CN113822565B - Method for graded and refined analysis of time-frequency characteristics of fan monitoring data - Google Patents
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Abstract
The invention discloses a method for carrying out graded refinement analysis on time-frequency characteristics of fan monitoring data, which comprises the following steps of firstly, preprocessing the monitoring data according to the fan monitoring data; secondly, respectively calculating instantaneous characteristic frequencies and response information sets in the monitoring data by utilizing a time domain statistical method, a Fourier transform (FFT) method, a short-time Fourier transform (STFT) method and an adaptive time-frequency analysis method or a multi-resolution time-frequency analysis method; and finally, calculating and separating the first-stage instantaneous characteristic frequency and the second-stage instantaneous characteristic frequency according to a given instantaneous characteristic frequency difference threshold value, and further obtaining the time-frequency response characteristics of the fan after hierarchical refinement. Based on the obtained first-stage and second-stage instantaneous characteristic frequencies and corresponding characteristic responses, the method can effectively and accurately analyze the response characteristics of the fan without analyzing the operation modal characteristics and operation interference characteristics of the fan in advance, and achieves the purposes of service wind power structure state research and judgment and operation maintenance.
Description
Technical Field
The invention relates to the field of offshore wind power or onshore wind power, in particular to a method for graded and refined analysis of time-frequency characteristics of fan monitoring data.
Background
Fans operate in complex load environments. In general, when monitoring the state of a fan, the obtained state monitoring data not only contains a certain environmental noise component, but also is interfered by electromechanical equipment of the fan in operation. Generally, different types of interference factors in the fan condition monitoring have different characteristics, and usually show different action frequency domains and action periods.
However, since the working environment and working state of the fans are different, various different types of interference factors in the operation of different fans are difficult to be analyzed and processed uniformly; therefore, before the operation characteristics and the operation interference characteristics of a certain fan are obtained through pre-analysis, the response characteristics of the fan cannot be effectively and accurately analyzed by using a conventional time-frequency analysis method. The conventional time-frequency analysis method usually has fixed time and frequency resolution, and can only carry out general screening analysis on fan response; meanwhile, although the time-frequency multi-resolution analysis method can accurately capture the time-frequency domain characteristics of the fan, the time-frequency information amount of the corresponding method is too large, and the operation characteristics and the operation interference characteristics of the fan cannot be distinguished.
At present, the requirements of state evaluation and operation maintenance of the established wind power project are obvious. Response characteristics of monitoring data of the fan are analyzed and recognized, actual response state characteristics of the fan structure are timely and accurately mastered and evaluated, and the method is very important for operating and maintaining decisions of the fan.
Disclosure of Invention
The invention aims to provide a method for graded and refined analysis of time-frequency characteristics of fan monitoring data, which is used for obtaining a first-stage instantaneous characteristic frequency, a second-stage instantaneous characteristic frequency and corresponding characteristic responses, so that the actual response state characteristics of a fan structure can be accurately mastered and evaluated, and the purposes of service wind power structure state evaluation and operation maintenance are achieved.
The technical scheme adopted by the invention is as follows: a method for graded and refined analysis of time-frequency characteristics of fan monitoring data comprises the following steps:
(1) According to fan state monitoring data (displacement, acceleration, strain or inclination and the like), calculating a monitoring data extreme value and characteristics thereof, and time-course statistical characteristics, separating direct-current components, analyzing and extracting trend items, checking and removing abnormal values from the monitoring data to obtain a preprocessing result;
according to existing fan state monitoring data, calculating a monitoring data extreme value and characteristics thereof, calculating monitoring data time-course statistical characteristics, calculating and separating monitoring data direct-current components, analyzing monitoring data trend items, and performing abnormal value check for subsequent processing;
the monitoring data extreme value comprises a monitoring data maximum value, a monitoring data minimum value, an absolute value maximum value, a monitoring data minimum value and the like;
the monitoring data characteristics comprise the occurrence frequency of the extreme value of the monitoring data, the corresponding occurrence time and the like;
the time course statistical characteristics comprise but are not limited to mean values, mean square values, variances, probability density functions, dimensional parameter indexes, dimensionless parameter indexes and the like; the dimensionless parameter indexes include, but are not limited to, a square root amplitude, an average amplitude, a mean square amplitude, a peak value, and the like, and the dimensionless parameter indexes include, but are not limited to, a waveform index, a peak value index, a pulse index, a margin index, a skewness index, a kurtosis index, and the like.
