Blade vibration parameter online monitoring method, device, equipment and medium

Abstract

The invention discloses a blade vibration parameter online monitoring method, device and equipment and a medium, and relates to the field of non-contact measurement. The method comprises the following steps: acquiring actual arrival time data of a blade, and preprocessing the actual arrival time data to obtain normal time data; theoretical arrival time data of the blade is calculated according to the normal time data, and the blade rotating speed and the blade vibration displacement are calculated according to the theoretical arrival time data based on the blade tip timing principle; based on the self-adaptive window, trend term removal is carried out on blade vibration displacement according to the accelerated speed of the blade rotating speed, and a target vibration displacement signal is obtained; performing ultra-wavelet transform according to the target vibration displacement signal to obtain three-dimensional signal feature information and a resonance interval of the target vibration displacement signal; and according to the three-dimensional signal characteristic information, nonlinear fitting is carried out in the resonance interval to obtain the blade vibration parameters, so that the blade vibration parameters can be identified on line under the conditions of high inherent frequency and low rotating speed.

Description

Technical Field

[0001] The present invention relates to the field of non-contact measurement, and in particular to a method, device, equipment and medium for online monitoring of blade vibration parameters. Background Art

[0002] Blade vibration parameter monitoring is divided into multi-sensor method and single sensor method. In terms of single-sensor blade vibration parameter identification, the main method is to extract aliasing frequencies for identification through time-frequency analysis. However, due to the existence of the "time-frequency uncertainty principle", the traditional time-frequency analysis method requires high speed to improve the time-frequency accuracy, and the aliasing frequencies extracted at low speeds are not concentrated enough, which makes this type of method unable to be applied to the parameter identification field of blades with low speeds or high natural frequencies. At the same time, the identification operation of this type of method is relatively complicated, and only offline analysis of rotating blade vibration parameters is realized; however, offline analysis cannot timely reflect the blade health status of the turbine machinery. Therefore, it is necessary to design a single-sensor blade vibration parameter online monitoring method and device at low speed to solve the above problems. Summary of the invention

[0003] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, device, equipment and medium for online monitoring of blade vibration parameters, which can be used to online identify blade vibration parameters under high natural frequency and low rotation speed by using ultrawavelet transform.

[0004] The present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for online monitoring of blade vibration parameters, comprising:

[0006] Acquiring actual arrival time data of the blade, and preprocessing the actual arrival time data to obtain normal time data;

[0007] Calculating theoretical arrival time data of the blade according to the normal time data, and calculating blade speed and blade vibration displacement according to the theoretical arrival time data based on the blade tip timing principle;

[0008] Based on an adaptive window, a trend item is removed from the blade vibration displacement to obtain a trend item removal signal, and a target vibration displacement signal is determined according to the acceleration of the blade rotation speed and the trend item removal signal;

[0009] Performing ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal;

[0010] Nonlinear fitting is performed based on the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

[0011] In one embodiment, the preprocessing of the actual arrival time data to obtain normal time data includes:

[0012] Eliminating erroneous narrow pulses from the actual arrival time data to obtain data to be processed;

[0013] The data to be processed is subjected to median absolute value deviation processing to obtain the normal time data.

[0014] In one embodiment, the calculating of the theoretical arrival time data of the blade according to the normal time data, and the calculating of the blade speed and the blade vibration displacement according to the theoretical arrival time data based on the blade tip timing principle, comprises:

[0015] Based on the fact that the theoretical arrival time and the corresponding accumulated arc of the blade satisfy a linear relationship, a least squares straight line fitting solution is performed according to the normal time data to obtain the slope of the fitting straight line and the theoretical arrival time data;

[0016] The blade rotation speed is calculated according to the blade rotation parameter and the slope of the fitting straight line, and the blade vibration displacement is calculated according to the blade rotation speed.

[0017] In one embodiment, the method of removing the trend item of the blade vibration displacement based on the adaptive window to obtain a trend item removal signal, and determining the target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal includes:

[0018] intercepting the blade vibration displacement according to a preset fixed data window to obtain an initial vibration displacement signal;

[0019] Performing trend term curve fitting on the initial vibration displacement signal through the filter of the adaptive window to obtain a fitting trend term;

[0020] Performing trend item removal on the initial vibration displacement signal according to the fitting trend item to obtain a trend item removed vibration signal;

[0021] The target vibration displacement signal is determined from the detrended vibration signal according to the acceleration of the blade rotation speed and the Pearson correlation coefficient.

[0022] In one embodiment, determining the target vibration displacement signal from the detrended vibration signal according to the acceleration of the blade rotation speed and the Pearson correlation coefficient includes:

[0023] Calculating the data correlation between the initial vibration displacement signal and the vibration signal with the trend removed to obtain a Pearson correlation coefficient;

[0024] If the Pearson correlation coefficient is greater than a preset coefficient threshold, the trend-removed vibration signal is used as the target vibration displacement signal;

[0025] If the Pearson correlation coefficient is less than the preset coefficient threshold, the initial vibration displacement signal is divided into a plurality of sub-segments, the acceleration of each sub-segment is calculated according to the blade rotation speed, and the acceleration variance of each acceleration is calculated;

[0026] If the acceleration variance is greater than a preset variance threshold, the preset fixed data window is reduced based on a preset unit to obtain a reduced data window, and the blade vibration displacement is intercepted based on the reduced data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold;

[0027] If the acceleration variance is less than a preset variance threshold, the preset fixed data window is expanded based on the preset unit to obtain an expanded data window, and the blade vibration displacement is intercepted based on the expanded data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold.

