CN112666219A - Blade detection method, device and equipment based on infrared thermal imaging - Google Patents
Blade detection method, device and equipment based on infrared thermal imaging Download PDFInfo
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Abstract
The invention discloses a blade detection method based on infrared thermal imaging, which comprises the following steps: acquiring a thermal imaging image of the surface of the fan blade; processing the thermal imaging image to obtain a first image set; calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model; and detecting the image to be detected according to the variable linear regression feature model so as to obtain the defect information of the fan blade. The scheme provided by the invention can realize accurate detection of the surface defects of the fan blade, reduce the detection cost and improve the detection efficiency.
Description
Technical Field
The invention relates to the technical field of wind driven generators, in particular to a blade detection method, a blade detection device and blade detection equipment based on infrared thermal imaging.
Background
Wind energy is an important renewable energy source, and with the expansion of the wind energy market in China, the fan manufacturing industry gradually enters a high-speed development period. The service life and the safety of the wind driven generator influence the step of wind power utilization and development, and the fan blade is a core component of the wind driven generator, so that the service life and the safety of the fan blade directly influence the service life and the safety condition of the whole wind driven generator set. Because the operating environment of a wind power plant is complex, the fan blades operate at high altitude all day long, the influence of various factors such as wind sand, pollution, lightning stroke, typhoon and the like is received for a long time, the fan blades are easy to have defects and gradually expand, if the defects of the fan blades cannot be found in time, the load and the rigidity matrix can be directly influenced, and finally the service life and the operation safety of the blades are reduced.
In the prior art, the surface of a fan blade is detected by a visual observation method (including modes of adopting a high-power telescope, high-altitude detour visual detection and the like), the method is non-contact detection and is visual, but the method has the problems of high detection cost of manual inspection, large subjective influence of people, low recognition rate, low detection efficiency and the like.
Disclosure of Invention
In view of the above, the present invention provides a blade detection method, device and apparatus based on infrared thermal imaging, which can implement accurate detection of surface defects of a fan blade, reduce detection cost and improve detection efficiency.
In order to achieve the above object, the present invention provides a blade detection method based on infrared thermal imaging, including:
acquiring a thermal imaging image of the surface of the fan blade;
processing the thermal imaging image to obtain a first image set;
calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model;
and detecting the image to be detected according to the variable linear regression feature model so as to obtain the defect information of the fan blade.
Preferably, the step of obtaining a first image set after processing the images comprises:
denoising the image through a mean value filter, and then carrying out graying processing to obtain a grayscale image set;
and performing characteristic scaling on the gray level image set to a preset range to obtain a first image set.
Preferably, the step of performing the graying processing includes:
and carrying out graying processing on the image by a weighted average method.
Preferably, the graying the image by the weighted average method includes:
and performing weighted average calculation on the RGB three components by different weights according to the f (i, j) ═ 0.30R (i, j) +0.59G (i, j) +0.11B (i, j), wherein i represents the ith row and j represents the jth column.
Preferably, the step of scaling the characteristics of the grayscale image set to a preset range includes:
according to
And performing characteristic scaling on the gray level image set to a preset range.
Preferably, the step of calculating the defect feature of the first image set by using a gradient descent algorithm to obtain a multivariate linear regression feature model includes the following steps:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and the number of the first and second groups,
gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
In order to achieve the above object, the present invention further provides a blade detecting device based on infrared thermal imaging, the device comprising:
the acquiring unit is used for acquiring a thermal imaging image of the surface of the fan blade;
the processing unit is used for processing the thermal imaging image to obtain a first image set;
the calculation unit is used for calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model;
and the detection unit is used for detecting the image to be detected according to the variable linear regression feature model so as to acquire the defect information of the fan blade.
Preferably, the processing unit further includes:
the first processing unit is used for carrying out graying processing after the image is denoised by the mean value filter to obtain a grayscale image set;
and the second processing unit is used for carrying out characteristic scaling on the gray level image set to a preset range to obtain a first image set.
Preferably, the computing unit specifically includes:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and the number of the first and second groups,
gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
To achieve the above object, the present invention further provides an infrared thermal imaging-based blade detection apparatus, including a processor, a memory, and a computer program stored in the memory, where the computer program is executable by the processor to implement an infrared thermal imaging-based blade detection method as described in the above embodiments.
Has the advantages that:
according to the scheme, the thermal imaging image of the surface of the fan blade is obtained, the thermal imaging image is processed to obtain a first image set, the defect characteristics of the first image set are calculated by adopting a gradient descent algorithm to obtain a multivariable linear regression characteristic model, the image to be detected is detected according to the variable linear regression characteristic model, so that the defect information of the fan blade is obtained, the accurate detection of the surface defects of the fan blade can be realized, the specific defect type of the surface of the fan blade can be accurately identified, the detection cost is greatly reduced, and the detection efficiency is improved.