The direct current component refers to a non-zero mean value component in monitoring data, is related to the offset of monitoring response, and can generate an impulse response function at a zero frequency in a frequency domain;
the trend item refers to frequency components in the monitoring data, wherein the period of the frequency components is greater than the time interval length of the monitoring data; correlation analysis methods include, but are not limited to, least squares, filtering methods, and the like.
The abnormal value test includes but is not limited to analyzing and rejecting abnormal values by using a 3 sigma criterion (a standard deviation of not more than three times) and other methods;
(2) Analyzing the result obtained in the step (1) by using a time domain statistical method, a Fourier transform (FFT) method and a short-time Fourier transform (STFT) method in order to analyze the correlation and the power spectrum characteristics of the monitoring data, and acquiring a more significant instantaneous characteristic frequency and response information set in the monitoring data according to a curve peak-valley value in the analysis result;
the steps include:
(2.1) acquiring the frequency characteristics of the monitoring data of frequency domain distribution (without time information) by utilizing Fourier transform (FFT);
(2.2) acquiring windowed monitoring data time-frequency distribution characteristics by using short-time Fourier transform (STFT);
(2.3) analyzing and extracting a frequency value (instantaneous characteristic frequency) { f ] which is more significant in the monitoring data, according to (2.1) and (2.2) above 1 And its response information set { A } 1 }；
The monitoring data frequency characteristics comprise frequency domain transformation characteristics such as frequency spectrum, self-power spectrum and cross-power spectrum;
the windowing refers to a method of performing frequency domain transform (FFT) on the monitoring data within a time Δ t of t = τ to obtain a frequency domain response with a resolution Δ f of the time t = τ, and further obtaining a distribution of the monitoring data over a time frequency window Δ t × Δ f, that is, the time frequency resolution is Δ t × Δ f;
the windowed time-frequency distribution characteristic refers to the power spectrum characteristic of monitoring data obtained by calculating given time resolution delta t and frequency resolution delta f;
the more significant frequency and the response information thereof refer to the frequency corresponding to the power spectrum extreme value appearing in the windowed time-frequency distribution characteristic and the response power spectrum amplitude value thereof.
(3) Analyzing the result obtained in the step (1) by using a self-adaptive time-frequency analysis method or a multiresolution time-frequency analysis method, solving different signal mode components (or different frequency resolution components), and acquiring instantaneous characteristic frequencies { f ] of different time-frequency resolutions in the monitoring data according to peak-valley values of frequency domain curves corresponding to the different signal components 2 And a set of response messages { A } 2 }；
The self-adaptive time-frequency analysis method comprises self-adaptive time-frequency analysis methods such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD);
the multi-resolution time-frequency analysis method comprises but is not limited to multi-resolution time-frequency analysis methods such as wavelet multi-resolution analysis (WMRA), wavelet packet multi-resolution analysis (WPMRA) and the like;
the self-adaptive time-frequency analysis method or the multi-resolution time-frequency analysis method comprises the following steps:
(3.1) calculating D of different resolution components (or eigenmode IM) of intercepted monitoring data i ；
(3.2) calculating different resolution Components D by FFT i The frequency spectrum of (a); selecting time resolution delta t and frequency resolution delta f according to the requirement of the problem, and calculating D by using STFT i Windowing the time frequency spectrum;
and (3.3) analyzing and extracting the corresponding frequency of the power spectrum extreme value with different resolutions appearing in the monitoring data and the corresponding response power spectrum amplitude value thereof according to the (3.1) and the (3.2), namely the instantaneous characteristic frequency { f } 2 And a set of response messages { A } 2 }；
In the step (3), the instantaneous characteristic frequency and response information of a time domain and a frequency domain are separated by using a self-adaptive time-frequency analysis method or a multi-resolution time-frequency analysis method, and because the result obtained in the step (2) is the characteristic frequency and response which are approximately weighted (or averaged) in the time domain, the time domain characteristic is reserved and the frequency domain characteristic is refined in the step (3) compared with the step (2), so that the result obtained in the step (3) is richer compared with the step (2), and a part of the instantaneous characteristic frequency and response information intersection can exist between the two;
