exemplary embodiment of the present invention is to provide a method and an apparatus for identifying abnormal vibration, so as to solve the technical problems that the existence of abnormal vibration in main components of a wind turbine generator system cannot be found in time and the efficiency of identifying the abnormal vibration is low in the prior art.

According to an aspect of exemplary embodiments of the present invention, there is provided a method of identifying abnormal vibration, the method including: acquiring operation data of a preset component of a wind generating set in a plurality of time periods, wherein the operation data comprises vibration acceleration data of the preset component and rotating speed data related to the preset component; respectively carrying out frequency domain conversion on the vibration acceleration data of the predetermined component in the plurality of time periods to obtain a plurality of acceleration frequency spectrums corresponding to each other; determining a frequency value for abnormal vibration analysis in each acceleration frequency spectrum; and determining whether the predetermined component has abnormal vibration or not based on the determined frequency value for the abnormal vibration analysis in each acceleration frequency spectrum and the rotation speed data related to the predetermined component in each time period.

Alternatively, the types of abnormal vibration may include fundamental frequency vibration abnormality of the predetermined component and frequency multiplication vibration abnormality of the predetermined component.

Optionally, the step of determining a frequency value for abnormal vibration analysis in each acceleration frequency spectrum may include: searching for a frequency point of which the frequency amplitude value is greater than a frequency amplitude threshold value in the acceleration frequency spectrum; and taking the frequency value corresponding to the searched frequency point as the frequency value for analyzing the abnormal vibration in the acceleration frequency spectrum.

Optionally, the step of determining a frequency value for abnormal vibration analysis in each acceleration frequency spectrum may include: determining whether a frequency amplitude value corresponding to a preset concerned frequency point in the acceleration frequency spectrum is greater than a frequency amplitude threshold value; and if the frequency amplitude value corresponding to the preset attention frequency point is larger than a frequency amplitude threshold value, taking the frequency value corresponding to the preset attention frequency point as a frequency value for analyzing abnormal vibration in the acceleration frequency spectrum.

Alternatively, the preset frequency points of interest may be all the frequency points included in the acceleration spectrum, which are arranged in descending order according to the magnitude of the frequency amplitude value, and a predetermined number of previous frequency points.

Alternatively, the step of determining whether there is abnormal vibration in the predetermined component based on the determined frequency value for the abnormal vibration analysis in each acceleration spectrum and the rotation speed data associated with the predetermined component for each period of time may include: determining whether a predetermined linear distribution rule is satisfied between the frequency value and the rotation speed data; and when the frequency value and the rotating speed data meet the preset linear distribution rule, determining that the preset component has abnormal vibration.

Optionally, the step of determining whether a predetermined linear distribution law is satisfied between the frequency value and the rotation speed data may include: drawing a rotating speed-frequency scatter diagram based on the frequency values and the rotating speed data, wherein one scatter point in the rotating speed-frequency scatter diagram can correspond to the rotating speed data of a time period and one frequency value used for abnormal vibration analysis in an acceleration frequency spectrum corresponding to the time period; selecting scattered points within a preset range of a linear model with a preset frequency; and determining whether the frequency value and the rotating speed data meet a preset linear distribution rule of a preset frequency linear model or not based on the selected scatter points.

Optionally, the step of determining whether the predetermined linear distribution law of the predetermined frequency linear model between the frequency value and the rotation speed data is satisfied based on the selected scatter point may include: performing linear regression on the frequency value corresponding to the selected scattering point and the rotating speed data to obtain a model parameter of the linear regression; calculating a difference between the model parameter and a specified parameter of the predetermined component; and when the difference value is not larger than a first set value, determining that the frequency value corresponding to the selected scattering point and the rotating speed data meet a preset linear distribution rule of a preset frequency linear model.

Optionally, the step of determining whether the predetermined linear distribution law of the predetermined frequency linear model between the frequency value and the rotation speed data is satisfied based on the selected scatter point may include: establishing an objective function indicating the distance of each scatter point to the predetermined frequency linear model; substituting the frequency value corresponding to the selected scattering point and the rotating speed data into a target function to obtain a value of the target function; and when the value of the target function is not larger than a set value, determining that the frequency value corresponding to the selected scattering point and the rotating speed data meet a preset linear distribution rule of a preset frequency linear model.

Optionally, the step of determining whether a predetermined linear distribution law is satisfied between the frequency value and the rotation speed data may include: calculating a rotation speed statistic value reflecting data characteristics for each time period based on the rotation speed data; and determining whether the frequency value and the rotating speed statistic value meet a preset linear distribution rule or not.

Optionally, the revolution speed statistic value reflecting the data characteristic for each time period may include any one of the following items: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period.

Optionally, the step of frequency-domain converting the vibration acceleration data of the predetermined component in the plurality of time periods to obtain a corresponding plurality of acceleration frequency spectrums may include: determining whether the rotating speed data related to the preset component in each time period is in a set rotating speed range; and if the rotating speed data related to the preset component in any time period is in a set rotating speed range, performing frequency domain conversion on the vibration acceleration data of the preset component in any time period to obtain a corresponding acceleration frequency spectrum.

Alternatively, the step of determining whether the rotation speed data related to the predetermined component in each period of time is within a set rotation speed range may include: calculating a standard deviation or steady state error of the rotational speed data associated with the predetermined component for each time period; determining that the rotational speed data associated with the predetermined component for any one time period is within a set rotational speed range if the standard deviation or steady state error calculated for the time period is within a set threshold range; and if the standard deviation or the steady-state error calculated for any time period is not in the set threshold value range, determining that the rotating speed data related to the predetermined component in any time period is not in the set rotating speed range.

Optionally, the predetermined component may comprise any one of: the wind driven generator comprises a wind driven generator, a generator tooth groove, a gear box and a rolling bearing, wherein when the predetermined component is the wind driven generator, the vibration acceleration data of the predetermined component can be vibration acceleration data of a cabin of the wind driven generator, the rotating speed data related to the predetermined component can be rotating speed data of the wind driven generator, and the type of abnormal vibration can comprise fundamental frequency vibration abnormity of the wind driven generator and frequency multiplication vibration abnormity of fundamental frequency; when the predetermined component is a generator slot, the vibration acceleration data of the predetermined component can be vibration acceleration data of a cabin of the wind driven generator, the rotating speed data related to the predetermined component can be rotating speed data of the wind driven generator, and the type of abnormal vibration can comprise generator slot frequency vibration abnormality and frequency multiplication vibration abnormality of the generator slot frequency; when the predetermined component is a gearbox, the vibration acceleration data of the predetermined component can be vibration acceleration data of a power gear or a driven gear in the gearbox, the rotating speed data related to the predetermined component can be rotating speed data of a shaft where the power gear or the driven gear is located, and the type of abnormal vibration can comprise frequency multiplication vibration abnormity of the meshing frequency of the power gear and the meshing frequency of the power gear or frequency multiplication vibration abnormity of the meshing frequency of the driven gear and the meshing frequency of the driven gear; when the predetermined component is a rolling bearing, the vibration acceleration data of the predetermined component may be vibration acceleration data of a bearing seat of the rolling bearing, the rotational speed data related to the predetermined component may be rotational speed data of the rolling bearing, and the type of the abnormal vibration may include a rolling bearing fault characteristic frequency vibration abnormality and a frequency doubling vibration abnormality of the rolling bearing fault characteristic frequency.

