CN110332080B - Fan blade health real-time monitoring method based on resonance response - Google Patents
Fan blade health real-time monitoring method based on resonance response Download PDFInfo
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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
The invention discloses a fan blade health real-time monitoring method based on resonance response. The real-time monitoring of the fan blade is realized by researching the difference of resonance modes generated when the blade is in different states. According to the fan blade health real-time detection method, resonance modes of different stages in the whole life cycle of the fan blade are tested and statistically analyzed by using a resonance mode detection test bed, and 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 are established. And carrying out interval division on the obtained data by utilizing mathematical statistics to obtain modal intervals of different stages of the fan blade, and obtaining the remaining life intervals of all states through experiments to establish a database. In the real-time detection stage, an excitation source, an environmental vibration analyzer and a filter are used for finding out a resonance peak to be compared with a database for analysis, and the health stage of the detected fan blade and the residual life of the fan blade are obtained through analysis.
Description
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
n、σ
The confidence 1- α is 0.95, α is 0.05,
looking up the normal distribution numerical table to obtain mu0.025＝1.96
Confidence lower limit:
confidence upper limit:
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
n、σ
The confidence 1- α is 0.95，α＝0.05，
Looking up the normal distribution numerical table to obtain mu0.025＝1.96
Confidence lower limit:
confidence upper limit:
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
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. A real-time monitoring method for fan blade health based on resonance response comprises a resonance mode detection test bed, a wind turbine blade, an excitation source, an environmental vibration analyzer, a filter and an upper computer;
the resonance mode detection test bed is used for carrying out experiment and statistical analysis on resonance modes excited at different stages in the whole life cycle of the wind turbine blade, and the obtained data is subjected to interval division by using mathematical statistics to establish a resonance mode interval of the wind turbine blade in a running-in period, a normal working resonance mode interval, a severe abrasion resonance mode interval and a failure fracture stage resonance mode interval;
the fan blade health real-time monitoring method based on resonance response comprises a database establishing stage, a real-time detection stage and an intelligent decision stage; firstly, establishing a database, carrying out staged detection on the wind turbine blades by adopting the resonance modal detection test bed, carrying out multi-group synchronous detection on the blades detected in the same model at different stages, and separately recording the obtained data;
the well recorded data obtained were subjected to the following statistical analysis:
respectively carrying out interval estimation on the damping ratio and the natural frequency in the run-in period detection data, wherein the data total X obeys normal distribution N (mu, sigma)2) And performing interval estimation on the normal population mean value mu, and respectively calculating confidence intervals with 95% confidence of the damping ratio and the natural frequency:
deriving from the sample values
n、σ；
The confidence 1-alpha is 0.95,α＝0.05，
looking up the normal distribution numerical table to obtain mu0.025＝1.96；
Confidence lower limit:
confidence upper limit:
solving a confidence interval of the damping ratio, and calculating a confidence interval of the natural frequency;
solving a normal working resonance mode confidence interval, a severe abrasion resonance mode confidence interval and a failure fracture stage resonance mode confidence interval;
obtaining residual life intervals corresponding to the modal confidence intervals through experiments;
comparing and dividing the real-time detected data with each modal confidence interval obtained by experiments by using a dividing method in a clustering algorithm;
the partitioning method in the clustering algorithm specifically comprises the following steps:
inputting: detecting data A [ p.. r ] in real time;
the subscript q (p < ═ q < ═ r) is present, wherein elements in a [ p.. q ] do not exceed a [ r ], and elements in a [ q +1.. r ] are each greater than a [ r ];
setting two subscripts i, j as p-1 and p initially, respectively, scanning j in [ p.. r ], if aj < ═ ar ], exchanging aj with aji +1, then increasing i by 1, increasing a [ p.. i ] and aji +1.. j ] with the increase of j, and finally reaching r, exchanging aji +1 with ajr;
and comparing the real-time detection data with the established database, and analyzing to obtain the health stage of the blade of the wind turbine and the residual life of the blade of the wind turbine.
2. The resonance response based real-time monitoring method for the health of the fan blade according to claim 1, characterized in that: the real-time health monitoring method utilizes the characteristic that resonance modes excited by different stages of the blade are different to carry out real-time monitoring.
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| CN112985578B (en) * | 2019-12-13 | 2023-09-26 | 中车株洲电力机车研究所有限公司 | Wind turbine generator set resonance diagnosis method and device |
| WO2022109846A1 (en) * | 2020-11-25 | 2022-06-02 | 深圳市大疆创新科技有限公司 | State inspection method, apparatus and system for movable platform, and movable platform |
| CN114000989B (en) * | 2021-11-30 | 2023-07-18 | 中国华能集团清洁能源技术研究院有限公司 | Wind generating set blade aerodynamic performance attenuation detection method and test system |
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