in the step (2) and the step (3), because different analysis steps are performed on the results obtained in the step (1) and the results obtained in the subsequent step (4) are processed and analyzed, the implementation sequence of the step (2) and the step (3) is not limited in sequence;
(4) Based on the instantaneous characteristic frequency and the response information set obtained in the steps (2) and (3), obtaining a first-stage instantaneous characteristic frequency and a second-stage instantaneous characteristic frequency according to a given instantaneous characteristic frequency difference threshold;
the calculation separation method is to calculate { f ] based on the results obtained in (2) and (3) 2 In with { f } 1 The instantaneous characteristic frequency difference delta in the frequency spectrum is calculated according to a given instantaneous characteristic frequency difference threshold value delta 0 The separation satisfies that delta is less than or equal to delta 0 Or delta>δ 0 The method of partial instantaneous characteristic frequency of (1);
the { f } 2 In and { f } 1 Instantaneous characteristic frequency difference δ = | f in 2 -f 1 |/f 1 ，f 2 ∈{f 2 }，f 1 ∈{f 1 }；
The instantaneous characteristic frequency difference threshold δ 0 The method comprises the steps of manually assigning instantaneous characteristic frequency difference values according to actual fan characteristics and problem analysis requirements;
the first stage instantaneous characteristic frequency { f } 1 Is { f 2 In with { f } 1 The instantaneous characteristic frequency difference delta in (v) is not greater than delta 0 Instantaneous characteristic frequency of, and f 1 Instantaneous characteristic frequency of i.e.
The second-stage instantaneous characteristic frequency { f } 2 Is { f 2 In with { f } 1 The instantaneous characteristic frequency difference delta in the symbol is larger than delta 0 Instantaneous characteristic frequency of, i.e.
(5) According to the step (4), the obtained first-stage instantaneous characteristic frequency corresponds to the characteristic response with long duration and high frequency of occurrence in the fan, the obtained second-stage instantaneous characteristic frequency corresponds to the characteristic response with short duration and low frequency of occurrence in the fan, and the obtained first-stage instantaneous characteristic frequency, the obtained second-stage instantaneous characteristic frequency and the response information thereof are the fan time-frequency response characteristics after hierarchical refinement.
The invention has the beneficial effects that:
1. the invention provides a method for graded and refined analysis of time-frequency characteristics of fan monitoring data, which can effectively and accurately analyze fan response characteristics without analyzing the operation modal characteristics and operation interference characteristics of a fan in advance.
2. The invention sequentially utilizes a plurality of time-frequency domain analysis methods, combines the advantages of a common frequency domain method and a self-adaptive/multi-resolution time-frequency analysis method, and analyzes and identifies the response characteristics of the fan in a grading and refining way, namely the characteristics of long duration and high occurrence frequency and the characteristics of short duration and low occurrence frequency.
3. The fan characteristics obtained by the detailed analysis of the invention respectively correspond to fan structure response (structure mode, fault characteristics and the like) or excitation characteristics and the like, and can be used for fan state identification evaluation and operation and maintenance decision.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a raw monitoring data time-course image of an embodiment.
Fig. 3 is a time-frequency image of a short-time fourier transform (STFT) result of monitoring data of an embodiment, wherein a frequency resolution Δ f =0.05Hz, and a time resolution Δ t =33.3s.
Fig. 4 is a monitor data wavelet multiresolution analysis (WMRA) result image of an embodiment.
Fig. 5 is a time-frequency image of D1 in the monitoring data short-time fourier transform (STFT) result of the embodiment, in which the frequency resolution Δ f =0.05Hz and the time resolution Δ t =33.3s.
Fig. 6 is a time-frequency image of D2 in the monitoring data short-time fourier transform (STFT) result of the embodiment, in which the frequency resolution Δ f =0.05Hz and the time resolution Δ t =33.3s.
Detailed Description
The present invention will be described in detail by the following specific examples, but those skilled in the art will appreciate that the following examples are not intended to limit the scope of the present invention, and that any modifications and variations based on the present invention are within the scope of the present invention.