[0028] In one embodiment, performing ultrawavelet transform on the target vibration displacement signal to obtain three-dimensional signal feature information and resonance interval of the target vibration displacement signal includes:

[0029] Based on a preset fixed rotation frequency step length and a preset data length, extracting a plurality of signal data and corresponding rotation frequency data from the target vibration displacement signal;

[0030] For each frequency conversion data, based on the Nyquist theorem, a confusion frequency range is determined according to the frequency conversion data; a center frequency is determined according to the confusion frequency range, and a discrete wavelet of multiple cycles is generated according to the center frequency;

[0031] Convolving each discrete wavelet with the signal data corresponding to the frequency conversion data to obtain a plurality of amplitude-frequency responses, and geometrically averaging each of the amplitude-frequency responses to obtain a final ultrawavelet transform response corresponding to the frequency conversion data;

[0032] The final responses of the ultra-wavelet transform corresponding to each of the frequency conversion data are spliced ​​to obtain the three-dimensional signal characteristic information; and the amplitude is screened according to a preset amplitude threshold and the three-dimensional signal characteristic information to obtain the resonance interval.

[0033] In one embodiment, performing nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters includes:

[0034] determining target signal characteristic information from the three-dimensional signal characteristic information according to the resonance interval;

[0035] Based on a single parameter method and a dimensional relationship corresponding to the three-dimensional signal characteristic information, nonlinear fitting is performed on the target signal characteristic information to obtain the blade vibration parameters.

[0036] In a second aspect, the present invention provides an online monitoring device for blade vibration parameters, comprising:

[0037] A processing module, used for acquiring actual arrival time data of the blade, and preprocessing the actual arrival time data to obtain normal time data;

[0038] A calculation module, used to calculate the theoretical arrival time data of the blade according to the normal time data, and based on the blade tip timing principle, calculate the blade speed and blade vibration displacement according to the theoretical arrival time data;

[0039] A removal module, configured to remove a trend item from the blade vibration displacement based on an adaptive window to obtain a trend item removal signal, and determine a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal;

[0040] A transformation module, used for performing ultra-wavelet transformation according to the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal;

[0041] The fitting module is used to perform nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

[0042] In a third aspect, the present invention proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for online monitoring of blade vibration parameters as described in the first aspect is implemented.

[0043] In a fourth aspect, the present application proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for online monitoring of blade vibration parameters as described in the first aspect.

[0044] The blade vibration parameter online monitoring method, device, equipment and medium disclosed in the present invention obtain the actual arrival time data of the blade, and pre-process the actual arrival time data to obtain normal time data; calculate the theoretical arrival time data of the blade according to the normal time data, and based on the blade tip timing principle, calculate the blade rotation speed and the blade vibration displacement according to the theoretical arrival time data; based on an adaptive window, remove the trend item of the blade vibration displacement to obtain a trend item removal signal, and determine the target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal; perform ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal; perform nonlinear fitting on the three-dimensional signal characteristic information and the resonance interval to obtain the blade vibration parameters. In this way, by preprocessing the theoretical arrival time data collected by a single sensor, more complete and accurate normal time data can be obtained; the blade speed and blade vibration displacement are calculated according to the normal time data, and the length of the fitting window is adaptively adjusted based on the blade speed, thereby effectively removing the interference of the trend term and retaining the effective information of the blade vibration displacement to obtain the target vibration displacement signal; then, an ultrawavelet transform is performed on the target vibration displacement signal to achieve online identification of blade vibration parameters even in the case of high natural frequency and low speed, thereby accurately reflecting the health status of the blade. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0046] Figure 1 A schematic diagram of a process of the blade vibration parameter online monitoring method proposed in this embodiment is shown;

[0047] Figure 2 A schematic diagram of the structure of the detection device proposed in this embodiment is shown;

[0048] Figure 3 A schematic diagram showing the normal interval and abnormal interval proposed in this embodiment is shown;

[0049] Figure 4 A schematic diagram of overlapping windows proposed in this embodiment is shown;

[0050] Figure 5 Another schematic diagram of the process of the online monitoring method of blade vibration parameters proposed in this embodiment is shown;

[0051] Figure 6 A schematic diagram showing the three-dimensional signal feature information drawn by the ASLT method proposed in this embodiment;

[0052] Figure 7 A schematic diagram of the resonance interval drawn by the ASLT method proposed in this embodiment is shown;

[0053] Figure 8 A schematic diagram showing a two-dimensional graph of the relationship between sampling frequency and confusion frequency drawn by the existing single-sensor method of this embodiment is shown;

[0054] Fig. 9 A schematic structural diagram of the blade vibration parameter online monitoring device proposed in this embodiment is shown.

[0055] Description of the accompanying drawings:

[0056] 900 - blade vibration parameter online monitoring device; 901 - processing module; 902 - calculation module; 903 - removal module; 904 - transformation module; 905 - fitting module. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0058] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0059] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0060] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0061] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present invention belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0062] Example 1

[0063] The disclosed embodiment provides a method for online monitoring of blade vibration parameters, which is used to identify blade vibration parameters online even under conditions of high natural frequency and low rotation speed by using ultra-wavelet transform.

[0064] See also Figure 1 , a blade vibration parameter online monitoring method includes steps S101 to S105, and each step is described in detail below.

[0065] Step S101, obtaining actual arrival time data of the blade, and preprocessing the actual arrival time data to obtain normal time data.

[0066] In this embodiment, the Figure 2 The detection device shown collects the actual arrival time data of the blades. Due to noise, environmental pollution and other reasons, abnormal conditions such as redundant pulse signals and missing pulse signals may occur. Therefore, it is necessary to preprocess the actual arrival time data to obtain normal time data. In this way, by preprocessing the original actual arrival time data, the vibration curves of each blade can be clearly distinguished, the speed curve is smoother, and the noise interference is significantly reduced, providing a high-quality signal basis for subsequent online blade parameter identification, ensuring the accuracy of subsequent real-time analysis, data integrity and monitoring robustness.

