CN103984952A - Method for diagnosing surface crack fault of blade of wind turbine generator of electric power system based on machine vision image - Google Patents
Method for diagnosing surface crack fault of blade of wind turbine generator of electric power system based on machine vision image Download PDFInfo
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Abstract
The invention discloses a method for diagnosing a surface crack fault of a blade of a wind turbine generator of an electric power system based on a machine vision image. The method comprises the first step of dividing the blade of the wind turbine generator to obtain blade elements, the second step of photographing the blade elements of the wind turbine generator and removing a background, the third step of carrying out secondary division on a blade element result image obtained in the second step to obtain image elements and carrying out feature extraction on the element result image, the fourth step of training a support vector machine used for diagnosing the fault, the fifth step of diagnosing the surface fault mode of the blade elements of the wind turbine generator through the trained support vector machine, and the sixth step of carrying out the operations of the second step, the third step and the fifth step on all the blade elements until the surface fault diagnosis of the whole blade is completed. The method is high in diagnosis precision.
Description
Technical field
The present invention relates to wind energy conversion system fault diagnosis field, specifically refer to the method to electric system blade of wind-driven generator surface crack diagnosing malfunction based on machine vision image.
Background technology
Blade is as one of the highest parts of wind energy conversion system failure rate, because the reasons such as it is bulky, installation of sensors is difficult, watch-dog is expensive do not have a kind of fault diagnosis and fault supervision means that can rig-site utilization all the time.The fault diagnosis of existing pneumatic equipment blades made is mainly indirectly to diagnose pneumatic equipment blades made fault by the fault-signal supervision to wind energy conversion system driving-chain.The research of the fault diagnosis of pneumatic equipment blades made has been caused scientific research personnel's attention and carried out multiple tentative research.Comprise the surperficial noncontact method for diagnosing faults of the contact diagnostic method of the sensors such as vibration, stress, acoustic emission being installed and blade being carried out to thermal imaging, x-ray scanning, digital picture correlation analysis of pneumatic equipment blades made.
For contact method for diagnosing faults, owing to being by sensor institute installation position signal is monitored to reach the object that blade integral is monitored, so localization of fault accuracy rate is lower, and because the more meeting of required number of sensors brings the shortcomings such as high cost and difficult arrangement.But contactless method for diagnosing faults is accurately fault is positioned because the expensive and bulky very difficult installation at the scene of its equipment price is not applied equally.
Summary of the invention
The object of this invention is to provide a kind of based on machine vision image the method to electric system blade of wind-driven generator surface crack diagnosing malfunction.
Above-mentioned purpose of the present invention realizes by following technical solution: the method based on machine vision image to electric system blade of wind-driven generator surface crack diagnosing malfunction, the method comprises the steps:
Step 1, division blade of wind-driven generator, obtain blade primitive;
The detailed process of step 1 is as follows: first, adopts the method coat colour band on blade of wind-driven generator surface to carry out region division to whole blade, is divided into N sub regions, and N >=1 wherein, any one subregion is all called blade primitive;
Step 2, blade of wind-driven generator primitive is taken pictures, and background is rejected;
The detailed process of step 2 is as follows: first blade of wind-driven generator primitive is taken, obtained the original image of blade primitive; Then, original image is processed through gray scale, from cromogram, become gray-scale map, obtain the gray level image of blade primitive; By adopting Roberts contour extraction method to carry out blade primitive profile to gray level image, extract, obtain the bianry image of blade primitive, this bianry image Leaf primitive is white prospect, and remainder is black background; Further, bianry image is carried out to morphology processing, reach the effect of preliminary denoising, obtain preliminary denoising image; And then, in preliminary denoising image, adopt bianry image connected component labeling method, preliminary denoising image is carried out to twice sweep, scan for the first time by the pixel of lining by line scan, the neighbouring relations between judgement pixel, give identical connection label to belonging to the pixel of same connected region; The mark repeating is eliminated in scanning for the second time, merge the subregion belong to same connected region but to there is isolabeling not number, white 8 connected regions that find region area maximum by two step scannings, white 8 connected regions of this maximum are exactly the blade of wind-driven generator primitive image-region that will obtain; Finally, from original image, scratch and remove the region outside blade of wind-driven generator primitive image-region, reach the object of rejecting background, can access the blade primitive result images of aerogenerator simultaneously;
Step 3, the blade primitive result images that step 2 is obtained carry out secondary and are divided into picture element, and primitive result images is carried out to feature extraction;
The detailed process of step 3 is as follows: first, the blade primitive result images that step 2 is obtained is divided into a plurality of parallelogram mesh, and then according to grid, pneumatic equipment blades made primitive result images is split into picture element; Then adopt data characteristics extraction algorithm to process picture element, obtain the data characteristics of picture element.
