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CN110332080B - A real-time monitoring method for fan blade health based on resonance response - Google Patents

A real-time monitoring method for fan blade health based on resonance response Download PDF

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CN110332080B
CN110332080B CN201910708242.9A CN201910708242A CN110332080B CN 110332080 B CN110332080 B CN 110332080B CN 201910708242 A CN201910708242 A CN 201910708242A CN 110332080 B CN110332080 B CN 110332080B
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毕俊喜
刘春苗
甘世明
郑成龙
宋晓娟
黄帅鑫
管虎
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Inner Mongolia University of Technology
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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Abstract

本发明公开了一种基于共振响应的风机叶片健康实时监测方法，该方法由共振模态检测试验台对风机叶片进行共振的模态检测，得出风机叶片不同状态下的产生共振的模态区间。通过研究叶片不同状态时产生共振模态的差异，实现对风机叶片的实时监测。所述风机叶片健康实时检测方法，利用共振模态检测试验台对风机叶片整个生命周期内不同阶段的共振模态进行实验并统计分析，建立磨合期共振模态区间、正常工作共振模态区间、剧烈磨损共振模态区间、失效断裂阶段共振模态区间。利用数理统计对所得数据进行区间划分，得出风机叶片不同阶段的模态区间，并通过实验得出各个状态的剩余寿命区间建立数据库。在实时检测阶段，利用激振源、环境振动分析仪、滤波器找出共振峰与数据库对比分析，分析得出所测风机叶片所处的健康阶段和所述风机叶片的剩余寿命。

Figure 201910708242

The invention discloses a real-time monitoring method for the health of fan blades based on resonance response. The method uses a resonance mode detection test bench to perform resonance modal detection on the fan blades, and obtains the modal intervals in which the fan blades generate resonance under different states. . Real-time monitoring of fan blades can be achieved by studying the differences in resonance modes generated when the blades are in different states. The real-time detection method for the health of the fan blade uses the resonance mode detection test bench to conduct experiments and statistical analysis on the resonance modes of the fan blades at different stages in the entire life cycle, and establish the resonance mode interval during the running-in period, the normal working resonance mode interval, Severe wear resonance mode interval, failure fracture stage resonance mode interval. The obtained data are divided into intervals by mathematical statistics, and the modal intervals of different stages of the fan blades are obtained, and the remaining life intervals of each state are obtained through experiments to establish a database. In the real-time detection stage, use the excitation source, the environmental vibration analyzer, and the filter to find out the resonance peak and compare it with the database, and analyze the health stage of the measured fan blade and the remaining life of the fan blade.