As shown in FIG. 1, a method for hierarchical refinement analysis of time-frequency characteristics of fan monitoring data comprises the following steps:
(1) According to the existing fan state monitoring data, obtaining an original monitoring data time-course image shown in fig. 2, calculating monitoring data extreme values (maximum value, minimum value, absolute value maximum and minimum value) and time-course statistical characteristics (mean value, standard deviation and the like), as shown in table 1, calculating and separating monitoring data direct-current components, calculating and determining trend item characteristics of the monitoring data by using a least square method, performing abnormal value verification, and removing abnormal values;
TABLE 1 extreme values of monitoring data and their characteristics
| Monitoring quantity | Minimum value | Maximum value of | Maximum absolute value | Minimum absolute value | Mean value | Standard deviation of |
| RX | -1.132 | 0.458 | 1.132 | 0 | 0.0748 | 0.1029 |
| RY | -1.924 | 0.624 | 1.924 | 0 | -0.0417 | 0.1729 |
| FX | -1.153 | 0.469 | 1.153 | 0 | -0.0005 | 0.0978 |
| FY | -1.742 | 0.919 | 1.742 | 0 | -0.00001 | 0.1573 |
(2) On the basis of the preprocessing result, calculating autocorrelation and cross-correlation coefficients of the monitoring data by using a time domain statistical method, and further solving a frequency spectrum, an auto-power spectrum and a cross-power spectrum of the monitoring data by using a Fourier transform (FFT) method, thereby solving a monitoring data coherence function; furthermore, a short-time fourier transform (STFT) is used to derive a monitoring data time-frequency result image, as shown in fig. 3 (where the frequency resolution Δ f =0.05Hz, and the time resolution Δ t =33.3 s); synthesize the above frequency spectrumExtreme values in the power spectrum, cross-power spectrum, coherent function and time frequency result are obtained, and then more obvious instantaneous characteristic frequency { f ] in the monitored data is obtained 1 And a set of response messages { A } 1 The location of the time-frequency result extremum in fig. 3, f =0.6438Hz and f =0.7063Hz;
(3) Based on the preprocessing result, a wavelet multiresolution time frequency analysis method (WMRA) is utilized to solve different resolution components (eigenmode IM) D of the monitoring data in 8 frequency resolutions i As a result (see FIG. 4), let the time step of the monitored data be Δ t, the mth frequency resolution signal component is extracted and distributed in [ 1/(2) by band-pass filtering m+1 Δt),1/(2 m Δt)]Results of monitoring data signals of frequency bands (M =1, … M); m is the selected amount of frequency resolution and requires a trade-off between the amount of computation and the degree of separation of the frequency components in the signal.
Different resolution components (eigenmode IM) D calculated based on WMRA i As a result, D is calculated from the selected Δ t and Δ f using STFT i Obtaining instantaneous characteristic frequency { f with different frequency resolutions in the monitored data by the windowed time-frequency spectrum 2 And a set of response messages { A } 2 As in fig. 5 and 6 (the instantaneous characteristic frequencies of the part in fig. 5 are as shown, f =2.25hz,2.844Hz,3.294hz and 3.694Hz, respectively);
(4) Based on the instantaneous characteristic frequency and the response information set { f) obtained in (2) and (3) 1 And { A } 1 And { f }and 2 And { A } 2 Calculating instantaneous characteristic frequency difference delta according to a given instantaneous characteristic frequency difference threshold value delta 0 (δ 0 May be taken to be 20%), to derive the first stage instantaneous eigenfrequency { f } 1 ＝{f 2 } δ≤20％ ∪{f 1 And second order instantaneous feature frequency { f } 2 ＝{f 2 } δ>20％ ；
(5) Based on (4), the characteristic response (corresponding to the first-stage instantaneous characteristic frequency { f) }with long duration and high occurrence frequency in the fan is obtained 1 ) And a short duration, low frequency of occurrence characteristic response in the fan (corresponding to a second level instantaneous characteristic frequency { f } 2 ) Namely, the fan time-frequency response characteristics after grading and refining.
The above embodiments are not intended to limit the present invention, and the present invention is not limited to the above embodiments, and all embodiments are within the scope of the present invention as long as the requirements of the present invention are met.