According to another aspect of exemplary embodiments of the present invention, there is provided an apparatus to identify abnormal vibration, the apparatus including: the operation data acquisition module is used for acquiring operation data of a preset component of the wind generating set in a plurality of time periods, wherein the operation data comprises vibration acceleration data of the preset component and rotating speed data related to the preset component; the time-frequency conversion module is used for respectively carrying out frequency domain conversion on the vibration acceleration data of the preset component in the time periods so as to obtain a plurality of corresponding acceleration frequency spectrums; the frequency value determining module is used for determining a frequency value used for abnormal vibration analysis in each acceleration frequency spectrum; and the abnormal vibration analysis module is used for determining whether the preset component has abnormal vibration or not based on the frequency value used for the abnormal vibration analysis in each determined acceleration frequency spectrum and the rotating speed data related to the preset component in each time period.

Optionally, the frequency value determining module may be configured to search a frequency point in the acceleration frequency spectrum, where the frequency amplitude value is greater than the frequency amplitude threshold, and use a frequency value corresponding to the searched frequency point as a frequency value for analyzing abnormal vibration in the acceleration frequency spectrum.

Optionally, the frequency value determining module may be configured to determine whether a frequency amplitude value corresponding to a preset frequency of interest in the acceleration spectrum is greater than a frequency amplitude threshold, and if the frequency amplitude value corresponding to the preset frequency of interest is greater than the frequency amplitude threshold, the frequency value determining module uses the frequency value corresponding to the preset frequency of interest as the frequency value for analyzing the abnormal vibration in the acceleration spectrum.

Optionally, the abnormal vibration analysis module may be configured to determine whether a predetermined linear distribution rule is satisfied between the frequency value and the rotation speed data, and determine that abnormal vibration exists in the predetermined component when the predetermined linear distribution rule is satisfied between the frequency value and the rotation speed data.

Optionally, the abnormal vibration analysis module may include: the scatter diagram drawing module is used for drawing a rotating speed-frequency scatter diagram based on the frequency value and the rotating speed data, wherein one scatter point in the rotating speed-frequency scatter diagram can correspond to the rotating speed data of a time period and one frequency value used for abnormal vibration analysis in an acceleration frequency spectrum corresponding to the time period; the scattered point screening module is used for selecting scattered points within a preset range of the linear model with the preset frequency; and the linear distribution determining module is used for determining whether a preset linear distribution rule of the preset frequency linear model is met between the frequency value and the rotating speed data or not based on the selected scatter point.

Optionally, the linear distribution determining module may be configured to perform linear regression on the frequency value corresponding to the selected scattering point and the rotation speed data to obtain a model parameter of the linear regression, calculate a difference between the model parameter and a specified parameter of the predetermined component, and determine that a predetermined linear distribution rule of the predetermined frequency linear model is satisfied between the frequency value corresponding to the selected scattering point and the rotation speed data when the difference is not greater than a first set value.

Optionally, the linear distribution determining module may include: an objective function establishing submodule for establishing an objective function indicating a root mean square value of a distance from each scatter point to the predetermined frequency linear model; the objective function value calculation submodule is used for substituting the frequency value corresponding to the selected scattering point and the rotating speed data into an objective function to obtain the value of the objective function; and the distribution rule determining submodule is used for determining that the frequency value corresponding to the selected scattered point and the rotating speed data meet the preset linear distribution rule of the preset frequency linear model when the value of the target function is not larger than a set value.

Optionally, the abnormal vibration analysis module may be configured to calculate a rotation speed statistic value reflecting data characteristics for each time period based on the rotation speed data, and determine whether a predetermined linear distribution rule is satisfied between the frequency value and the rotation speed statistic value.

Optionally, the revolution speed statistic value reflecting the data characteristic for each time period may include any one of: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period.

Optionally, the time-frequency conversion module may be configured to determine whether the rotation speed data related to the predetermined component in each time period is within a set rotation speed range, and if the rotation speed data related to the predetermined component in any time period is within the set rotation speed range, perform frequency domain conversion on the vibration acceleration data of the predetermined component in any time period to obtain a corresponding acceleration spectrum.

Optionally, the time-frequency conversion module may be configured to calculate a standard deviation or a steady-state error of the rotation speed data associated with the predetermined component in each time period, determine that the rotation speed data associated with the predetermined component in any time period is within a set rotation speed range if the standard deviation or the steady-state error calculated for any time period is within a set threshold range, and determine that the rotation speed data associated with the predetermined component in any time period is not within the set rotation speed range if the standard deviation or the steady-state error calculated for any time period is not within the set threshold range.

According to still another aspect of exemplary embodiments of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described method of identifying abnormal vibration.

According to still another aspect of exemplary embodiments of the present invention, there is provided a computing apparatus including: a processor; a memory storing a computer program which, when executed by the processor, implements the above-described method of identifying abnormal vibration.

By adopting the method and the equipment for identifying the abnormal vibration, the preset component with the abnormal vibration in the wind generating set can be timely and accurately positioned, and powerful support is provided for effectively evaluating the vibration state of the preset component.

Drawings

The above and other objects, features and advantages of exemplary embodiments of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings which illustrate exemplary embodiments.

Fig. 1 illustrates a flowchart of a method of identifying abnormal vibration according to an exemplary embodiment of the present invention;

FIG. 2 shows a flowchart of the steps of determining whether a scatter point satisfies a predetermined linear distribution law according to an exemplary embodiment of the present invention;

FIGS. 3A-3D respectively illustrate exemplary graphs of a speed-frequency scatter plot in accordance with an exemplary embodiment of the present invention;

FIG. 4 shows a flowchart of the steps of determining whether a scatter point satisfies a predetermined linear distribution law based on a predetermined frequency linear model according to an exemplary embodiment of the present invention;

FIG. 5 shows a flowchart of the steps of determining whether a scatter satisfies a predetermined linear distribution law based on an objective function, according to an exemplary embodiment of the present invention;

fig. 6 illustrates a structural diagram of an apparatus for recognizing abnormal vibration according to an exemplary embodiment of the present invention;

fig. 7 illustrates a structural diagram of an abnormal vibration analysis module according to an exemplary embodiment of the present invention;

fig. 8 illustrates a structural diagram of a linear distribution determination module according to an exemplary embodiment of the present invention.

Detailed Description

Various example embodiments will now be described more fully with reference to the accompanying drawings, in which some example embodiments are shown.

Fig. 1 illustrates a flowchart of a method of identifying abnormal vibration according to an exemplary embodiment of the present invention.

Referring to fig. 1, in step S10, operational data of predetermined components of a wind turbine generator set over a plurality of time periods is obtained. Here, the operation data may include vibration acceleration data of the predetermined component and rotational speed data related to the predetermined component.

Preferably, the vibration acceleration data of the predetermined component may include vibration acceleration data of a first predetermined direction and vibration acceleration data of a second predetermined direction. As an example, the first predetermined direction may refer to a direction from the head to the tail of the wind turbine generator system, and the second predetermined direction may refer to a direction perpendicular to the wind direction (e.g., a field worker standing downwind facing the nose, the left-right direction of the field worker may be defined as the second predetermined direction).