[0067] In a specific embodiment, step S101 includes: removing erroneous narrow pulses from the actual arrival time data to obtain data to be processed; and performing median absolute value deviation processing on the data to be processed to obtain the normal time data.

[0068] In this embodiment, two arrays are used to store the rising edge time R={R1, R2, ..., R n} and falling edge time F={F1,F2,…,F n}, the difference between the two is the time interval between the rising edge and the falling edge D = {D1, D2, ..., D n}, where D i =|R i -F i|; Based on the principle that the blades are evenly distributed on the blade disk and the rotation speed in one circle can be considered constant, a slightly excess number of blades n can be selected as a processing unit, F and R can be divided into multiple windows, and the average value of the difference between each window interval is calculated as the threshold Taking 0.7θ as the final reference threshold, the interval D i The rising and falling edges greater than 0.7θ are added to the new arrays R and F, that is, R′={R i ∣D i ≥0.7θ}, F′={F i ∣D i ≥0.7θ}, so that arrays R′ and F′ constitute the data to be processed, that is, the data after eliminating the erroneous narrow pulses.

[0069] For example, since the number of blades is 32, data slightly larger than the number of blades, such as 40 pulses, is selected as the processing unit. Taking the rising and falling edges of 40 pulses as the processing unit as an example, the time interval between the same rising and falling edges is solved, the average value of the 40 intervals is calculated, and 0.7 times the interval mean is used as the threshold value. i The rising edge and falling edge greater than 0.7θ are taken as the data to be processed, and the data after eliminating the erroneous narrow pulses can be obtained.

[0070] Furthermore, the median absolute deviation is processed on the data to be processed after the erroneous narrow pulses are removed to obtain the normal time data to fill in the missing data. The location of data loss can be located based on the principle that the intervals between adjacent rising edges or adjacent falling edges in a small range will not differ too much. Therefore, in order to avoid the extreme difference caused by data loss, the median absolute deviation (MAD) is needed to select the threshold, and the threshold is used to locate the location where the rising edge or falling edge data is missing, and the missing data is filled by linear interpolation to obtain the normal time data, that is, the normal actual arrival time data, thereby solving the data loss situation.

[0071] Taking the rising edge data as an example, first, set the processing window length to 300 and solve 300 rising edges {R1, R2, …, R 300} time interval I={I1,I2,…,I 300}, where I j =R j+1 -R j (1≤h<300), calculate the median absolute deviation of the time interval: MAD=median(|I1-median(I)|,|I2-median(I)|,…,|I 299 -median(I)|), calculate the window threshold: θ d= median(I) + 1.4826 × MAD. Based on this window threshold as the judgment, the normal interval is I r = {I r | I r < θ d}, and the abnormal interval is I g = {I k | I k > θ d}, to locate the position of the missing data. Please refer to Figure 3 , the window threshold set according to the median absolute deviation can effectively locate the position of the missing data.

[0072] Furthermore, since there is a multiple relationship between the abnormal interval and the normal interval, the abnormal data R′ k+1 , R′ k the number of missing intervals between Take the mean value of I r as the interpolation interval where L is the number of normal data in the window, so that the missing data between the rising edge R′ k+1 and R′ k can be expressed as R′ add = R′ k + m × ΔR, where m is the number of missing data points, satisfying 1 ≤ m < N; perform the same operation on the falling edge data to complete the data cleaning of the falling edge.

[0073] It should be noted that considering the possible data loss at the connection of adjacent windows, the overlapping window method is adopted to improve the data coverage rate. As Figure 4 shown, the overlapping part of window 1 and window 2 can provide supplementary information. The overlapping part in the figure is 0.1 times the window length, that is, 30 rising edge data. This processing method can reduce the impact of information loss at the window connection on the result and make the processing more continuous.

[0074] Step S102, calculate the theoretical arrival time data of the blade according to the normal time data, and based on the tip timing principle, calculate the blade rotation speed and the blade vibration displacement according to the theoretical arrival time data.

[0075] In this embodiment, based on the normal time data, it is calculated that in the ideal state, the predicted time when the same specific point (such as the tip) on the blade should reach the reference position (such as the sensor) in the next rotation cycles forms the theoretical arrival time data. The tip timing principle is a method of monitoring the blade rotation speed and vibration state by measuring the time when the blade (usually the tip) passes through a fixed position (such as the sensor). Based on the tip timing principle and using the theoretical arrival time data, the blade rotation speed and the blade vibration displacement can be effectively calculated.

[0076] In a specific embodiment, step S102 includes: based on the linear relationship between the theoretical arrival time and the corresponding accumulated radian of the blade, performing a least squares straight line fitting solution according to the normal time data to obtain the slope of the fitting straight line and the theoretical arrival time data; calculating the blade rotation speed according to the blade rotation parameter and the slope of the fitting straight line, and calculating the blade vibration displacement according to the blade rotation speed.

[0077] It should be noted that the blade rotation parameters include the rotating blade radius and the number of blades.

[0078] In this embodiment, the blade speed in one circle is considered to be constant, and the theoretical reaching time and the corresponding accumulated arc of the blade satisfy a linear relationship: t t(j,b) =k fit(j) ×Ψ (j,b) +b fit(j) Assuming that the number of blades in a circle is n, the actual arrival time data of n blades in a circle {t (j,1) ,t (j,2) ,t (j,3) ,...,t (j,n)} and the corresponding accumulated radians {Ψ (j,1) ,Ψ (j,2) ,Ψ (j,3) ,...,Ψ (j,n)}Execute the least squares straight line fitting to solve t t(j,b) , k fit(j) , b fit(j) , where t t(j,b) represents the theoretical arrival time of blade b in the jth circle, Ψ (j,b) represents the accumulated arc of blade b in the jth circle, k fit(j) represents the slope of the fitted line in the jth circle, b fit(j) is the intercept of the j-th circle fitting straight line; the j-th circle speed can be expressed as where t t(j,n+1) =k fit(j) 2π+b fit(j) ,t t(j,1) =k fit(j) ×0+b fit(j) , r is the radius of the rotating blade, T j is the rotation period of the jth circle; calculate the vibration displacement x of the blade (j,b) =(t (j,b) -t t(j,b) )·v fit(j) , where x (j,b) Represents the vibration displacement of blade number b in the jth circle.