Step 4, fault diagnosis support vector machine used is trained;
The detailed process of step 4 is as follows: using the picture element data characteristics obtaining in step 3 as training data; Then, adopt training data to train support vector machine, obtain the support vector machine training; In this step 4, the training of support vector machine adopts standard algorithm of support vector machine, and concrete training step is as follows:
(1) establish training set T={ (x
1, y
1), (x
2, y
2) ... (x
l, y
l) ∈ (X, Y)
l
X wherein
i∈ X ∈ R
p, y
i∈ Y={-1,1}, i=1 ... l;
X
ithe vector forming for the data characteristics of picture element; If x
ion corresponding picture element, there is blade crackle, y
ibe 1, otherwise y
ifor-1.
(2) solve optimization problem (1.1), obtain optimum solution:
α
i≥0,i＝1,…,l.
α wherein
ifor the intermediate variable of introducing, be the multiplier corresponding with each training data.
(3) select α
*positive component, calculate
B wherein
*for the intermediate variable of introducing, it is classification thresholds.
(4) construct linear optimal classification lineoid, draw decision function:
The support vector machine that step 5, use train is diagnosed the superficial failure kind of blade of wind-driven generator primitive;
The detailed process of step 5 is as follows: the picture element data characteristics that blade of wind-driven generator primitive is obtained through step 3 is followed successively by the decision function that variable x substitution step 4 obtains, value by decision function judges whether this generator blade primitive exists surface crack fault, if being 1 expression, the value of decision function there is blade crackle, if the value of decision function does not exist blade crackle for-1 expression;
Step 6, all blade primitives are all carried out to step 2, three, five operation, until complete the superficial failure diagnosis of integrated plate blade.
Above-mentioned formula is all quoted: Zhang Xuegong. about Statistical Learning Theory and support vector machine [J]. and the formula in robotization journal .2000 (01) is prior art.
In the present invention, in described step 3, it is to adopt the picture feature extraction method of analyzing based on dual-tree complex wavelet that picture element is carried out to data characteristics processing.
In the present invention, in described step 3, it is the image data method of descent adopting based on manifold learning that picture element is carried out to data characteristics processing.
In the present invention, in described step 3, it is the image data method of descent adopting based on improving manifold learning arithmetic that picture element is carried out to data characteristics processing.
In sum, this patent provides a kind of blade of wind-driven generator superficial failure diagnostic method based on computer vision; First, divide blade of wind-driven generator, obtain blade primitive.Then, pneumatic equipment blades made primitive is taken pictures, and background is rejected.And then, blade primitive result images is carried out to secondary and be divided into picture element, and to piecemeal document image primitive position.Further, picture element is carried out to feature extraction.The characteristic of further, getting a part of picture element is trained support vector machine as training data.Finally, by the support vector machine training, to whole pneumatic equipment blades made, whether exist surface crack fault to judge, and supervise the development of its fault.
Compared with prior art, this patent utilizes image acquiring apparatus that pneumatic equipment blades made fault is exercised supervision and diagnosed.For the blade background complexity in blade fault monitor procedure, blade fault location difficulty, blade fault kind classification difficulty, solution has been proposed.By being rejected to flow process, blade fault positioning flow and blade classification process, blade background designs.This patent can be rejected the background of blade complexity effectively, removes the complex background impact that identification produces on leaf image.Adopt colour band and anchor point to position and can effectively supervise the growth of fault pneumatic equipment blades made fault.
Accompanying drawing explanation
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail.
Fig. 1 is the overall flow schematic diagram of diagnostic method of the present invention;
Fig. 2 is that diagnostic method Leaf image background of the present invention is rejected flow process;
Fig. 3 is that in diagnostic method of the present invention, picture element extracts flow process.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in detail.