In an exemplary embodiment of the present invention, it may be determined whether the predetermined component has abnormal vibration in the first predetermined direction or whether the predetermined component has abnormal vibration in the second predetermined direction by processing the vibration acceleration data in the first predetermined direction and the vibration acceleration data in the second predetermined direction, respectively.

Here, the types of abnormal vibration may include fundamental frequency vibration abnormality of the predetermined component and frequency multiplication vibration abnormality of the predetermined component. As an example, the predetermined component may comprise any one of: wind driven generator, generator tooth's socket, gear box, antifriction bearing.

In a first embodiment, the predetermined component may be a wind generator of a wind park. In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of the nacelle of the wind turbine, and the rotational speed data associated with the predetermined component may be rotational speed data of the wind turbine. Accordingly, the types of abnormal vibration may include a fundamental frequency vibration abnormality of the wind power generator and a frequency doubling vibration abnormality of the fundamental frequency.

In a second embodiment, the predetermined component may be a generator slot (e.g., a generator stator slot). In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of the nacelle of the wind turbine, and the rotational speed data associated with the predetermined component may be rotational speed data of the wind turbine. Accordingly, the type of abnormal vibration may include a generator cogging frequency vibration abnormality and/or a multiple frequency vibration abnormality of the generator cogging frequency. Here, since the generator slot frequency is equal to the number of stator slots of the wind turbine × the wind turbine rotation speed/60, and the number of stator slots of the wind turbine is greater than the number of pole pairs of the wind turbine, the generator slot frequency (or frequency multiplication) is greater than the fundamental frequency (or frequency multiplication) of the wind turbine. That is, it is possible to determine whether there is abnormal vibration of the wind turbine tooth space (whether there is abnormal vibration of the generator tooth space frequency and/or abnormal vibration of the double frequency of the generator tooth space frequency) by analyzing data of a high frequency portion (a portion corresponding to the generator tooth space frequency and/or the double frequency of the generator tooth space frequency) in each acceleration spectrum, and to determine whether there is abnormal vibration of the wind turbine generator (whether there is abnormal vibration of the fundamental frequency of the wind turbine generator and/or abnormal vibration of the double frequency of the fundamental frequency) by analyzing data of a low frequency portion (a portion corresponding to the fundamental frequency and/or the double frequency of the wind turbine generator) in each acceleration spectrum.

In a third embodiment, the predetermined component may be a gearbox including a power gear and a driven gear therein. In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of a power gear in the gear box or vibration acceleration data of a driven gear, and the rotation speed data associated with the predetermined component may be rotation speed data of a shaft on which the power gear is located or rotation speed data of a shaft on which the driven gear is located. Accordingly, the types of abnormal vibration may include a power gear mesh frequency vibration abnormality, a multiple frequency vibration abnormality of the power gear mesh frequency, a driven gear mesh frequency vibration abnormality, and/or a multiple frequency vibration abnormality of the driven gear mesh frequency. Here, the meshing frequency of the power gear (or the driven gear) is equal to the number of teeth of the power gear (or the number of teeth of the driven gear) × the rotation speed of the shaft on which the power gear (or the driven gear) is located/60.

In the fourth embodiment, the predetermined component may be a rolling bearing, which may be referred to herein as any one of a plurality of rolling bearings in the wind turbine generator set. In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of a bearing housing of the rolling bearing, and the rotational speed data associated with the predetermined component may be rotational speed data of the rolling bearing.

Accordingly, the type of abnormal vibration may include a rolling bearing failure characteristic frequency vibration abnormality and/or a double frequency vibration abnormality of the rolling bearing failure characteristic frequency. Here, it should be understood that the rolling bearing may include a bearing inner ring, an outer ring, rolling bodies, a cage, and accordingly, the predetermined component may refer to the bearing inner ring, the outer ring, the rolling bodies, or the cage. For example, taking a predetermined component as a rolling body of a rolling bearing as an example, it is possible to identify whether there is a malfunction characteristic frequency abnormal vibration or a frequency doubling vibration abnormality of the malfunction characteristic frequency of the rolling body in the rolling body based on the method of identifying abnormal vibration according to the exemplary embodiment of the present invention. The inner ring, the outer ring, the rolling body and the retainer of the bearing correspond to respective failure coefficients, and the failure characteristic frequency can be obtained by the product of the failure coefficients and the rotation frequency.

In step S20, the vibration acceleration data of the predetermined component over a plurality of time periods are respectively subjected to frequency domain conversion to obtain a plurality of acceleration spectra respectively corresponding thereto.

For example, the vibration acceleration data of the predetermined component in any one of the plurality of time periods may be frequency-domain converted to obtain an acceleration spectrum corresponding to the vibration acceleration data in the any one time period, that is, the vibration acceleration data in one time period corresponds to one acceleration spectrum. As an example, the vibration acceleration data of the predetermined component over a plurality of time periods may be frequency-domain converted by fast fourier transform, however, the present invention is not limited thereto, and other ways may be adopted to perform the frequency-domain conversion.

Preferably, the acquired operation data of the predetermined component in a plurality of time periods may be screened in advance based on the rotation speed data related to the predetermined component, and the vibration acceleration data of the predetermined component in the screened operation data in the plurality of time periods may be subjected to frequency domain conversion respectively.

For example, it may be determined whether the rotational speed data associated with the predetermined component for each time period is within a set rotational speed range, and if the rotational speed data associated with the predetermined component for any time period is within the set rotational speed range, frequency domain conversion may be performed on the vibration acceleration data of the predetermined component for any time period to obtain a corresponding one of the acceleration frequency spectrums. Here, because the rotational speed data in the wind turbine generator system is a time variable, the fundamental frequency or the frequency multiplication of the predetermined component is related to the rotational speed, and the fundamental frequency or the frequency multiplication is difficult to identify the abnormal vibration under the condition of large rotational speed fluctuation. Therefore, in order to improve the accuracy of identifying the fundamental frequency or the frequency multiplication of the abnormal vibration, the fluctuation range of the rotational speed data may be limited, that is, the rotational speed fluctuation of the operation data for identifying the abnormal vibration may be made small.

As an example, the step of determining whether the rotational speed data associated with the predetermined component for each period of time is within the set rotational speed range may include: calculating a standard deviation or a steady-state error of the rotation speed data related to the predetermined component in each time period, determining that the rotation speed data related to the predetermined component in any time period is in a set rotation speed range if the standard deviation or the steady-state error calculated for any time period is in a set threshold range, and determining that the rotation speed data related to the predetermined component in any time period is not in the set rotation speed range if the standard deviation or the steady-state error calculated for any time period is not in the set threshold range. Here, the set threshold range may be set according to experience of a person skilled in the art, too wide setting of the set threshold range may reduce accuracy of abnormal vibration identification, and too narrow setting of the set threshold range may reduce data amount of rotation speed data for subsequent analysis and calculation, and may even result in that rotation speed data for analysis and calculation does not exist.

In step S30, a frequency value for abnormal vibration analysis in each acceleration spectrum is determined.