[0079] It should be noted that the blades are evenly distributed on the blade disk. For example, if there are 3 blades, the curvature between each blade is Add 2π for each rotation, so the arrival time of a blade corresponds to one radian.

[0080] Step S103 , based on an adaptive window, removing the trend item of the blade vibration displacement to obtain a trend item removal signal, and determining a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal.

[0081] In this embodiment, according to the blade speed, specifically, the acceleration of the blade speed can be used as a reference, and the Savitzky-Golay filter of the adaptive window can be used to fit the trend term curve of the vibration displacement, and the trend term in the original vibration displacement can be removed to obtain the target vibration displacement signal. The existence of the trend term will have a great impact on the result of numerical integration, resulting in distortion or even distortion of the integral curve, so it is necessary to eliminate the trend term first to ensure the accuracy and reliability of signal processing.

[0082] See also Figure 5 In a specific embodiment, step S103 includes steps S1031 to S1034, and each step is described in detail below.

[0083] Step S1031 : intercepting the blade vibration displacement according to a preset fixed data window to obtain an initial vibration displacement signal.

[0084] In this embodiment, a preset fixed data window W is used. n The blade vibration displacement is intercepted to obtain the initial vibration displacement signal. n Indicates the length of the data window at the nth interception.

[0085] Step S1032: performing trend term curve fitting on the initial vibration displacement signal through the filter of the adaptive window to obtain a fitting trend term.

[0086] In this embodiment, a second-order Savitzky-Golay filter with an adaptive window is used to filter the initial vibration displacement signal x orignal (t) Perform trend term curve fitting, and the fitted trend term is recorded as x trend (t).

[0087] Step S1033: removing the trend term of the initial vibration displacement signal according to the fitting trend term to obtain a vibration signal with the trend term removed.

[0088] In this embodiment, after removing the fitting trend term in the initial vibration displacement signal, the vibration signal x detrend (t) = xorignal (t)-x trend (t).

[0089] Step S1034: determining the target vibration displacement signal from the trend-removed vibration signal according to the acceleration of the blade rotation speed and the Pearson correlation coefficient.

[0090] In this embodiment, it is necessary to adjust the adaptive window according to the acceleration of the blade rotation speed, and determine the target vibration displacement signal retaining sufficient original information from the vibration signal after removing the trend term in combination with the Pearson correlation coefficient.

[0091] In a specific embodiment, step S1034 includes: calculating the data correlation between the initial vibration displacement signal and the detrended vibration signal to obtain a Pearson correlation coefficient; if the Pearson correlation coefficient is greater than a preset coefficient threshold, using the detrended vibration signal as the target vibration displacement signal; if the Pearson correlation coefficient is less than the preset coefficient threshold, dividing the initial vibration displacement signal into a plurality of sub-segments, calculating the acceleration of each of the sub-segments according to the blade rotation speed, and calculating the acceleration variance of each of the accelerations; if the acceleration variance is greater than a preset variance threshold, reducing the preset fixed data window based on a preset unit to obtain a reduced data window, and performing a step of intercepting the blade vibration displacement based on the reduced data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold; if the acceleration variance is less than the preset variance threshold, expanding the preset fixed data window based on the preset unit to obtain an expanded data window, and performing a step of intercepting the blade vibration displacement based on the expanded data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold.

[0092] In this embodiment, it is necessary to calculate the data correlation of the signal before and after the fitting trend term is removed to obtain the Pearson correlation coefficient r, and the calculation formula is: in and are the means of the initial vibration displacement signal and the detrended vibration signal, respectively. If the Pearson correlation coefficient r>0.75, the detrended vibration signal retains enough original information, and it can be considered that the fitting trend term meets the requirements. The corresponding detrended vibration signal can also be used as the target vibration displacement signal.

[0093] If the Pearson correlation coefficient r<0.75, the fixed data window W will be preset. n The initial vibration displacement signal in is divided into m sub-segments S j For each sub-segment S j , calculate the acceleration of the starting and ending points of the segment where fj and f j+m Respectively represent sub-segment S j The blade speed at the starting and ending points of the segment, j is the index of the segment, and the acceleration a of all segments is j Calculate the acceleration variance in represents the acceleration a of all sub-segments j If the acceleration variance Greater than the preset variance threshold It is considered that the speed changes significantly in this interval, and a shorter data window is needed to fit and remove the trend item to retain more local information. n The preset fixed data window in step S1031 is reduced by a unit to obtain a reduced data window W n+1 =0.9×W n , and based on the reduced data window, go to step S1031 and start to repeat the above steps until the Pearson correlation coefficient is greater than the preset coefficient threshold; if the acceleration variance Less than the preset variance threshold It is considered that the speed change in this range is not obvious, and more data needs to be obtained for fitting. At this time, 0.1W n The preset fixed data window in step S1031 is enlarged by units to obtain an enlarged data window W n+1 =1.1×W n , based on the expansion of the data window, go to step S1031 and start to repeat the above steps until the Pearson correlation coefficient is greater than the preset coefficient threshold.