The invention provides a kind of based on machine vision image the method to electric system blade of wind-driven generator surface crack diagnosing malfunction, the method comprises the steps:
Step 1, division blade of wind-driven generator, obtain blade primitive;
In embodiments of the present invention, be used in pneumatic equipment blades made surface and coat the method for colour band leaf area is divided, so that the extraction to guarded region.This is due to nearly hundred meters of large scale wind power machine single blade length, therefore single capture can not complete the image acquisition to whole blade.By colour band, divide, blade integral is carried out to region and be divided into 20 sub regions, any one subregion is all called blade primitive, and then regulating camera angle to make the overwhelming majority in blade photograph region is a certain region, by conversion camera angle, just can reach the effect to whole blade scanning.Also can according to actual needs blade integral be carried out to region and be divided into N sub regions, wherein N >=1.
Step 2, blade of wind-driven generator primitive is taken pictures, and background is rejected;
In embodiments of the present invention, first choosing suitable shooting angle and intensity of illumination takes pneumatic equipment blades made.Wherein, suitable shooting angle and intensity of illumination are by testing acquisition.Shooting angle and normal angle are at 0～75 °, the image that intensity of illumination obtains when 90～1300LUX can retain the information of image substantially, can not produce the fault zone producing due to illumination variation and degenerate or growth, can guarantee that fault picture is easy to identification and extracts.
Further, convert original image to gray-scale map, then by adopting Roberts contour extraction method and morphology disposal route to obtain the binary map of image.Roberts contour extraction method is asked for image border by the gradient of image pixel gray scale and is changed, and then the profile of blade detected.Morphology disposal route comprises expansion, corrodes with and combine opening operation and the closed operation of use, can effectively suppress the noise of image, guarantees that the profile of wind energy conversion system image remains unchanged simultaneously.
And then bianry image is carried out obtaining after morphology processing the two-value denoising image of image, find region the best part in image to be blade place part, then take out the bianry image that other parts obtain blade, finally find the image that can extract whole blade primitive in original image in corresponding blade bianry image for white part, reach the object of rejecting background, obtain blade primitive result images.
First, original image is processed through gray scale, from cromogram, becomes gray-scale map; Then, use Roberts operator to carry out profile extraction to wind energy conversion system primitive image, obtain the binary map of wind energy conversion system picture, pneumatic equipment blades made is white prospect, and remainder is black background; Further, binary map is carried out to morphology processing, can reach the effect of preliminary denoising; And then, in the image of preliminary denoising, find white 8 connected regions of region area maximum, be exactly the pneumatic equipment blades made elementary area of gained; Finally, from former figure, scratch and remove the region outside pneumatic equipment blades made primitive, just can obtain complete pneumatic equipment blades made primitive result images.White 8 connected regions that find region area maximum are also prior art.
Wherein, find white 8 connected regions of region area maximum to adopt bianry image connected component labeling method, two-step approach, preliminary denoising image is carried out to twice sweep: scan for the first time by the pixel of lining by line scan, neighbouring relations between judgement pixel, give identical connection label to belonging to the pixel of same connected region.But the mark that scanning eliminate to repeat for the second time, merges the subregion that belongs to same connected region have isolabeling not number.
Step 3, blade primitive result images is carried out to secondary be divided into picture element, and picture element is carried out to feature extraction;
First, blade primitive result images is carried out to secondary and be divided into picture element; Because large-scale blade surface can produce radian, in embodiments of the present invention, artificial making a mark a little again in blade primitive result images, can divide region secondary by four angle points in gauge point and region, and these grid inside can be similar to be thought at grade.Utilize gauge point to cut apart leaf area, leaf area is divided into a plurality of parallelogram mesh, and then according to grid, wind energy conversion system leaf image is divided into picture element, to reach the object of refinement guarded region, and can position trouble location.
First, by coating the pneumatic equipment blades made of colour band, after the processing of step 1, obtain blade primitive result images; Then, utilize gauge point to cut apart blade primitive result images, blade blade primitive is divided into a plurality of grids; Finally, according to grid, pneumatic equipment blades made primitive result images is split into subregion, just obtain wind energy conversion system picture element, recorded the position after piecemeal simultaneously, can reach the fault category of the whole blade of monitoring and the object of fault growth by monitor for faults kind and fault.
Further, wind energy conversion system picture element is carried out to feature extraction; In embodiments of the present invention, to picture element storehouse, adopt several data feature extraction algorithm to process picture element, obtain data characteristics and extract result.