Figure 201910708242

Description

Fan blade health real-time monitoring method based on resonance response
Technical Field
The invention belongs to the technical field of mechanical equipment fault monitoring, and particularly relates to a fan blade health real-time monitoring method based on resonance response.
Background
In recent years, wind energy is mainly developed as a main new energy form, wind driven generators are more and more widely applied, and the health problem of a fan system is more and more important. For wind power generation equipment, the safety and reliability of a wind turbine blade are very important, the wind turbine blade is the first step of converting wind energy into electric energy, and the damage of the blade can cause the paralysis of the whole wind turbine. Typically, damage to wind turbine blades may be caused by humidity, fatigue, storms, and lightning strikes. Aerodynamic interference between different turbines in a wind farm may also place excessive loads on the blades, which if overloaded for a long period of time will cause the blades to fail. The design age of a common blade is ten to thirty years, and the wind turbine blade is very easy to damage due to aging, corrosion and fatigue of materials and long-term operation in outdoor severe environment in the long-term service process, and the blade is broken possibly caused by accumulated damage so as to collapse the whole wind turbine. Because the wind turbine blade has good aerodynamic performance, the geometrical mechanism of the wind turbine blade is relatively complex, and the wind turbine blade is bulky due to the requirement of wind turbine power. When real-time detection is carried out, great difficulty exists in strain degree and accurate service life assessment in the blades, and therefore, how to efficiently and accurately carry out real-time monitoring on the operation conditions of the blades is very important.
Disclosure of Invention
In order to solve the existing problems, the invention provides a real-time monitoring method for the health of a fan blade based on resonance response.
The invention is realized by the following technical scheme:
a real-time fan blade health monitoring method based on resonance response comprises a resonance mode detection test bed, a wind driven generator blade, an excitation source, an environmental vibration analyzer, a filter and an upper computer. And carrying out experimental detection on modes exciting resonance at different stages in the whole life cycle of the wind driven generator blade by using the resonance mode detection test bed.
The well recorded data obtained were subjected to the following statistical analysis:
taking the run-in period detection data as an example, the damping ratio and the natural frequency in the run-in period detection data are respectively subjected to interval estimation. Data population X obeys normal distribution N (mu, sigma)2) And carrying out interval estimation on the normal population mean value mu. Confidence intervals with 95% confidence of damping ratio and natural frequency are respectively calculated:
deriving from the sample values
Figure BDA0002152854820000021
n、σ
The confidence 1- α is 0.95, α is 0.05,
Figure BDA0002152854820000022
looking up the normal distribution numerical table to obtain mu0.025＝1.96
Confidence lower limit:
Figure BDA0002152854820000023
confidence upper limit:
Figure BDA0002152854820000024
and (5) solving a confidence interval of the damping ratio, and calculating the confidence interval of the natural frequency in the same way.
And solving the normal working resonance mode confidence interval, the severe abrasion resonance mode confidence interval and the failure fracture stage resonance mode confidence interval in the same way.
And establishing a running-in period resonance modal interval, a normal working resonance modal interval, a severe abrasion resonance modal interval and a failure fracture stage resonance modal interval. And carrying out interval division on the obtained data by utilizing mathematical statistics to obtain modal intervals of exciting resonance of the fan blade at different stages, obtaining the remaining life intervals of each state through experiments, and establishing a database. In the real-time monitoring stage, the resonance mode of the blade is measured by the excitation source, the environmental vibration analyzer and the filter, and the real-time detection data is compared and divided with each mode interval obtained by the experiment by adopting a dividing method in a clustering algorithm.
And obtaining the real-time state of the fan blade and the residual service life of the fan blade.
As a further optimization scheme of the invention, the resonance response-based real-time monitoring method for the health of the fan blade utilizes the characteristic that resonance modes excited at different stages in the life cycle of the blade are different, and carries out real-time monitoring and residual life assessment.
As a further optimization scheme of the invention, the main component of the fan blade is a composite material, the fan blade belongs to a nonlinear system, the amplitude jump phenomenon occurs in a resonance region, a resonance peak is obviously deformed, and super-harmonic resonance and sub-harmonic resonance can occur. The excitation source is used for inducing the fan blade to resonate, the environment vibration analyzer is used for detecting the mode of a resonance area, and the filter is used for eliminating super-harmonic resonance and sub-harmonic resonance and simplifying the obvious deformation of a resonance peak.
Drawings
FIG. 1 is an overall logic diagram of the present invention;
FIG. 2 is a connection diagram of the real-time detection device of the present invention;
FIG. 3 is a data processing logic diagram of the present invention;