[0094] It should be noted that the length of the fitting window is adaptively adjusted based on the acceleration variance corresponding to the speed, and the Pearson correlation coefficient is used as the evaluation index of the fitting effect. Under the conditions of sudden speed changes and complex working conditions, this method can not only flexibly capture the changing trend of vibration displacement, but also retain the effective information of vibration displacement while removing the interference of trend terms, laying a solid foundation for subsequent accurate analysis.

[0095] Step S104: performing ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal feature information and resonance interval of the target vibration displacement signal.

[0096] In this embodiment, the target vibration displacement data is subjected to ultrawavelet transform to extract its amplitude-frequency characteristics, and the resonance interval is identified based on the amplitude-frequency characteristics and the three-dimensional signal characteristic information is determined, thereby achieving accurate identification of vibration information even under high natural frequency and low rotation speed conditions.

[0097] In a specific embodiment, step S104 includes: based on a preset fixed frequency step size and a preset data length, intercepting multiple signal data and their corresponding frequency data from the target vibration displacement signal; for each frequency data, based on the Nyquist theorem, determining the confusion frequency range according to the frequency data; and determining the center frequency according to the confusion frequency range, and generating multiple periods of discrete wavelets according to the center frequency; convolving each of the discrete wavelets with the signal data corresponding to the frequency data to obtain multiple amplitude-frequency responses, and geometrically averaging each of the amplitude-frequency responses to obtain a final response of the ultra-wavelet transform; splicing each of the ultra-wavelet transform final responses to obtain the three-dimensional signal feature information; and performing amplitude screening according to a preset amplitude threshold and the three-dimensional signal feature information to obtain the resonance interval.

[0098] In this embodiment, according to the preset fixed frequency step length ΔΩ and the preset data length W L (l) The signal data x[n] = {x[0], x[1], x[2], ..., x[L-1]} and the corresponding frequency data Ω[n] = {Ω[0], Ω[1], Ω[2], ..., Ω[L-1]} are intercepted from the target vibration displacement signal, that is, the frequency change ΔΩ is processed once. Where l represents the window index, L represents the data length in the window, and n is the time index with a value of 0, 1, 2, ..., L-1.

[0099] Furthermore, according to the Nyquist theorem, the window W L The maximum aliasing frequency that can be extracted by spectrum analysis in (l) is That is, the confusion frequency range is 0hz to You can preset a set of center frequencies For each center frequency F[n], generate discrete Morlet wavelets with multiple period numbers: Where σ is the time expansion factor, c j is the preset wavelet period scaling factor, that is, a series of Morlet wavelets with different period numbers are generated at the center frequency F[n], and wavelets with different period numbers are Convolve with x[n] one by one to get the corresponding amplitude-frequency response. For the Morlet wavelet with a center frequency of F[n] and j cycles, the convolution result is: Take the geometric average of all convolution results with the center frequency F[n] to generate the final response of the ultra-wavelet transform at this frequency: Where o is the order of ultra-wavelet transform, that is, the number of wavelets with different period numbers, is the amplitude response. Through the above operation, the window W is extracted LThe aliasing frequency f of the target vibration displacement signal in (l) at different rotation frequencies Ω[n] a And the amplitude

[0100] Exemplarily, a fixed frequency step size ΔΩ=0.5Hz is preset to collect vibration displacement data, that is, the frequency change of 0.5Hz is processed once; ultrawavelet is performed on the target vibration displacement signals in the frequency ranges [95Hz, 95.5Hz), [95.5Hz, 96Hz), [96Hz, 96.5Hz)...[199.5Hz, 200Hz) respectively. Specifically, taking the frequency conversion interval [95Hz, 95.5Hz) data as an example, the target vibration displacement data of this interval is intercepted; first, according to the Nyquist theorem, the maximum confusion frequency that can be identified in this interval is 47.75Hz, and the center frequency F = {5Hz, 10Hz, 15Hz, 20Hz, 25Hz, 30Hz, 35Hz, 40Hz, 45Hz} is set with a step size of 5Hz, the minimum number of cycles is set to 3, the maximum number of cycles is set to 10, and the order is set to 8. These parameters can be dynamically adjusted according to actual conditions, and Morlet wavelets with different numbers of cycles are generated according to the center frequency; for example, when the center frequency is 5Hz, a Morlet wavelet with a period c equal to 3 to 10 is generated {ψ 5Hz,c＝3 , 5Hz,c＝4 , 5Hz,c＝5 ... 5Hz,c＝10}; Then let these wavelets convolve with the vibration displacement signal in the frequency range [95Hz, 95.5Hz) to obtain the amplitude-frequency response R corresponding to each cycle number 5Hz,c＝i , the final amplitude-frequency response at the center frequency is obtained by geometrically averaging the amplitude-frequency response at different cycle numbers, that is, the final response of the ultra-wavelet transform By performing similar processing on the remaining center frequencies, a complete time-frequency analysis result in the interval [95 Hz, 95.5 Hz) can be obtained, and the same operation is performed on the remaining frequency conversion intervals.

[0101] Furthermore, when the aliasing frequency is zero, that is, f a = 0, Reaching the local maximum value, according to the blade resonance, the blade natural frequency is equal to the integer multiple of the rotation frequency and the blade vibration amplitude reaches the local maximum value, it can be determined that the blade resonance occurs at this time. Figure 6 , the aliased frequency f at resonance a Concentrated near "0 frequency" and the amplitude reaches a maximum value Therefore, near the "0 frequency" The amplitude is screened for the threshold, and the interval that meets this condition is taken as the resonance interval, that is, for the resonance local area, The interval is considered as the resonance interval, such as Figure 7The part shown in the box. is the local maximum found by traversal search, f th To determine the confusion frequency threshold for resonance, it can be set to 3Hz to 5Hz.