Claims (8)
1. A method for graded and refined analysis of time-frequency characteristics of fan monitoring data is characterized by comprising the following steps:
(1) According to the fan monitoring data, calculating an extreme value and characteristics thereof and time course statistical characteristics, separating direct current components, analyzing trend items, checking and eliminating abnormal values to obtain a preprocessing result;
(2) Processing the preprocessing result by utilizing a time domain statistical method, a Fourier transform method and a short-time Fourier transform method to obtain more remarkable instantaneous characteristic frequency { f ] in the monitoring data 1 And a set of response messages { A } 1 }；
(3) Processing the preprocessing result by using a self-adaptive time-frequency analysis method or a multi-resolution time-frequency analysis method to obtain the instantaneous characteristic frequencies { f ] of different time-frequency resolutions in the monitoring data 2 And a set of response messages { A } 2 }；
(4) Separating the first-stage instantaneous characteristic frequency { f } according to a given instantaneous characteristic frequency difference threshold value based on the instantaneous characteristic frequency and response information sets obtained in the steps (2) and (3) 1 Second order instantaneous characteristic frequency { f } 2 ；
(5) And (4) obtaining the characteristic response with long duration and high frequency of occurrence in the fan corresponding to the first-stage instantaneous characteristic frequency and the characteristic response with short duration and low frequency of occurrence in the fan corresponding to the second-stage instantaneous characteristic frequency.
2. The method for the time-frequency characteristic graded refinement analysis of the fan monitoring data according to claim 1, wherein in (1), the extreme values comprise a maximum value, a minimum value, a maximum absolute value and a minimum absolute value of the monitoring data; the characteristics include the frequency of occurrence of the extremum and the corresponding time of occurrence.
3. The method for the time-frequency characteristic graded refinement analysis of the fan monitoring data according to claim 1, wherein in (1), the time course statistical characteristics comprise a mean value, a mean square value, a variance, a probability density function, a dimensional parameter index and a dimensionless parameter index; the dimensionless parameter indexes comprise a waveform index, a peak index, a pulse index, a margin index, a skewness index and a kurtosis index.
4. The method for the time-frequency characteristic graded refinement analysis of the wind turbine monitoring data according to claim 1, wherein in the step (1), the analysis trend term adopts a least square method or a filtering method.
5. The method for the time-frequency characteristic graded refinement analysis of the wind turbine monitoring data according to claim 1, wherein in the step (1), the abnormal value is checked and removed by using a 3 sigma criterion.
6. The method for the time-frequency characteristic grading and refining analysis of the wind turbine monitoring data according to claim 1, wherein the more significant instantaneous characteristic frequency { f ] in the monitoring data is obtained in the step (2) 1 And a set of response messages { A } 1 The method concretely comprises the following steps:
(2.1) acquiring monitoring data frequency characteristics of frequency domain distribution by using a Fourier transform method;
(2.2) acquiring windowed monitoring data time-frequency distribution characteristics by using short-time Fourier transform;
(2.3) analyzing and extracting the more significant instantaneous characteristic frequency { f) in the monitoring data according to (2.1) and (2.2) 1 And its response information set { A } 1 }。
7. The method for graded and refined analysis of time-frequency characteristics of wind turbine monitoring data according to claim 1, wherein the adaptive time-frequency analysis method or the multiresolution time-frequency analysis method comprises the following steps:
(3.1) calculating different resolution components of intercepted monitoring data;
(3.2) calculating frequency spectrums of different resolution components by utilizing a Fourier transform method; selecting time resolution and frequency resolution according to the problem, and calculating time frequency spectrums after windowing components with different resolutions by using a short-time Fourier transform method;
(3.3) analyzing and extracting instantaneous characteristic frequencies { f ] of all different time resolutions and frequency resolutions appearing in the monitoring data according to (3.1) and (3.2) 2 And a set of response messages { A } 2 }。
8. The method for time-frequency characteristic hierarchical refinement analysis of wind turbine monitoring data according to claim 1, characterized in that a first-level instantaneous characteristic frequency { f } is separated 1 Second level instantaneous characteristic frequency { f } 2 The specific method comprises the following steps:
(4.1) calculating { f ] based on the results obtained in (2) and (3) 2 In and { f } 1 Instantaneous characteristic frequency difference δ, δ = | f in (d) 2 -f 1 |/f 1 ，f 2 ∈{f 2 }，f 1 ∈{f 1 }；
(4.2) according to a given instantaneous characteristic frequency difference threshold δ 0 The separation satisfies that delta is less than or equal to delta 0 Or delta>δ 0 Part of the instantaneous characteristic frequency of (a);
(4.3)δ≤δ 0 instantaneous characteristic frequency of and f 1 The instantaneous characteristic frequency in the frequency is the first-stage instantaneous characteristic frequency { f } 1 ；
(4.4)δ>δ 0 Is the second level instantaneous characteristic frequency { f } 2 。
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