Here, the abscissa of each of the converted acceleration frequency spectra may be a frequency value, and the ordinate may be a frequency amplitude value, and the frequency value for the abnormal vibration analysis may be selected based on a comparison of the frequency amplitude value with a frequency amplitude threshold value in an exemplary embodiment of the present invention.

In one aspect, the step of determining a frequency value for abnormal vibration analysis in each acceleration spectrum may include: and searching frequency points of which the frequency amplitude values are larger than the frequency amplitude threshold value in the acceleration frequency spectrum, and taking frequency values corresponding to the searched frequency points as frequency values for abnormal vibration analysis in the acceleration frequency spectrum. At this time, one or more frequency values for abnormal vibration analysis may be determined from an acceleration spectrum.

Alternatively, the step of determining a frequency value for abnormal vibration analysis in each acceleration spectrum may comprise: and determining whether a frequency amplitude value corresponding to a preset attention frequency point in the acceleration frequency spectrum is greater than a frequency amplitude threshold value, and if the frequency amplitude value corresponding to the preset attention frequency point is greater than the frequency amplitude threshold value, taking a frequency value corresponding to the preset attention frequency point as a frequency value for abnormal vibration analysis in the acceleration frequency spectrum. The determination mode only judges the preset attention frequency point, and is more accurate and efficient compared with the first mode of determining the frequency value for abnormal vibration analysis.

As an example, the preset attention frequency points may be frequency points a predetermined number of times before all the frequency points included in the acceleration spectrum are arranged in descending order of magnitude of the frequency amplitude value. For example, it can be considered that a frequency value corresponding to a frequency point with a maximum frequency amplitude value in the acceleration spectrum is a fundamental frequency of the predetermined component, a frequency value corresponding to a frequency point with a second maximum frequency amplitude value is a frequency multiplication factor of 2 of the predetermined component, and so on. At this time, the analysis of the preset concerned frequency point is equivalent to the analysis of the fundamental frequency and the frequency multiplication of the preset component, and the accuracy of the abnormal vibration identification can be improved.

In step S40, it is determined whether there is abnormal vibration in the predetermined component based on the frequency value for abnormal vibration analysis in each determined acceleration spectrum and the rotational speed data associated with the predetermined component for each period of time.

Specifically, it is determined whether a predetermined linear distribution rule is satisfied between a frequency value for abnormal vibration analysis in each acceleration frequency spectrum and rotation speed data associated with a predetermined component for each period of time, and it is determined that abnormal vibration exists in the predetermined component when the predetermined linear distribution rule is satisfied between the frequency value and the rotation speed data. And when the frequency value and the rotating speed data do not meet a preset linear distribution rule, determining that the preset component does not have abnormal vibration. Here, the predetermined linear distribution rule may be a distribution rule for embodying a linear relationship between the rotation speed and the frequency of the predetermined component.

Preferably, the step of determining whether a predetermined linear distribution law is satisfied between the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotational speed data associated with the predetermined component for each period of time may include: and calculating a rotating speed statistic value reflecting data characteristics of each time segment on the basis of the rotating speed data related to the preset component in each time segment, and determining whether a frequency value used for abnormal vibration analysis in each acceleration frequency spectrum and the rotating speed statistic value meet a preset linear distribution rule.

As an example, the revolution speed statistic value reflecting the data characteristic for each period may include any one of: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period. As an example, the effective value of the rotational speed data may refer to a maximum value of the rotational speed data associated with the predetermined component during the time period and

the ratio of (a) to (b).

The step of determining whether or not a predetermined linear distribution rule is satisfied between the frequency value for abnormal vibration analysis and the rotation speed statistic in each acceleration spectrum will be described below with reference to fig. 2, taking rotation speed data associated with a predetermined component as an example of the rotation speed statistic.

FIG. 2 shows a flowchart of the steps of determining whether a scatter point satisfies a predetermined linear distribution law according to an exemplary embodiment of the present invention.

In step S201, a rotation speed-frequency scattergram is plotted based on the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotation speed statistic value for each time period. Here, one scatter in the rotation speed-frequency scatter diagram may correspond to a rotation speed statistic for a time period and one frequency value for abnormal vibration analysis in the acceleration spectrum corresponding to the time period.

In step S202, a scatter point within a predetermined range of the linear model with a predetermined frequency is selected.

Preferably, all the scatter points included in the rotation speed-frequency scatter diagram can be screened, that is, the scatter points within the preset range of the predetermined frequency linear model are selected for subsequent abnormal vibration analysis. Here, a person skilled in the art may define the size of the preset range according to actual needs, and the accuracy of the identification may be improved by performing the abnormal vibration analysis using the scattering points within the preset range of the linear model with the predetermined frequency.

For example, the predetermined frequency linear model may be a model for performing abnormal vibration analysis with respect to a predetermined component, that is, the predetermined frequency linear model may be a model capable of expressing a linear relationship between a rotational speed and a frequency of the predetermined component. Taking the predetermined component as an example of a wind turbine, the predetermined frequency linear model may be a model for reflecting a linear relationship between a fundamental frequency/a frequency multiplication of the wind turbine and a rotation speed of the wind turbine, for example, the predetermined frequency linear model may be represented as f_nN × p × r/60, where f_nThe frequency is the fundamental wave frequency or the frequency multiplication of the fundamental wave frequency of the wind driven generator, p is the magnetic pole pair number of the wind driven generator, r is the rotating speed (rotor rotating speed) of the wind driven generator, and n is an integer greater than or equal to 1. When n is 1, f₁Representing the fundamental frequency of the wind-driven generator, when n is more than or equal to 2, f_nRepresenting the multiple of the fundamental frequency of the wind turbine.

For the case of wind turbine fundamental frequency (i.e., n is 1), the preset range may refer to the scattered point boundary

Or

Within the contained area. Similarly, for the case of a multiple of the fundamental frequency of the wind turbine (i.e., n ≧ 2), the predetermined range may refer to the scattered point boundary

Or

Within the contained area.

As an example, the value ranges of the parameter b and the parameter k in the above-mentioned scatter point boundary may be determined based on the corresponding grid-connected rotation speed range, the set rotation speed range (or the set threshold value range), and the number of pole pairs of the wind turbine.

In step S203, it is determined whether a predetermined linear distribution law of a predetermined frequency linear model is satisfied between the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotation speed statistic value in each time period based on the selected scatter point. That is, the predetermined linear distribution rule may be determined by a frequency linear model, and when the predetermined linear distribution rule of the predetermined frequency linear model is satisfied between the frequency value and the rotation speed statistic value, it is determined that the predetermined component has abnormal vibration corresponding to the predetermined frequency linear model.

For example, when the predetermined frequency linear model is a model for reflecting a linear relationship between a fundamental frequency of the wind turbine and a rotational speed of the wind turbine, if a predetermined linear distribution rule of the predetermined frequency linear model is satisfied between the frequency value and the rotational speed statistic value, it is determined that fundamental frequency abnormal vibration exists in the wind turbine. And when the preset frequency linear model is a model for reflecting the linear relation between the frequency multiplication of the fundamental frequency of the wind driven generator and the rotating speed of the wind driven generator, if the frequency value and the rotating speed statistic value meet the preset linear distribution rule of the preset frequency linear model, determining that the wind driven generator has abnormal vibration of the frequency multiplication of the fundamental frequency.