[0102] Furthermore, multiple windows {W L (1),W L (2),W L (3),...,W L The final response of the ultra-wavelet transform of (l)} is spliced ​​in the time domain, and then according to the one-to-one correspondence between time and transfer frequency, the “transfer frequency Ω-confusion frequency f a -Amplitude|R SL |”Three-dimensional signal feature information.

[0103] In this way, by using the ultra-wavelet transform method, that is, dividing multiple time-frequency windows, setting different center frequencies in each window according to the rotation frequency, and then creating multiple wavelet groups with different number of cycles and convolving with the signal according to the center frequency, the highly concentrated confusion frequency can be obtained by geometrically averaging all the convolution results, so that the vibration information can be accurately identified even at low rotation speeds. In addition, because this method has good frequency focusing capabilities, the positioning of resonance events and the identification of frequency doublings become simpler and more accurate.

[0104] Step S105 , performing nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

[0105] In this embodiment, the three-dimensional signal characteristic information in the resonance interval is fitted to obtain blade vibration parameters such as resonance frequency, vibration amplitude, phase, quality factor and frequency harmonics, so that the blade vibration parameters can be identified online even under high natural frequency and low rotation speed, and the health status of the blade can be reflected in real time.

[0106] In a specific embodiment, step S105 includes: determining target signal characteristic information from the three-dimensional signal characteristic information according to the resonance interval; performing nonlinear fitting on the target signal characteristic information based on a single parameter method and a dimensional relationship corresponding to the three-dimensional signal characteristic information to obtain the blade vibration parameters.

[0107] In this embodiment, when the blade resonates, the blade vibration displacement x(Ω) and the rotation frequency Ω satisfy the relationship: Where A0 is the displacement caused by the exciting force, n EO is the vibration frequency, f n is the resonant frequency conversion, ω n is the first-order resonant frequency, Q is the quality factor, is the blade vibration phase, x DCFor the three-dimensional signal characteristic information data in the resonance range, that is, the target signal characteristic information, the blade vibration parameters such as the resonance frequency, vibration amplitude, vibration phase, and quality factor can be obtained by using Levenberg-Marquardt nonlinear fitting based on the relationship between the blade vibration displacement x(Ω) and the rotation frequency Ω.

[0108] Furthermore, the vibration signal sampling rate f under a single sensor is s Equal to the rotation frequency Ω, according to the relationship between vibration confusion frequency and rotation frequency: f a =min k |f o -kΩ|k∈Z + , the "confusion frequency f" in the target signal feature information a The Levenberg-Marquardt nonlinear fitting is used to solve the k value based on the resonance interval data of the two dimensions of - rotation frequency Ω, and then the k value is solved according to the resonance time f a = 0, solve for the blade natural frequency f o = kΩ and frequency multiplication n EO = k. Among them, min k It means adjusting the positive integer k so that the formula is minimized.

[0109] The above data [95Hz, 200Hz] are subjected to parameter identification using the “single-sensor AWSTFT method” proposed in the document “Rotating blade frequency identification by single-probe blade tiptiming” to obtain the corresponding resonant rotational frequency, frequency doubling and natural frequency of the blade, and compared with the resonant rotational frequency, frequency doubling and natural frequency of the blade obtained by the method of this embodiment. For the comparative experimental data results, please see Table 1. It can be seen that the blade natural frequencies calculated by the two methods are not much different, which verifies the effectiveness of the ASLT method of this embodiment; and this embodiment can calculate the frequency doubling and natural frequency under low rotational frequency data, which once again verifies the effectiveness of the method of this embodiment at low speeds. At the same time, combined with Figure 7 The two-dimensional diagram of the relationship between the transfer frequency and the confusion frequency drawn by the method of this embodiment is Figure 8 From the two-dimensional graph of the sampling frequency-confusion frequency relationship drawn by the AWSTFT method, it can be seen that the method used in this embodiment extracts a higher degree of confusion frequency concentration, and even under the conditions of low speed and low sampling, it can have a good resolution, which makes up for the deficiency of the prior art that it cannot be recognized at low speed. It should be emphasized that the recognition process of the method proposed in this embodiment is carried out online without too much manual intervention.

[0110] Table 1:

[0111]

[0112] The blade vibration parameter online monitoring method proposed in this embodiment obtains the actual arrival time data of the blade, and pre-processes the actual arrival time data to obtain normal time data; calculates the theoretical arrival time data of the blade according to the normal time data, and based on the blade tip timing principle, calculates the blade rotation speed and the blade vibration displacement according to the theoretical arrival time data; based on an adaptive window, removes the trend item of the blade vibration displacement to obtain a trend item removal signal, and determines a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal; performs ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal; performs nonlinear fitting on the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters. In this way, by preprocessing the theoretical arrival time data collected by a single sensor, more complete and accurate normal time data can be obtained; the blade speed and blade vibration displacement are calculated according to the normal time data, and the length of the fitting window is adaptively adjusted based on the blade speed, thereby effectively removing the interference of the trend term and retaining the effective information of the blade vibration displacement to obtain the target vibration displacement signal; then, an ultrawavelet transform is performed on the target vibration displacement signal to achieve online identification of blade vibration parameters even in the case of high natural frequency and low speed, thereby accurately reflecting the health status of the blade.

[0113] Example 2

[0114] In addition, the present disclosure provides a blade vibration parameter online monitoring device 900, see Fig. 9 , the device comprises:

[0115] The processing module 901 is used to obtain the actual arrival time data of the blade, and pre-process the actual arrival time data to obtain normal time data;

[0116] A calculation module 902 is used to calculate the theoretical arrival time data of the blade according to the normal time data, and calculate the blade speed and blade vibration displacement according to the theoretical arrival time data based on the blade tip timing principle;

[0117] A removal module 903 is used to remove the trend item of the blade vibration displacement based on an adaptive window to obtain a trend item removal signal, and determine a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal;

[0118] A transformation module 904 is used to perform ultra wavelet transformation on the target vibration displacement signal to obtain three-dimensional signal feature information and resonance interval of the target vibration displacement signal;

[0119] The fitting module 905 is used to perform nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

[0120] Optionally, the processing module 901 is further used to eliminate erroneous narrow pulses from the actual arrival time data to obtain data to be processed; and perform median absolute value deviation processing on the data to be processed to obtain the normal time data.