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and drawings, but the present invention is not limited to these embodiments.
The invention realizes real-time monitoring by utilizing resonance response and is applied to the detection of the wind driven generator blade. As shown in fig. 1: a real-time monitoring method for fan blade health based on resonance response is divided into two parts, including a database establishing stage, a real-time detection stage and an intelligent decision stage. Firstly, a database is established, in order to accurately collect data, the resonance mode detection test bed is adopted to carry out staged detection on the fan blades, a plurality of groups of blades detected by the same model at different stages are synchronously detected, and the obtained data are separately recorded.
The well recorded data obtained were subjected to the following statistical analysis:
taking the run-in period detection data as an example, the damping ratio and the natural frequency in the run-in period detection data are respectively subjected to interval estimation. Data population X obeys normal distribution N (mu, sigma)2) And carrying out interval estimation on the normal population mean value mu. Confidence intervals with 95% confidence of damping ratio and natural frequency are respectively calculated:
deriving from the sample values
Figure BDA0002152854820000031
n、σ
The confidence 1- α is 0.95，α＝0.05，
Figure BDA0002152854820000041
Looking up the normal distribution numerical table to obtain mu0.025＝1.96
Confidence lower limit:
Figure BDA0002152854820000042
confidence upper limit:
Figure BDA0002152854820000043
and (5) solving a confidence interval of the damping ratio, and calculating the confidence interval of the natural frequency in the same way.
And solving the normal working resonance mode confidence interval, the severe abrasion resonance mode confidence interval and the failure fracture stage resonance mode confidence interval in the same way.
Establishing a modal interval of the fan blade: a running-in period resonance mode interval, a normal working resonance mode interval, a severe abrasion resonance mode interval and a failure fracture stage resonance mode interval. For ease of programming, a function pointer name is set for each interval: the index name of the resonant mode interval in the running-in period is Runnng-in; the pointer name of the normal working resonance mode interval is Regular-work; the pointer name of the violent abrasion resonance mode interval is Abrasive; the index of the resonant mode interval at the failure fracture stage is named Falife. In addition, the remaining life intervals corresponding to the various state intervals are obtained through experiments.
Subsequently, real-time detection is performed: under natural conditions, even if the fan is suspended, the detection accuracy of the fan blade cannot reach the experimental level, the main component of the fan blade is made of a composite material and belongs to a nonlinear system, the amplitude jump phenomenon occurs in a resonance area, a resonance peak is obviously deformed, and super-harmonic resonance and sub-harmonic resonance may occur, so that the actual detection is more difficult. Therefore, the resonance mode of the blade is measured by adopting the excitation source, the environmental vibration analyzer and the filter, so that the detection steps are simplified, and the measured result has enough reliability. The specific functions of the excitation source, the environment vibration analyzer and the filter are as follows: the excitation source is used for inducing the fan blade to resonate, the environment vibration analyzer is used for detecting the mode of a resonance area, and the filter is used for eliminating super-harmonic resonance and sub-harmonic resonance and simplifying the obvious deformation of a resonance peak. The obtained data is the mode of the blade of the wind driven generator measured in real time. Namely the damping ratio and the natural frequency.
And finally, carrying out comparison analysis and life prediction, and comparing and dividing the real-time detection data with each modal interval obtained by the experiment by using a dividing method in a clustering algorithm.
The algorithm idea is as follows: inputting: real-time detection data A [ p
The subscript q (p < ═ q < ═ r) is present, wherein the elements in a [ p.. q ] do not exceed a [ r ], and the elements in a [ q +1.. r ] are each greater than a [ r ].
The two indices i, j are initially set to p-1 and p, respectively, let j scan in [ p.. r ], if aj < ═ ar ], then a j is exchanged for a [ i +1], then i is increased by 1, as j increases, a [ p.. i ] and a [ i +1.. j ] also increase, and finally j reaches r, exchanging a [ i +1] for a [ r ].
C + + Source program
Figure BDA0002152854820000051
Figure BDA0002152854820000061
The program is a partial program, and the complete program compares the real-time detection data with the established database and analyzes the health stage of the detected fan blade and the residual service life of the fan blade.
As shown in fig. 2, the excitation source is disposed on one side of the fan blade, the input probe of the environmental vibration analyzer is disposed on the surface of the fan blade, the output probe is connected to the filter, and the filter is connected to the upper computer.
As shown in fig. 3, the resonance mode detection test bed measures multiple groups of experimental data, divides the obtained data into intervals by using mathematical statistics, divides a running-in period resonance mode interval, a normal working resonance mode interval, a severe wear resonance mode interval and a failure fracture stage resonance mode interval, controls a single time variable, estimates the remaining life of the fan blade corresponding to each interval, and makes analysis decisions.