It should be understood that the manner of determining whether the predetermined linear distribution rule is satisfied between the frequency value and the rotation speed statistic shown in fig. 2 is merely an example, and those skilled in the art may determine in other manners.

Fig. 3A to 3D respectively show exemplary graphs of a rotational speed-frequency scattergram according to an exemplary embodiment of the present invention.

Fig. 3A and 3B show a rotation speed-frequency scattergram when the predetermined component is a wind turbine, vibration acceleration data of the predetermined component is vibration acceleration data in a first predetermined direction and a second predetermined direction, respectively, with a rotation speed statistic value (e.g., an average rotation speed) on the abscissa and a frequency value on the ordinate, curve 1 representing a predetermined frequency linear model of a fundamental frequency of the wind turbine, and curve 2 representing a predetermined frequency linear model of a frequency doubling (2 frequency doubling) of the fundamental frequency of the wind turbine. Taking curve 1 as an example, when it is desired to identify whether the wind turbine has abnormal vibration of the fundamental frequency, a scatter point in a predetermined range around curve 1 may be selected, whether the selected scatter point conforms to a predetermined linear distribution rule of the linear model of the predetermined frequency is determined by linear regression parameter estimation or a determination manner of an objective function, and if the selected scatter point conforms to the predetermined linear distribution rule of the linear model of the predetermined frequency, it is indicated that the wind turbine has abnormal vibration of the fundamental frequency.

Fig. 3C and 3D show a rotation speed-frequency scattergram when the predetermined component is a wind turbine, vibration acceleration data of the predetermined component is vibration acceleration data in a first predetermined direction or a second predetermined direction, an abscissa is a rotation speed statistic value (e.g., an average rotation speed), an ordinate is a frequency value, a curve 1 represents a predetermined frequency linear model of a fundamental frequency of the wind turbine, a curve 2 represents a predetermined frequency linear model of 2-fold frequency of the fundamental frequency of the wind turbine, and a curve 3 represents a predetermined frequency linear model of 3-fold frequency of the fundamental frequency of the wind turbine, respectively. The specific identification method of the abnormal vibration is the same as the identification method shown in fig. 3A and 3B, and the details of this part of the present invention are not repeated.

Preferably, whether or not the predetermined linear distribution law is satisfied between the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotational speed data associated with the predetermined component in each period of time may be determined by a predetermined frequency linear model for reflecting the predetermined linear distribution law or by an objective function for indicating a distance of each scatter point (scatter point formed by the frequency value and the rotational speed data) to the predetermined frequency linear model.

The step of determining whether the scatter satisfies the predetermined linear distribution law based on the predetermined frequency linear model will be described with reference to fig. 4.

FIG. 4 shows a flowchart of the steps of determining whether a scatter point satisfies a predetermined linear distribution law based on a predetermined frequency linear model according to an exemplary embodiment of the present invention.

As shown in fig. 4, in step S401, linear regression is performed on the frequency values corresponding to the selected scattering points and the rotation speed data, so as to obtain model parameters of linear regression. As an example, a least square method or a maximum likelihood method may be used to perform linear regression on the frequency values and the rotation speed data corresponding to the scattered points to obtain a linear regression model, and then determine model parameters of the linear regression model. However, the invention is not limited thereto, and linear regression analysis may be performed on scatter points in other ways.

In step S402, a difference between the model parameter and a specified parameter of a predetermined component is calculated, and it is determined whether the difference is greater than a first set value.

As an example, when the predetermined component is a wind turbine, the specified parameter may be a number of pairs of magnetic poles of the wind turbine, when the predetermined component is a generator slot, the specified parameter may be a number of pairs of magnetic poles of the wind turbine, when the predetermined component is a gear box, the specified parameter may be a number of teeth of a power gear or a number of teeth of a driven gear, and when the predetermined component is a rolling bearing, the specified parameter may be a failure coefficient of the rolling bearing (e.g., a failure coefficient corresponding to one of an inner ring, an outer ring, a rolling body, and a cage of the rolling bearing).

For example, taking the predetermined component as a wind turbine as an example, the frequency values and the rotational speed data corresponding to the selected scattering points are linearly regressed to obtain model parameters

Then model parameters are calculated

And the difference is made with the magnetic pole pair number p of the wind driven generator.

If the difference between the model parameter and the specified parameter of the predetermined component is not greater than the first set value, step S403 is performed: and determining a preset linear distribution rule which meets a preset frequency linear model between the frequency value corresponding to the selected scattered point and the rotating speed data.

If the difference between the model parameter and the specified parameter of the predetermined component is greater than the first set value, step S404 is performed: and determining that the frequency value corresponding to the selected scattering point and the rotating speed data do not meet the preset linear distribution rule of the preset frequency linear model.

The step of determining whether the scatter point satisfies the predetermined linear distribution rule based on the objective function is described below with reference to fig. 5.

FIG. 5 shows a flowchart of steps for determining whether a scatter point satisfies a predetermined linear distribution law based on an objective function according to an exemplary embodiment of the present invention.

As shown in fig. 5, in step S501, an objective function is established. Here, the objective function may indicate a distance of each scatter point to the predetermined frequency linear model. Preferably, the objective function may indicate a root mean square value of a distance of each of the scatter points to the predetermined frequency linear model.

For example, the objective function can be represented by the following formula:

in the formula (1), y represents an objective function, N is the number of scattered points, f_i' is the ordinate value, r, of the ith scatter point in the rotational speed-frequency scatter diagram_i' is and f_i' abscissa value in the corresponding rotational speed-frequency scattergram, i.e., rotational speed statistic corresponding to the i-th scattergram.

In step S502, a value of the objective function is obtained by substituting the frequency value corresponding to the selected scattering point and the rotational speed data into the objective function. For example, the value of the objective function y can be calculated by the above formula (1).

In step S503, it is determined whether the value of the objective function is greater than a second set value.

If the value of the objective function is not greater than the second set value, step S504 is performed: and determining a preset linear distribution rule which meets a preset frequency linear model between the frequency value corresponding to the selected scattered point and the rotating speed data.

If the value of the objective function is greater than the second set value, step S505 is performed: and determining that the frequency value corresponding to the selected scattered point and the rotating speed data do not meet the preset linear distribution rule of a preset frequency linear model.

Preferably, the value ranges of the first and second setting values may be determined based on a scatter boundary (i.e., a preset range of a predetermined frequency linear model).

Fig. 6 illustrates a structural diagram of an apparatus for recognizing abnormal vibration according to an exemplary embodiment of the present invention.

As shown in fig. 6, the apparatus for identifying abnormal vibration according to an exemplary embodiment of the present invention includes an operation data acquisition module 10, a time-frequency conversion module 20, a frequency value determination module 30, and an abnormal vibration analysis module 40.

Specifically, the operation data acquisition module 10 is used for acquiring operation data of a predetermined component of the wind turbine generator system in a plurality of time periods. Here, the operation data may include vibration acceleration data of the predetermined component and rotational speed data related to the predetermined component.