[0121] Optionally, the calculation module 902 is also used to perform a least squares straight line fitting solution based on the normal time data based on the linear relationship between the theoretical arrival time and the corresponding accumulated radian of the blade, to obtain the slope of the fitting straight line and the theoretical arrival time data; calculate the blade rotation speed according to the blade rotation parameter and the slope of the fitting straight line, and calculate the blade vibration displacement according to the blade rotation speed.

[0122] Optionally, the removal module 903 is further used to intercept the blade vibration displacement according to a preset fixed data window to obtain an initial vibration displacement signal; perform trend term curve fitting on the initial vibration displacement signal through the filter of the adaptive window to obtain a fitted trend term; perform trend term removal on the initial vibration displacement signal according to the fitted trend term to obtain a trend term removal vibration signal; and determine the target vibration displacement signal from the trend term removal vibration signal according to the acceleration of the blade rotation speed and the Pearson correlation coefficient.

[0123] Optionally, the removal module 903 is further used to calculate the data correlation between the initial vibration displacement signal and the vibration signal with the trend item removed to obtain a Pearson correlation coefficient; if the Pearson correlation coefficient is greater than a preset coefficient threshold, the vibration signal with the trend item removed is used as the target vibration displacement signal; if the Pearson correlation coefficient is less than the preset coefficient threshold, the initial vibration displacement signal is divided into a plurality of sub-segments, the acceleration of each sub-segment is calculated according to the blade rotation speed, and the acceleration variance of each acceleration is calculated; if the acceleration variance is greater than a preset variance threshold, the preset fixed data window is reduced based on a preset unit to obtain a reduced data window, and the step of intercepting the blade vibration displacement based on the reduced data window to obtain an initial vibration displacement signal is performed until the Pearson correlation coefficient is greater than the preset coefficient threshold; if the acceleration variance is less than the preset variance threshold, the preset fixed data window is expanded based on the preset unit to obtain an expanded data window, and the step of intercepting the blade vibration displacement based on the expanded data window to obtain an initial vibration displacement signal is performed until the Pearson correlation coefficient is greater than the preset coefficient threshold.

[0124] Optionally, the transformation module 904 is also used to intercept multiple signal data and their corresponding frequency rotation data from the target vibration displacement signal based on a preset fixed frequency rotation step and a preset data length; for each frequency rotation data, based on the Nyquist theorem, determine the confusion frequency according to the frequency rotation data; and determine the center frequency according to the confusion frequency, and generate multiple periods of discrete wavelets according to the center frequency; convolve each of the discrete wavelets with the signal data corresponding to the frequency rotation data to obtain multiple amplitude-frequency responses, and perform geometric averaging on each of the amplitude-frequency responses to obtain a final response of the ultra-wavelet transform; splice each of the ultra-wavelet transform final responses to obtain the three-dimensional signal feature information; and perform amplitude screening according to a preset amplitude threshold and the three-dimensional signal feature information to obtain the resonance interval.

[0125] Optionally, the fitting module 905 is also used to determine the target signal characteristic information from the three-dimensional signal characteristic information according to the resonance interval; perform nonlinear fitting on the target signal characteristic information based on the single parameter method and the dimensional relationship corresponding to the three-dimensional signal characteristic information to obtain the blade vibration parameters.

[0126] The device provided in the embodiment of the present disclosure can execute the steps of the blade vibration parameter online monitoring method provided in Example 1, which will not be described again to avoid repetition.

[0127] The blade vibration parameter online monitoring device proposed in this embodiment obtains the actual arrival time data of the blade, and pre-processes the actual arrival time data to obtain normal time data; calculates the theoretical arrival time data of the blade according to the normal time data, and based on the blade tip timing principle, calculates the blade rotation speed and the blade vibration displacement according to the theoretical arrival time data; based on an adaptive window, removes the trend item of the blade vibration displacement to obtain a trend item removal signal, and determines a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal; performs ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal; performs nonlinear fitting on the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters. In this way, by preprocessing the theoretical arrival time data collected by a single sensor, more complete and accurate normal time data can be obtained; the blade speed and blade vibration displacement are calculated according to the normal time data, and the length of the fitting window is adaptively adjusted based on the blade speed, thereby effectively removing the interference of the trend term and retaining the effective information of the blade vibration displacement to obtain the target vibration displacement signal; then, an ultrawavelet transform is performed on the target vibration displacement signal to achieve online identification of blade vibration parameters even in the case of high natural frequency and low speed, thereby accurately reflecting the health status of the blade.

[0128] Example 3

[0129] In addition, an embodiment of the present disclosure provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for online monitoring of blade vibration parameters described in Example 1 is implemented.

[0130] The device provided in the embodiment of the present disclosure can execute the steps of the blade vibration parameter online monitoring method provided in Example 1, which will not be described again to avoid repetition.

[0131] Example 4

[0132] The embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method for online monitoring of blade vibration parameters described in Embodiment 1 is implemented.

[0133] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0134] The computer-readable storage medium provided in this embodiment can implement the blade vibration parameter online monitoring method provided in Example 1, and will not be described again here to avoid repetition.

[0135] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.