In the above scheme, the step of obtaining the first image set after processing the images includes: and denoising the image through a mean filter, performing graying processing to obtain a grayscale image set, performing characteristic scaling on the grayscale image set to a preset range to obtain a first image set, and enhancing the contrast of the image and improving the operation speed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a blade detection method based on infrared thermal imaging according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a blade detection apparatus based on infrared thermal imaging according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The present invention will be described in detail with reference to the following examples.
The blade detection method based on the infrared thermal imaging can realize accurate detection of the surface defects of the fan blade, reduce the detection cost and improve the detection efficiency.
Fig. 1 is a schematic flow chart of a blade detection method based on infrared thermal imaging according to an embodiment of the present invention.
In this embodiment, the method includes:
and S11, acquiring a thermal imaging image of the surface of the fan blade.
When the unmanned aerial vehicle is specifically implemented, the image of the surface of the fan blade is obtained through the unmanned aerial vehicle. Specifically, the unmanned aerial vehicle is provided with a high-definition camera and an infrared camera to shoot image information, and the high-definition image and the related pose information are transmitted to the processing platform through WiFi.
And S12, processing the thermal imaging image to obtain a first image set.
Wherein, after the image is processed, the step of obtaining a first image set comprises:
s12-1, denoising the image through a mean value filter, and then carrying out graying processing to obtain a grayscale image set.
In particular, the graying process is a process of changing a color image including brightness and color into a grayscale image. The graying process further comprises the steps of obtaining RGB components according to the read data image, calculating the gray value of the pixel point, and re-assigning according to the color components of the pixel point, so as to obtain a gray image.
Wherein the step of performing graying processing comprises:
graying the image by using a weighted average method.
Wherein the graying the image by the weighted average method comprises:
and performing weighted average calculation on the RGB three components by different weights according to the f (i, j) ═ 0.30R (i, j) +0.59G (i, j) +0.11B (i, j), wherein i represents the ith row and j represents the jth column.
And S12-2, performing characteristic scaling on the gray level image set to a preset range to obtain a first image set.
Wherein the step of scaling the characteristics of the grayscale image set to a preset range comprises:
according to
And performing characteristic scaling on the gray level image set to a preset range.
And S13, calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model.
The step of calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model comprises the following steps:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and the number of the first and second groups,
gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
In the present embodiment, the conditional probability formula hθ(x) P (y is 1| x; Θ) is conditioned by θ, which is obtained by calculation of logistic regression, gradient descent of logistic regression, and gradient descent of regularization term. The purpose of the gradient descent by adding a regularization term is toThe multivariate linear regression feature model is prevented from being over-fitted, namely the multivariate linear regression feature model is prevented from being too complicated due to too high order, so that the detection precision is reduced.
And S14, detecting the image to be detected according to the variable linear regression feature model, thereby obtaining the defect information of the fan blade.
In specific implementation, after the image to be detected is detected through the variable linear regression feature model so as to obtain the defect information of the fan blade, the method further comprises the step of identifying the defect type of the surface of the fan blade through detecting the image to be detected, wherein the defect type of the surface of the fan blade comprises air bubbles, icing, roughening, sand holes, external cracks and the like on the surface of the fan blade.
The blade detection device based on the infrared thermal imaging can realize accurate detection of the surface defects of the fan blade, reduce the detection cost and improve the detection efficiency.
Fig. 2 is a schematic structural diagram of a blade detection apparatus based on infrared thermal imaging according to an embodiment of the present invention.
In this embodiment, the apparatus 20 includes:
an acquisition unit 21 for acquiring a thermographic image of the surface of the fan blade.
And the processing unit 22 is configured to process the thermal imaging image to obtain a first image set.
And the calculating unit 23 is configured to calculate the defect feature of the first image set by using a gradient descent algorithm, so as to obtain a multivariate linear regression feature model.
And the detection unit 24 is used for detecting the image to be detected according to the variable linear regression feature model so as to acquire the defect information of the fan blade.
Wherein the processing unit 22 further includes:
(1) and the first processing unit is used for carrying out graying processing after the image is denoised by the mean value filter to obtain a grayscale image set.
Wherein the graying processing comprises:
graying the image by using a weighted average method.
Wherein the graying the image by the weighted average method comprises:
and performing weighted average calculation on the RGB three components by different weights according to the f (i, j) ═ 0.30R (i, j) +0.59G (i, j) +0.11B (i, j), wherein i represents the ith row and j represents the jth column.
(2) And the second processing unit is used for carrying out characteristic scaling on the gray level image set to a preset range to obtain a first image set.