Particularly, there are following 3 kinds of data characteristics extraction algorithms:
1, the picture feature of analyzing based on dual-tree complex wavelet is extracted:
First, adopting dual-tree complex wavelet to carry out two dimension to image decomposes; Then, select energy, entropy, moment of inertia, relevant average and standard deviation as 8 dimensional features, the textural characteristics of image is extracted, as the standard of characteristics of image.
Dual-tree complex wavelet refers to that adopting two wavelet tree to carry out analyzing and processing makes algorithm have approximate translation invariance and coefficient of dissociation correspondence and each opposite sex of direction.The result that dual-tree complex wavelet image decomposes can retain the details of original signal more with respect to discrete wavelet analysis, has reduced local distortion.
2, the image data dimensionality reduction based on manifold learning:
Manifold learning refers to from the data of dimensional Euclidean Space sampling and recovers low dimensional manifold structure, obtains corresponding embedding mapping, to realize yojan, the dimensionality reduction or visual of data simultaneously.The LLE method in epidemiology learning method chosen in the embodiment of the present invention is as data processing method.
LLE algorithm can be summed up as three steps: (1) finds k Neighbor Points of each point in high dimensional data sample; (2) by the Neighbor Points of each sample point, calculated the partial reconstruction weight matrix of this sample point; (3) by the partial reconstruction weight matrix of this sample point and its Neighbor Points, calculated the output valve of this sample point.
In the embodiment of the present invention, use LLE to carry out the data that obtain after two and three dimensions dimensionality reduction, the result obtaining as Data Dimensionality Reduction to database intra vane fault picture data.
3, the image data dimensionality reduction based on improving manifold learning arithmetic:
First, by fuzzy clustering, raw data is divided into n class, and then obtains each class cluster centre and individual fuzzy membership.
Then, according to the result of fuzzy clustering, select the sample at the most close Mei Lei center as the training sample of General Neural Network cluster.Use training data training General Neural Network.The output sequence of all input sample datas of neural network forecast that train for General Neural Network prediction module.
Further, according to neural network forecast output sequence, Y is divided into n class sample data X, then obtains all sample mean mean in every class
i(i=1,2 ..., n), obtain the distance ecent that all sample X prop up to center
i(i=1,2 ..., n), from M sample of distance matrix chosen distance minimum, as one group, set its corresponding network and be input as i.So again obtained n * m group network training data, its input data are raw data, and output data are category feature data.Obtain sample class number, the number of center of a sample and every class sample.
Finally, by number and the characteristic of the sample class number obtaining, center of a sample, every class sample, LLE algorithm carries out dimension-reduction treatment to data, obtains data characteristics and extracts result.
Step 4, fault diagnosis support vector machine used is trained;
First, using the part in the picture element data characteristics obtaining in step 3 as training data; Then, adopt training data to support vector machine.
Support vector machine is the statistical theory based on research and learning law thereof under finite sample.Its objective is that finding a higher-dimension lineoid classifies to sample data, and wish that can make the lineoid of two class sample interval maximums is that plane builds principle.
In embodiments of the present invention, adopt standard algorithm of support vector machine, its training step is as follows:
(1) establish training set T={ (x
1, y
1), (x
2, y
2) ... (x
l, y
l) ∈ (X, Y)
l
X wherein
i∈ X ∈ R
p, y
i∈ Y={-1,1}, i=1 ... l;
X
ithe vector forming for the data characteristics of picture element; If x
ion corresponding picture element, there is blade crackle, y
ibe 1, otherwise y
ifor-1.
(2) solve optimization problem (1.1), obtain optimum solution:
α
i≥0,i＝1,…,l.
α wherein
ifor the intermediate variable of introducing, be the multiplier corresponding with each training data.
(3) select α
*positive component, calculate
B wherein
*for the intermediate variable of introducing, it is classification thresholds.
(4) construct linear optimal classification lineoid, draw decision function:
Whether the support vector machine training in step 5, use step 4 exists crack fault to diagnose to the surface of blade of wind-driven generator primitive.