[0136] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0137] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for online monitoring of blade vibration parameters, characterized in that: include: Acquiring actual arrival time data of the blade, and preprocessing the actual arrival time data to obtain normal time data; Calculating theoretical arrival time data of the blade according to the normal time data, and calculating blade speed and blade vibration displacement according to the theoretical arrival time data based on the blade tip timing principle; Based on an adaptive window, a trend item is removed from the blade vibration displacement to obtain a trend item removal signal, and a target vibration displacement signal is determined according to the acceleration of the blade rotation speed and the trend item removal signal; Performing ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal; Nonlinear fitting is performed based on the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

2. The blade vibration parameter online monitoring method according to claim 1 is characterized in that: The preprocessing of the actual arrival time data to obtain normal time data includes: Eliminating erroneous narrow pulses from the actual arrival time data to obtain data to be processed; The data to be processed is subjected to median absolute value deviation processing to obtain the normal time data.

3. The blade vibration parameter online monitoring method according to claim 1 is characterized in that: The calculating of the theoretical arrival time data of the blade according to the normal time data, and the calculating of the blade speed and the blade vibration displacement according to the theoretical arrival time data based on the blade tip timing principle, comprises: Based on the fact that the theoretical arrival time and the corresponding accumulated arc of the blade satisfy a linear relationship, a least squares straight line fitting solution is performed according to the normal time data to obtain the slope of the fitting straight line and the theoretical arrival time data; The blade rotation speed is calculated according to the blade rotation parameter and the slope of the fitting straight line, and the blade vibration displacement is calculated according to the blade rotation speed.

4. The blade vibration parameter online monitoring method according to claim 1 is characterized in that: The method of removing the trend item of the blade vibration displacement based on the adaptive window to obtain a trend item removal signal, and determining a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal, includes: intercepting the blade vibration displacement according to a preset fixed data window to obtain an initial vibration displacement signal; Performing trend term curve fitting on the initial vibration displacement signal through the filter of the adaptive window to obtain a fitting trend term; Performing trend item removal on the initial vibration displacement signal according to the fitting trend item to obtain the trend item removed vibration signal; The target vibration displacement signal is determined from the detrended vibration signal according to the acceleration of the blade rotation speed and the Pearson correlation coefficient.

5. The blade vibration parameter online monitoring method according to claim 4 is characterized in that: The step of determining the target vibration displacement signal from the vibration signal with the trend removed according to the acceleration of the blade rotation speed and the Pearson correlation coefficient comprises: Calculating the data correlation between the initial vibration displacement signal and the vibration signal with the trend removed to obtain the Pearson correlation coefficient; If the Pearson correlation coefficient is greater than a preset coefficient threshold, the trend-removed vibration signal is used as the target vibration displacement signal; If the Pearson correlation coefficient is less than the preset coefficient threshold, the initial vibration displacement signal is divided into a plurality of sub-segments, the acceleration of each sub-segment is calculated according to the blade rotation speed, and the acceleration variance of each acceleration is calculated; If the acceleration variance is greater than a preset variance threshold, the preset fixed data window is reduced based on a preset unit to obtain a reduced data window, and the blade vibration displacement is intercepted based on the reduced data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold; If the acceleration variance is less than a preset variance threshold, the preset fixed data window is expanded based on the preset unit to obtain an expanded data window, and the blade vibration displacement is intercepted based on the expanded data window to obtain an initial vibration displacement signal until the Pearson correlation coefficient is greater than the preset coefficient threshold.

6. The blade vibration parameter online monitoring method according to claim 1, characterized in that: The step of performing ultrawavelet transformation on the target vibration displacement signal to obtain three-dimensional signal feature information and resonance interval of the target vibration displacement signal includes: Based on a preset fixed rotation frequency step length and a preset data length, extracting a plurality of signal data and corresponding rotation frequency data from the target vibration displacement signal; For each frequency conversion data, based on the Nyquist theorem, a confusion frequency range is determined according to the frequency conversion data; a center frequency is determined according to the confusion frequency range, and a discrete wavelet of multiple cycles is generated according to the center frequency; Convolving each discrete wavelet with the signal data corresponding to the frequency conversion data to obtain a plurality of amplitude-frequency responses, and geometrically averaging each of the amplitude-frequency responses to obtain a final ultrawavelet transform response corresponding to the frequency conversion data; The final responses of the ultra-wavelet transform corresponding to each of the frequency conversion data are spliced ​​to obtain the three-dimensional signal characteristic information; and the amplitude is screened according to a preset amplitude threshold and the three-dimensional signal characteristic information to obtain the resonance interval.

7. The blade vibration parameter online monitoring method according to claim 1, characterized in that: The performing nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters includes: determining target signal characteristic information from the three-dimensional signal characteristic information according to the resonance interval; Based on a single parameter method and a dimensional relationship corresponding to the three-dimensional signal characteristic information, nonlinear fitting is performed on the target signal characteristic information to obtain the blade vibration parameters.

8. An online monitoring device for blade vibration parameters, characterized in that: include: A processing module, used for acquiring actual arrival time data of the blade, and preprocessing the actual arrival time data to obtain normal time data; A calculation module, used to calculate the theoretical arrival time data of the blade according to the normal time data, and based on the blade tip timing principle, calculate the blade speed and blade vibration displacement according to the theoretical arrival time data; A removal module, configured to remove a trend item from the blade vibration displacement based on an adaptive window to obtain a trend item removal signal, and determine a target vibration displacement signal according to the acceleration of the blade rotation speed and the trend item removal signal; A transformation module, used for performing ultra-wavelet transformation according to the target vibration displacement signal to obtain three-dimensional signal characteristic information and resonance interval of the target vibration displacement signal; The fitting module is used to perform nonlinear fitting according to the three-dimensional signal characteristic information and the resonance interval to obtain blade vibration parameters.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the method for online monitoring of blade vibration parameters according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the blade vibration parameter online monitoring method as described in any one of claims 1 to 7.

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