The step of scaling the characteristics of the gray image set to a preset range comprises the following steps:
according to
And performing characteristic scaling on the gray level image set to a preset range.
The calculating unit 23 specifically includes:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and the number of the first and second groups,
gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
Each unit module of the apparatus 20 can respectively execute the corresponding steps in the above method embodiments, and therefore, the description of each unit module is omitted here, and please refer to the description of the corresponding steps above in detail.
The embodiment of the present invention further provides an infrared thermal imaging based blade detection apparatus, which includes a processor, a memory, and a computer program stored in the memory, where the computer program is executable by the processor to implement the infrared thermal imaging based blade detection method according to the above embodiment.
The blade detection device based on infrared thermal imaging can include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of an infrared thermography-based blade inspection device and does not constitute a limitation of an infrared thermography-based blade inspection device, and may include more or fewer components than shown, or combine certain components, or different components, for example, the infrared thermography-based blade inspection device may also include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the control center of the infrared thermal imaging based blade sensing apparatus utilizes various interfaces and wires to connect the various parts of the entire infrared thermal imaging based blade sensing apparatus.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the infrared thermal imaging-based blade detection apparatus by executing or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein the unit integrated with the blade detecting device based on infrared thermal imaging can be stored in a computer readable storage medium if the unit is realized in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiments in the above embodiments can be further combined or replaced, and the embodiments are only used for describing the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention, and various changes and modifications made to the technical solution of the present invention by those skilled in the art without departing from the design idea of the present invention belong to the protection scope of the present invention.
Claims (10)
1. A blade detection method based on infrared thermal imaging is characterized by comprising the following steps:
acquiring a thermal imaging image of the surface of the fan blade;
processing the thermal imaging image to obtain a first image set;
calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model;
and detecting the image to be detected according to the variable linear regression feature model so as to obtain the defect information of the fan blade.
2. The method according to claim 1, wherein the step of processing the images to obtain a first image set comprises:
denoising the image through a mean value filter, and then carrying out graying processing to obtain a grayscale image set;
and performing characteristic scaling on the gray level image set to a preset range to obtain a first image set.
3. The blade detection method based on infrared thermal imaging as claimed in claim 2, wherein the step of performing graying processing comprises:
and carrying out graying processing on the image by a weighted average method.
4. The blade detection method based on infrared thermal imaging as claimed in claim 3, wherein the step of graying the image by weighted average method comprises:
and performing weighted average calculation on the RGB three components by different weights according to the f (i, j) ═ 0.30R (i, j) +0.59G (i, j) +0.11B (i, j), wherein i represents the ith row and j represents the jth column.
5. The method according to claim 2, wherein the step of scaling the gray image set to a preset range comprises:
according to
And performing characteristic scaling on the gray level image set to a preset range.
6. The method as claimed in claim 1, wherein the step of calculating the defect feature of the first image set by using a gradient descent algorithm to obtain a multivariate linear regression feature model comprises the steps of:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and the number of the first and second groups,
gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
7. An infrared thermal imaging-based blade detection device, characterized in that the device comprises:
the acquiring unit is used for acquiring a thermal imaging image of the surface of the fan blade;
the processing unit is used for processing the thermal imaging image to obtain a first image set;
the calculation unit is used for calculating the defect characteristics of the first image set by adopting a gradient descent algorithm to obtain a multivariate linear regression characteristic model;
and the detection unit is used for detecting the image to be detected according to the variable linear regression feature model so as to acquire the defect information of the fan blade.
8. The blade detection apparatus based on infrared thermal imaging as claimed in claim 7, wherein the processing unit further comprises:
the first processing unit is used for carrying out graying processing after the image is denoised by the mean value filter to obtain a grayscale image set;
and the second processing unit is used for carrying out characteristic scaling on the gray level image set to a preset range to obtain a first image set.
9. The blade detection device based on infrared thermal imaging according to claim 7, wherein the computing unit specifically includes:
according to the conditional probability: h isθ(x) Obtaining a probability P (y 1| x; Θ) to determine a defect type classification of the fan blade, further comprising:
according to logistic regression:
wherein z ═ ΘTX，
Thus determining hθ(x)；
Gradient descent according to logistic regression:
and (c) and (d).
Gradient descent according to addition of regularization term:
thus determining theta;
wherein, in the above formula, h (x) represents a function h related to x, g (z) represents a function g related to z, e, P, x and T represent mathematical operation signs, θ represents weight, α and λ represent hyper-parameters, m represents sample number, Labels represents true value of sample label, i represents ith row, and j represents jth column.
10. Blade detection device based on infrared thermography, comprising a processor, a memory and a computer program stored in said memory, said computer program being executable by said processor to implement a method for blade detection based on infrared thermography according to any of claims 1 to 6.
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Application publication date: 20210416 |