Claims (2)

1.一种基于共振响应的风机叶片健康实时监测方法，包括共振模态检测试验台、风力机叶片、激振源、环境振动分析仪、滤波器以及上位机；1. A method for real-time monitoring of fan blade health based on resonance response, comprising a resonance mode detection test bench, a wind turbine blade, a vibration excitation source, an environmental vibration analyzer, a filter and a host computer; 利用所述共振模态检测试验台对所述风力机叶片整个生命周期内不同阶段所激发的共振模态进行实验并统计分析，利用数理统计对所得数据进行区间划分，建立风力机叶片磨合期共振模态区间、正常工作共振模态区间、剧烈磨损共振模态区间、失效断裂阶段共振模态区间；Use the resonance mode detection test bench to conduct experiments and statistical analysis on the resonance modes excited at different stages in the entire life cycle of the wind turbine blade, use mathematical statistics to divide the obtained data into intervals, and establish the wind turbine blade running-in period resonance Mode interval, normal working resonance mode interval, severe wear resonance mode interval, and failure fracture stage resonance mode interval; 所述基于共振响应的风机叶片健康实时监测方法包括建立数据库阶段、实时检测阶段和智能决策阶段；首先建立数据库，采用所述共振模态检测试验台对所述风力机叶片进行阶段性检测，将不同阶段的同型号所测叶片进行多组同步检测，所得数据分开记录；The method for real-time monitoring of fan blade health based on resonance response includes a database establishment stage, a real-time detection stage and an intelligent decision-making stage; firstly, a database is established, and the resonance mode detection test bench is used to perform phased detection on the wind turbine blades, and the The measured blades of the same type at different stages are subjected to multiple sets of synchronous detection, and the obtained data are recorded separately; 将记录好的所得数据进行如下统计分析：Perform the following statistical analysis on the recorded data: 将磨合期检测数据中的阻尼比、固有频率分别进行区间估计，数据总体X服从正态分布N(μ，σ2)，对正态总体均值μ作区间估计，分别计算阻尼比和固有频率置信度为95％的置信区间：Perform interval estimation on the damping ratio and natural frequency in the test data during the running-in period respectively, the data population X obeys the normal distribution N(μ, σ 2 ), make interval estimation on the normal population mean μ, and calculate the damping ratio and natural frequency confidence respectively. 95% confidence interval for degree: 根据样本值得出
Figure FDA0002808734060000011
n、σ；According to the sample value
Figure FDA0002808734060000011
n, σ; 置信度1-α＝0.95，α＝0.05，
Figure FDA0002808734060000012
查正态分布数值表得μ0.025＝1.96；Confidence 1-α=0.95, α=0.05,
Figure FDA0002808734060000012
Check the table of normal distribution values and get μ 0.025 = 1.96; 置信下限：
Figure FDA0002808734060000013
Lower confidence limit:
Figure FDA0002808734060000013
 置信上限：
Figure FDA0002808734060000014
Upper confidence limit:
Figure FDA0002808734060000014
 求出阻尼比的置信区间，并计算出固有频率的置信区间；Find the confidence interval of the damping ratio, and calculate the confidence interval of the natural frequency; 求出正常工作共振模态置信区间、剧烈磨损共振模态置信区间、失效断裂阶段共振模态置信区间；Obtain the confidence interval of the resonance mode of normal operation, the confidence interval of the resonance mode of severe wear, and the confidence interval of the resonance mode of failure and fracture; 通过实验得出与各个模态置信区间相对应的剩余寿命区间；The remaining life interval corresponding to each modal confidence interval is obtained through experiments; 利用聚类算法中的划分法对实时检测的数据与实验得出的各个模态置信区间进行对比及划分；Use the partition method in the clustering algorithm to compare and divide the real-time detection data and the confidence intervals of each modal obtained from the experiment; 所述聚类算法中的划分法具体为：The division method in the clustering algorithm is specifically: 输入：实时检测数据A[p...r]；Input: real-time detection data A[p...r]; 存在下标q(p<＝q<＝r)，其中A[p...q]中的元素不超过A[r]，而A[q+1...r]中的元素均大于A[r]；There is a subscript q (p<=q<=r), where the elements in A[p...q] do not exceed A[r], and the elements in A[q+1...r] are all greater than A [r]; 设置两个下标i，j初始时分别为p-1和p，让j在[p...r]中扫描，若A[j]<＝A[r]，则将A[j]与A[i+1]交换，然后i增加1，随着j的增加，A[p...i]和A[i+1...j]也会增长，最终j到达r，将A[i+1]与A[r]交换；Set two subscripts i, j are initially p-1 and p respectively, let j scan in [p...r], if A[j]<=A[r], then A[j] and A[i+1] is exchanged, then i increases by 1, and as j increases, A[p...i] and A[i+1...j] also grow, and finally j reaches r, and A[ i+1] is exchanged with A[r]; 将实时检测数据与建立的数据库对比，分析得出所测风力机叶片所处的健康阶段和所述风力机叶片的剩余寿命。The real-time detection data is compared with the established database, and the health stage of the measured wind turbine blade and the remaining life of the wind turbine blade are obtained through analysis. 2.根据权利要求1所述的基于共振响应的风机叶片健康实时监测方法，其特征在于：所述健康实时监测方法利用了叶片不同阶段所激发共振的模态不同这一特性，进行实时监测。2 . The real-time monitoring method for the health of fan blades based on resonance response according to claim 1 , wherein the real-time monitoring method for health utilizes the characteristic that the modes of resonance excited by different stages of the blade are different to perform real-time monitoring. 3 .
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