In a third embodiment, the predetermined component may be a gearbox including a power gear and a driven gear therein. In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of a power gear in the gear box or vibration acceleration data of a driven gear, and the rotation speed data associated with the predetermined component may be rotation speed data of a shaft on which the power gear is located or rotation speed data of a shaft on which the driven gear is located. Accordingly, the types of abnormal vibration may include a power gear mesh frequency vibration abnormality, a multiple frequency vibration abnormality of the power gear mesh frequency, a driven gear mesh frequency vibration abnormality, and a multiple frequency vibration abnormality of the driven gear mesh frequency. Here, the meshing frequency of the power gear (or the driven gear) is equal to the number of teeth of the power gear (or the number of teeth of the driven gear) × the rotation speed of the shaft on which the power gear (or the driven gear) is located/60.

In the fourth embodiment, the predetermined component may be a rolling bearing, which may be referred to herein as any one of a plurality of rolling bearings in the wind turbine generator set. In this case, the vibration acceleration data of the predetermined component may be vibration acceleration data of a bearing housing of the rolling bearing, and the rotational speed data associated with the predetermined component may be rotational speed data of the rolling bearing. Accordingly, the type of abnormal vibration may include a rolling bearing failure characteristic frequency vibration abnormality and/or a double frequency vibration abnormality of the rolling bearing failure characteristic frequency. Here, it should be understood that the rolling bearing may include a bearing inner ring, an outer ring, rolling bodies, a cage, and accordingly, the predetermined component may refer to the bearing inner ring, the outer ring, the rolling bodies, or the cage. For example, taking a predetermined member as a rolling element of a rolling bearing as an example, it is possible to identify whether or not there is an abnormal vibration of a failure characteristic frequency of the rolling element or an abnormal vibration of a frequency doubling of the failure characteristic frequency of the rolling element. The inner ring, the outer ring, the rolling body and the retainer of the bearing correspond to respective failure coefficients, and the failure characteristic frequency can be obtained by the product of the failure coefficients and the rotation frequency.

The time-frequency conversion module 20 is configured to perform frequency domain conversion on the vibration acceleration data of the predetermined component in multiple time periods, so as to obtain multiple acceleration frequency spectrums corresponding to each other.

For example, the time-frequency conversion module 20 may be configured to perform frequency domain conversion on the vibration acceleration data of the predetermined component in any one of the time periods to obtain an acceleration spectrum corresponding to the vibration acceleration data in the any one time period, that is, the vibration acceleration data in one time period corresponds to one acceleration spectrum. As an example, the time-frequency conversion module 20 may perform frequency domain conversion on the vibration acceleration data of the predetermined component in a plurality of time periods through fast fourier transform, but the invention is not limited thereto, and may also perform frequency domain conversion in other manners.

Preferably, the time-frequency conversion module 20 may be configured to screen the acquired operation data of the predetermined component in a plurality of time periods in advance based on the rotation speed data related to the predetermined component, and perform frequency domain conversion on the vibration acceleration data of the predetermined component in the screened operation data in the plurality of time periods.

For example, the time-frequency conversion module 20 may be configured to determine whether the rotation speed data related to the predetermined component in each time period is within a set rotation speed range, and if the rotation speed data related to the predetermined component in any time period is within the set rotation speed range, perform frequency domain conversion on the vibration acceleration data of the predetermined component in any time period to obtain a corresponding acceleration spectrum.

As an example, the time-frequency conversion module 20 may be configured to calculate a standard deviation or a steady-state error of the rotation speed data related to the predetermined component in each time period, determine that the rotation speed data related to the predetermined component in any time period is within a set rotation speed range if the standard deviation or the steady-state error calculated for any time period is within a set threshold range, and determine that the rotation speed data related to the predetermined component in any time period is not within the set rotation speed range if the standard deviation or the steady-state error calculated for any time period is not within the set threshold range. Here, the setting threshold range may be set according to experience of those skilled in the art.

And a frequency value determining module 30, configured to determine a frequency value for abnormal vibration analysis in each acceleration frequency spectrum.

Here, the abscissa of each of the converted acceleration frequency spectra may be a frequency value, and the ordinate may be a frequency amplitude value, and the frequency value determination module 30 may select a frequency value for abnormal vibration analysis based on a comparison of the frequency amplitude value and a frequency amplitude threshold value in an exemplary embodiment of the present invention.

In one case, the frequency value determining module 30 may be configured to search a frequency point in the acceleration frequency spectrum, where the frequency amplitude value is greater than the frequency amplitude threshold, and use a frequency value corresponding to the searched frequency point as the frequency value for analyzing the abnormal vibration in the acceleration frequency spectrum. At this time, one or more frequency values for abnormal vibration analysis may be determined from an acceleration spectrum.

In another case, the frequency value determining module 30 may be configured to determine whether a frequency amplitude value corresponding to a preset frequency of interest in the acceleration spectrum is greater than a frequency amplitude threshold, and if the frequency amplitude value corresponding to the preset frequency of interest is greater than the frequency amplitude threshold, take the frequency value corresponding to the preset frequency of interest as the frequency value for analyzing the abnormal vibration in the acceleration spectrum.

As an example, the preset attention frequency points may be frequency points a predetermined number of times before all the frequency points included in the acceleration spectrum are arranged in descending order of magnitude of the frequency amplitude value. For example, it can be considered that a frequency value corresponding to a frequency point with a maximum frequency amplitude value in the acceleration spectrum is a fundamental frequency of the predetermined component, a frequency value corresponding to a frequency point with a second maximum frequency amplitude value is a frequency multiplication factor of 2 of the predetermined component, and so on. In this case, analyzing the frequency point of interest is equivalent to analyzing the fundamental frequency and the frequency multiplication of the predetermined component, and the accuracy of identifying the abnormal vibration can be improved.

And the abnormal vibration analysis module 40 is used for determining whether the preset component has abnormal vibration or not based on the frequency value used for the abnormal vibration analysis in each determined acceleration frequency spectrum and the rotating speed data related to the preset component in each time period.

Specifically, the abnormal vibration analyzing module 40 is configured to determine whether a predetermined linear distribution rule is satisfied between a frequency value used for the abnormal vibration analysis in each acceleration frequency spectrum and rotation speed data associated with a predetermined component in each time period, and determine that the predetermined component has abnormal vibration when the predetermined linear distribution rule is satisfied between the frequency value and the rotation speed data. And when the frequency value and the rotating speed data do not meet a preset linear distribution rule, determining that the preset component does not have abnormal vibration. Here, the predetermined linear distribution rule may be a distribution rule for embodying a linear relationship between the rotation speed and the frequency of the predetermined component.

Preferably, the abnormal vibration analyzing module 40 may be configured to calculate a rotation speed statistic value reflecting data characteristics for each time segment based on the rotation speed data associated with the predetermined component for each time segment, and determine whether a predetermined linear distribution rule is satisfied between a frequency value used for the abnormal vibration analysis in each acceleration spectrum and the rotation speed statistic value.

As an example, the revolution speed statistic value reflecting the data characteristic for each period may include any one of: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period. Here, the effective value of the rotational speed data may refer to a maximum value of the rotational speed data associated with the predetermined component during the period of time and

the ratio of (a) to (b).

The process of determining whether or not a predetermined linear distribution rule is satisfied between the frequency value for abnormal vibration analysis and the rotation speed statistic in each acceleration spectrum will be described below with reference to fig. 7, taking rotation speed data associated with a predetermined component as an example of the rotation speed statistic.