Concrete diagnostic method is as follows:
The picture element data characteristics that blade of wind-driven generator primitive is obtained through step 3 is followed successively by decision function in x substitution support vector machine, by the value of decision function, judges whether this generator blade primitive exists surface crack fault.The value of decision function is 1 or-1, and wherein 1 for existing blade crackle, and-1 for not existing.And then be 1 by the value of the corresponding decision function of which picture element, can further judge the position of surface crack.
For the fault of differentiating, can divide fault level, and fault judged result is stored as in the fault data table of database.By wind energy conversion system fault, dispose module database is monitored, if fault data table produces renewal meeting, according to different fault levels, hardware is operated, comprise warning, chain shutdown etc., and the operation of making is carried out to historical record.
Step 6, all blade primitives are all carried out to step 2, three, five operation, until complete the superficial failure diagnosis of integrated plate blade.
First, divide blade of wind-driven generator, obtain blade primitive; Then, pneumatic equipment blades made is taken pictures, and background is rejected.And then, blade primitive result images is carried out to secondary and be divided into picture element, and to piecemeal document image primitive position.Further, picture element is carried out to feature extraction.Picture element database is adopted to dual-tree complex wavelet is processed, manifold learning is processed, improve the several data feature extraction algorithms such as popular study processing processes image, and the characteristic of getting a part of picture element is trained to support vector machine as training data.Finally, by the support vector machine training, to pneumatic equipment blades made, whether exist surface crack fault to judge, and supervise the development of its fault.
In sum, this patent provides a kind of blade of wind-driven generator superficial failure diagnostic method based on computer vision: first, divide blade of wind-driven generator, obtain blade primitive.Then, pneumatic equipment blades made primitive is taken pictures, and background is rejected.And then, blade primitive result images is carried out to secondary and be divided into picture element, and to piecemeal document image primitive position.Further, picture element is carried out to feature extraction.The characteristic of further, getting a part of picture element is trained support vector machine as training data.Finally, by the support vector machine training, to whole pneumatic equipment blades made, whether exist surface crack fault to judge, and supervise the development of its fault.
Finally it should be noted that: above embodiment only, in order to technical scheme of the present invention to be described, is not intended to limit, the explanation of above embodiment is just for helping to understand technical scheme of the present invention; For one of ordinary skill in the art, according to thought of the present invention, change part in specific embodiments and applications, within all should being included in protection scope of the present invention.
Claims (4)
1. the method to electric system blade of wind-driven generator surface crack diagnosing malfunction based on machine vision image, the method comprises the steps:
Step 1, division blade of wind-driven generator, obtain blade primitive;
The detailed process of step 1 is as follows: first, adopts the method coat colour band on blade of wind-driven generator surface to carry out region division to whole blade, is divided into N sub regions, and N >=1 wherein, any one subregion is all called blade primitive;
Step 2, blade of wind-driven generator primitive is taken pictures, and background is rejected;
The detailed process of step 2 is as follows: first blade of wind-driven generator primitive is taken, obtained the original image of blade primitive; Then, original image is processed through gray scale, from cromogram, become gray-scale map, obtain the gray level image of blade primitive; By adopting Roberts contour extraction method to carry out blade primitive profile to gray level image, extract, obtain the bianry image of blade primitive, this bianry image Leaf primitive is white prospect, and remainder is black background; Further, bianry image is carried out to morphology processing, reach the effect of preliminary denoising, obtain preliminary denoising image; And then, in preliminary denoising image, adopt bianry image connected component labeling method, preliminary denoising image is carried out to twice sweep, scan for the first time by the pixel of lining by line scan, the neighbouring relations between judgement pixel, give identical connection label to belonging to the pixel of same connected region; The mark repeating is eliminated in scanning for the second time, merge the subregion belong to same connected region but to there is isolabeling not number, white 8 connected regions that find region area maximum by two step scannings, white 8 connected regions of this maximum are exactly the blade of wind-driven generator primitive image-region that will obtain; Finally, from original image, scratch and remove the region outside blade of wind-driven generator primitive image-region, reach the object of rejecting background, can access the blade primitive result images of aerogenerator simultaneously;
Step 3, the blade primitive result images that step 2 is obtained carry out secondary and are divided into picture element, and primitive result images is carried out to feature extraction;
The detailed process of step 3 is as follows: first, the blade primitive result images that step 2 is obtained is divided into a plurality of parallelogram mesh, and then according to grid, pneumatic equipment blades made primitive result images is split into picture element; Then adopt data characteristics extraction algorithm to process picture element, obtain the data characteristics of picture element;
Step 4, fault diagnosis support vector machine used is trained;
The detailed process of step 4 is as follows: using the picture element data characteristics obtaining in step 3 as training data; Then, adopt training data to train support vector machine, obtain the support vector machine training;
In this step 4, the training of support vector machine adopts standard algorithm of support vector machine, and concrete training step is as follows:
(1) establish training set T={ (x
1, y
1), (x
2, y
2) ... (x
l, y
l) ∈ (X, Y)
l
X wherein
i∈ X ∈ R
p, y
i∈ Y={-1,1}, i=1 ... l;
X
ithe vector forming for the data characteristics of picture element; If x
ion corresponding picture element, there is blade crackle, y
ibe 1, otherwise y
ifor-1
(2) solve optimization problem (1.1), obtain optimum solution:
α
i≥0,i＝1,…,l.