Fig. 7 illustrates a structural diagram of an abnormal vibration analysis module according to an exemplary embodiment of the present invention.

As shown in fig. 7, the abnormal vibration analyzing module 40 according to an exemplary embodiment of the present invention may include a scatter diagram drawing module 41, a scatter filter module 42, and a linear distribution determining module 43.

Specifically, the scatter diagram drawing module 41 is configured to draw a rotation speed-frequency scatter diagram based on the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotation speed statistic value in each time period. Here, one scatter in the rotation speed-frequency scatter diagram may correspond to a rotation speed statistic for a time period and one frequency value for abnormal vibration analysis in the acceleration spectrum corresponding to the time period.

And a scatter screening module 42, configured to select a scatter within a preset range of the linear model with a predetermined frequency.

Preferably, the scatter screening module 42 may be configured to screen all the scatters included in the rotation speed-frequency scattergram, that is, select the scatters within a preset range of the predetermined frequency linear model for subsequent abnormal vibration analysis. Here, a person skilled in the art may define the size of the preset range according to actual needs, and the accuracy of the identification may be improved by performing the abnormal vibration analysis using the scattering points within the preset range of the linear model with the predetermined frequency.

And a linear distribution determination module 43, configured to determine whether a predetermined linear distribution rule of a predetermined frequency linear model is satisfied between the frequency value for abnormal vibration analysis in each acceleration spectrum and the rotation speed statistic value in each time period based on the selected scatter point. That is, the predetermined linear distribution rule may be determined by a frequency linear model, and the linear distribution determining module 43 is configured to determine that the predetermined component has abnormal vibration corresponding to the predetermined frequency linear model when the predetermined linear distribution rule of the predetermined frequency linear model is satisfied between the frequency value and the rotation speed statistic value.

The following describes a process of determining whether the scatter point satisfies a predetermined linear distribution law based on a predetermined frequency linear model.

A linear distribution determining module 43, configured to perform linear regression on the frequency value corresponding to the selected scattering point and the rotation speed data to obtain a model parameter of the linear regression, calculate a difference between the model parameter and an assigned parameter of the predetermined component, and determine whether the difference is greater than a first set value, and if the difference between the model parameter and the assigned parameter of the predetermined component is not greater than the first set value, determine that a predetermined linear distribution rule of a linear model with a predetermined frequency is satisfied between the frequency value corresponding to the selected scattering point and the rotation speed data. And if the difference value of the model parameter and the specified parameter of the preset component is larger than a first set value, determining that the preset linear distribution rule of the preset frequency linear model is not satisfied between the frequency value corresponding to the selected dispersion point and the rotating speed data.

The process of determining whether the scatter point satisfies the predetermined linear distribution rule based on the objective function is described below with reference to fig. 8.

As shown in fig. 8, the linear distribution determining module 43 according to an exemplary embodiment of the present invention may include an objective function establishing submodule 431, an objective function value calculating submodule 432, and a distribution rule determining submodule 433.

In particular, the objective function establishing submodule 431 is used for establishing the objective function. Here, the objective function may indicate a distance of each scatter point to the predetermined frequency linear model. Preferably, the objective function may indicate a root mean square value of a distance of each of the scatter points to the predetermined frequency linear model.

And the objective function value calculation submodule 432 is configured to obtain a value of the objective function by substituting the frequency value and the rotation speed data corresponding to the selected scattering point into the objective function.

And a distribution rule determining submodule 433, configured to determine whether the value of the target function is greater than a second set value. If the value of the objective function is not greater than the second set value, the distribution rule determining submodule 433 determines that a predetermined linear distribution rule of a predetermined frequency linear model is satisfied between the frequency value corresponding to the selected scattering point and the rotational speed data. If the value of the objective function is greater than the second set value, the distribution rule determining submodule 433 is configured to determine that a predetermined linear distribution rule of a predetermined frequency linear model is not satisfied between the frequency value corresponding to the selected scattering point and the rotation speed data. Preferably, the value ranges of the first and second setting values may be determined based on a scatter boundary (i.e., a preset range of a predetermined frequency linear model).

There is also provided, in accordance with an exemplary embodiment of the present invention, a computing device. The computing device includes a processor and a memory. The memory is for storing a computer program. The computer program is executed by a processor to cause the processor to execute the method of identifying abnormal vibrations as described above.

There is also provided, in accordance with an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program. The computer readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the above-described method of identifying abnormal vibration. The computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths).

By adopting the method and the equipment for identifying the abnormal vibration, which are disclosed by the exemplary embodiment of the invention, the component with the abnormal frequency vibration in the wind generating set can be timely and accurately positioned, and a powerful support is provided for quickly and effectively evaluating the vibration state of the preset component. In addition, the efficiency of unit fault location can be effectively improved, and the operation and maintenance cost can be saved.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims.

Claims

1. A method of identifying fundamental frequency abnormal vibrations of a predetermined component of a wind park, the method comprising:

acquiring operation data of a preset component of a wind generating set in a plurality of time periods, wherein the operation data comprises vibration acceleration data of the preset component and rotating speed data related to the preset component;

respectively carrying out frequency domain conversion on the vibration acceleration data of the predetermined component in the plurality of time periods to obtain a plurality of acceleration frequency spectrums corresponding to each other;

determining a frequency value for abnormal vibration analysis in each acceleration frequency spectrum;

drawing a rotating speed-frequency scatter diagram based on the frequency values and the rotating speed data, wherein one scattering point in the rotating speed-frequency scatter diagram corresponds to the rotating speed data of a time period and one frequency value used for abnormal vibration analysis in an acceleration frequency spectrum corresponding to the time period;

selecting scattered points within a preset range of a linear model with a preset frequency;

performing linear regression on the frequency value corresponding to the selected scattering point and the rotating speed data to obtain a model parameter of the linear regression;

calculating a difference between the model parameter and a specified parameter of the predetermined component;

when the difference value is not larger than a first set value, determining that the frequency value corresponding to the selected scattering point and the rotating speed data meet a preset linear distribution rule of a preset frequency linear model;

the predetermined frequency linear model is a model for reflecting a linear relation between a fundamental frequency of the wind power generator and a rotation speed of the wind power generator, and when it is determined that the predetermined linear distribution rule is satisfied, it is determined that abnormal vibration of the fundamental frequency exists in the predetermined component, wherein the predetermined component includes a generator tooth groove, a gear box or a rolling bearing.

2. The method of claim 1, wherein the step of determining a frequency value for abnormal vibration analysis in each acceleration spectrum comprises:

searching for a frequency point of which the frequency amplitude value is greater than a frequency amplitude threshold value in the acceleration frequency spectrum;

and taking the frequency value corresponding to the searched frequency point as the frequency value for analyzing the abnormal vibration in the acceleration frequency spectrum.

3. The method of claim 1, wherein the step of determining a frequency value for abnormal vibration analysis in each acceleration spectrum comprises:

determining whether a frequency amplitude value corresponding to a preset concerned frequency point in the acceleration frequency spectrum is greater than a frequency amplitude threshold value;

and if the frequency amplitude value corresponding to the preset attention frequency point is larger than a frequency amplitude threshold value, taking the frequency value corresponding to the preset attention frequency point as a frequency value for analyzing abnormal vibration in the acceleration frequency spectrum.

4. The method according to claim 3, wherein the predetermined frequency points of interest are a predetermined number of previous frequency points in which all frequency points included in the acceleration spectrum are arranged in descending order according to the magnitude of the frequency amplitude value.

5. The method of claim 1, wherein the method further comprises:

calculating a rotation speed statistic value reflecting data characteristics for each period of time based on the rotation speed data to identify abnormal vibration of the fundamental wave frequency of the predetermined component based on the rotation speed statistic value.

6. The method of claim 5, wherein the data-characteristic-reflecting rotational speed statistics for each time period comprise any one of: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period.

7. The method of claim 1, wherein the step of frequency-domain converting the vibration acceleration data of the predetermined component over the plurality of time periods to obtain a corresponding plurality of acceleration spectra comprises:

determining whether the rotating speed data related to the preset component in each time period is in a set rotating speed range;

and if the rotating speed data related to the preset component in any time period is in a set rotating speed range, performing frequency domain conversion on the vibration acceleration data of the preset component in any time period to obtain a corresponding acceleration frequency spectrum.

8. The method of claim 7, wherein the step of determining whether the rotational speed data associated with the predetermined component for each time period is within a set rotational speed range comprises:

calculating a standard deviation or steady state error of the rotational speed data associated with the predetermined component for each time period;

determining that the rotational speed data associated with the predetermined component for any one time period is within a set rotational speed range if the standard deviation or steady state error calculated for the time period is within a set threshold range;

and if the standard deviation or the steady-state error calculated for any time period is not in the set threshold value range, determining that the rotating speed data related to the predetermined component in any time period is not in the set rotating speed range.

9. The method of claim 1,

when the preset component is a generator tooth space, the vibration acceleration data of the preset component is the vibration acceleration data of a cabin of the wind driven generator, the rotating speed data related to the preset component is the rotating speed data of the wind driven generator, and the type of abnormal vibration comprises generator tooth space frequency vibration abnormality and frequency doubling abnormal vibration of the generator tooth space frequency;

when the preset component is a gearbox, the vibration acceleration data of the preset component is vibration acceleration data of a power gear or a driven gear in the gearbox, the rotating speed data related to the preset component is rotating speed data of a shaft where the power gear or the driven gear is located, and the type of abnormal vibration comprises frequency multiplication abnormal vibration of the meshing frequency of the power gear and the meshing frequency of the power gear or frequency multiplication abnormal vibration of the meshing frequency of the driven gear and the meshing frequency of the driven gear;

when the preset component is a rolling bearing, the vibration acceleration data of the preset component is the vibration acceleration data of a bearing seat of the rolling bearing, the rotating speed data related to the preset component is the rotating speed data of the rolling bearing, and the type of abnormal vibration comprises rolling bearing fault characteristic frequency vibration abnormality and frequency doubling abnormal vibration of the rolling bearing fault characteristic frequency.

10. An apparatus for identifying fundamental frequency abnormal vibrations of a predetermined component of a wind turbine generator set, said apparatus comprising:

the operation data acquisition module is used for acquiring operation data of a preset component of the wind generating set in a plurality of time periods, wherein the operation data comprises vibration acceleration data of the preset component and rotating speed data related to the preset component;

the time-frequency conversion module is used for respectively carrying out frequency domain conversion on the vibration acceleration data of the preset component in the time periods so as to obtain a plurality of corresponding acceleration frequency spectrums;

the frequency value determining module is used for determining a frequency value used for abnormal vibration analysis in each acceleration frequency spectrum;

an abnormal vibration analysis module for determining whether there is abnormal vibration in the predetermined component based on the frequency value for abnormal vibration analysis in each acceleration frequency spectrum determined and the rotational speed data associated with the predetermined component for each period of time,

wherein the abnormal vibration analysis module includes:

the scatter diagram drawing module is used for drawing a rotating speed-frequency scatter diagram based on the frequency values and the rotating speed data, wherein one scatter point in the rotating speed-frequency scatter diagram corresponds to the rotating speed data of a time period and one frequency value used for abnormal vibration analysis in an acceleration frequency spectrum corresponding to the time period;

the scattered point screening module is used for selecting scattered points within a preset range of the linear model with the preset frequency;

a linear distribution determining module, configured to perform linear regression on the frequency value corresponding to the selected scattering point and the rotation speed data to obtain a linear regression model parameter, calculate a difference between the model parameter and a specified parameter of the predetermined component, and determine that a predetermined linear distribution rule of the predetermined frequency linear model is satisfied between the frequency value corresponding to the selected scattering point and the rotation speed data when the difference is not greater than a first set value,

11. The apparatus according to claim 10, wherein the frequency value determining module is configured to search for a frequency point in the acceleration spectrum where the frequency amplitude value is greater than the frequency amplitude threshold value, and use a frequency value corresponding to the searched frequency point as the frequency value in the acceleration spectrum for abnormal vibration analysis.

12. The apparatus according to claim 10, wherein the frequency value determining module is configured to determine whether a frequency amplitude value corresponding to a preset frequency of interest in the acceleration spectrum is greater than a frequency amplitude threshold value, and if the frequency amplitude value corresponding to the preset frequency of interest is greater than the frequency amplitude threshold value, the frequency value determining module takes the frequency value corresponding to the preset frequency of interest as the frequency value for analyzing the abnormal vibration in the acceleration spectrum.

13. The apparatus according to claim 12, wherein the preset frequency points of interest are a predetermined number of previous frequency points in which all frequency points included in the acceleration spectrum are arranged in descending order according to the magnitude of the frequency amplitude value.

14. The apparatus according to claim 10, wherein the abnormal vibration analyzing module is configured to calculate a rotation speed statistic value reflecting a data characteristic for each period of time based on the rotation speed data to identify the abnormal vibration of the fundamental wave frequency of the predetermined component based on the rotation speed statistic value.

15. The apparatus of claim 14, wherein the data-characteristic-reflecting rotational speed statistics for each time period comprise any one of: an average value of the rotational speed data related to the predetermined component during the time period, a median value of the rotational speed data related to the predetermined component during the time period, and an effective value of the rotational speed data related to the predetermined component during the time period.

16. The apparatus of claim 10, wherein the time-frequency conversion module is configured to determine whether the rotation speed data associated with the predetermined component in each time period is within a set rotation speed range, and if the rotation speed data associated with the predetermined component in any time period is within the set rotation speed range, perform frequency domain conversion on the vibration acceleration data of the predetermined component in any time period to obtain a corresponding acceleration spectrum.

17. The apparatus of claim 16, wherein the time-frequency transform module is configured to calculate a standard deviation or a steady state error of the rotational speed data associated with the predetermined component for each time period, determine that the rotational speed data associated with the predetermined component for any time period is within a set rotational speed range if the standard deviation or the steady state error calculated for the time period is within a set threshold range,

18. The apparatus of claim 10,

19. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of identifying fundamental frequency abnormal vibrations of a predetermined component of a wind park as claimed in any one of claims 1 to 9.

20. A computing device, the computing device comprising:

a processor;

memory storing a computer program which, when executed by the processor, implements the method of identifying fundamental frequency abnormal vibrations of a predetermined component of a wind park according to any one of claims 1 to 9.