α wherein
ifor the intermediate variable of introducing, be the multiplier corresponding with each training data;
(3) select α
*positive component, calculate
B wherein
*for the intermediate variable of introducing, it is classification thresholds;
(4) construct linear optimal classification lineoid, draw decision function:
The support vector machine that step 5, use train is diagnosed the superficial failure kind of blade of wind-driven generator primitive;
The detailed process of step 5 is as follows: the picture element data characteristics that blade of wind-driven generator primitive is obtained through step 3 is followed successively by the decision function that variable x substitution step 4 obtains, value by decision function judges whether this generator blade primitive exists surface crack fault, if being 1 expression, the value of decision function there is blade crackle, if the value of decision function does not exist blade crackle for-1 expression;
Step 6, all blade primitives are all carried out to step 2, three, five operation, until complete the superficial failure diagnosis of integrated plate blade.
According to claim 1 based on machine vision image the method to electric system blade of wind-driven generator surface crack diagnosing malfunction, it is characterized in that: in described step 3, it is to adopt the picture feature extraction method of analyzing based on dual-tree complex wavelet that picture element is carried out to data characteristics processing.
According to claim 1 based on machine vision image the method to electric system blade of wind-driven generator surface crack diagnosing malfunction, it is characterized in that: in described step 3, it is the image data method of descent adopting based on manifold learning that picture element is carried out to data characteristics processing.
According to claim 1 based on machine vision image the method to electric system blade of wind-driven generator surface crack diagnosing malfunction, it is characterized in that: in described step 3, it is the image data method of descent adopting based on improving manifold learning arithmetic that picture element is carried out to data characteristics processing.
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| CN106778723A (en) * | 2016-11-28 | 2017-05-31 | 华中科技大学 | A kind of pneumatic equipment bladess surface image extracting method in complex background environment |
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| CN108459027A (en) * | 2018-03-21 | 2018-08-28 | 华北电力大学 | A kind of blade of wind-driven generator detection method of surface flaw based on image procossing |
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| CN107587983A (en) * | 2017-09-01 | 2018-01-16 | 天津科技大学 | Novel wind motor blade state monitors and fault early warning system |
| CN108459027A (en) * | 2018-03-21 | 2018-08-28 | 华北电力大学 | A kind of blade of wind-driven generator detection method of surface flaw based on image procossing |
| CN108956655A (en) * | 2018-07-06 | 2018-12-07 | 西安交通大学 | A kind of gas turbine high temperature leaf digital ray detection partition method of unknown-model |
| CN108956655B (en) * | 2018-07-06 | 2019-12-20 | 西安交通大学 | Digital ray detection partitioning method for high-temperature blades of gas turbine with unknown model |
| WO2021036639A1 (en) * | 2019-08-31 | 2021-03-04 | 深圳市广宁股份有限公司 | Intelligent detection method for wind power device and related products |
| CN111413404A (en) * | 2020-03-27 | 2020-07-14 | 天津大学 | Blade crack online measurement method based on blade tip timing and support vector machine principle |
| CN111426459A (en) * | 2020-04-13 | 2020-07-17 | 天津大学 | Blade crack online measurement method based on blade tip timing and naive Bayes algorithm |
| WO2022157303A1 (en) | 2021-01-21 | 2022-07-28 | Eolotec Gmbh | Method and device for detecting and monitoring a structural change of structural components of wind turbines |
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