Boiler inner wall defect identification method based on deep learning and image processing technology
Through the method based on deep learning and image processing technology, a convolutional neural network model is constructed, which solves the problem of subjectivity and low efficiency of traditional boiler inner wall defect recognition methods, real-time monitoring and early warning of boiler inner walls is achieved, and the accuracy of recognition and system adaptability are improved.
Patent Information
- Application Number
- CN202411826347.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-12
- Publication Date
- 2025-04-11
AI Technical Summary
The traditional boiler inner wall defect identification method relies on manual experience, has problems of subjectivity and low efficiency, cannot provide real-time monitoring, and is dangerous in high-temperature and high-pressure environments.
Using methods based on deep learning and image processing technology, a convolutional neural network model is constructed through data acquisition, preprocessing, feature extraction, model design and training to realize real-time identification and monitoring of boiler inner wall defects.
It improves the accurate identification ability of inner wall defects, realizes real-time monitoring and early warning of the inner wall of the boiler, reduces false alarms and missed reports, adapts to various environmental conditions, and improves the stability and adaptability of the system.
Smart Images
Figure CN119810029A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of boiler inner wall detection, and specifically to a boiler inner wall defect recognition method based on deep learning and image processing technology. Background Art
[0002] Boiler inner wall defect identification refers to the process of using various technical means to detect and identify possible defects on the boiler inner wall. These defects may include corrosion, cracks, deformation and other problems, which are crucial to ensuring the safe operation of the boiler and extending the life of the equipment.
[0003] Traditional methods for identifying boiler inner wall defects have some defects. Traditional methods usually rely on the operator's experience and intuition to identify inner wall defects, which may lead to subjectivity and inconsistency. Manual inspection requires a lot of time and human resources, which is inefficient, especially for large boiler systems.
[0004] Traditional methods usually involve periodic or periodic inspections, which cannot provide real-time status monitoring of the inner wall and may miss some sudden problems. Under some special environmental conditions, such as high temperature and high pressure, manual inspections may be dangerous or simply impossible. Summary of the invention
[0005] The purpose of the present invention is to provide a method for identifying boiler inner wall defects based on deep learning and image processing technology to solve the problems raised in the above background technology.
[0006] To achieve the above object, the present invention provides the following technical solutions:
[0007] A method for identifying boiler inner wall defects based on deep learning and image processing technology comprises the following steps:
[0008] Step S1, data collection and preparation: collect a large amount of boiler inner wall image data, including normal state and different types of defects, annotate the image data, and mark the defective area in each image;
[0009] Step S2, data preprocessing: preprocessing the collected image data, including image denoising, size standardization, grayscale conversion, and using data enhancement techniques, including rotation, flipping, and scaling, to increase the diversity of training data;
[0010] Step S3, feature extraction: use the convolutional neural network deep learning architecture to extract features from the preprocessed image, and learn local and global features in the image through multi-layer convolution and pooling operations;
[0011] Step S4, model design: using a convolutional neural network structure to build a deep learning model to better capture the spatiotemporal information of the image;
[0012] Step S5, model training: divide the prepared data set into a training set and a validation set, use the labeled data to train the deep learning model, and optimize the model parameters to minimize the loss function;
[0013] Step S6, model evaluation: Use an independent test data set to evaluate the performance of the trained model, including accuracy, recall, and precision indicators, and consider cross-validation to verify the robustness of the model;
[0014] Step S7, post-processing: post-processing the model output, which may include filtering and threshold processing to further improve recognition accuracy;
[0015] Step S8, real-time monitoring system integration: embed the trained model into the real-time monitoring system to achieve real-time monitoring of the inner wall of the boiler, integrate sensors and data acquisition equipment, and ensure that the system can respond to defects in a timely manner;
[0016] Step S9, continuous optimization: Update the model regularly to adapt to new data and environmental changes, collect feedback from actual applications, continuously optimize algorithms and models, and improve the stability and accuracy of the system.
[0017] Furthermore, in step S1, a large amount of boiler inner wall image data is collected to ensure that the data contains normal conditions and various types of defects. The diversity and sufficiency of the data are crucial to the model performance. The images are manually annotated and the defective areas in each image are marked so as to train the model to learn the characteristics of the defects.
[0018] Furthermore, in step S2, the collected image data is preprocessed, including denoising, size standardization and grayscale conversion. Denoising helps to eliminate noise in the image, size standardization ensures that the input images have the same size, grayscale conversion simplifies the processing process, and rotation, flipping and scaling data enhancement techniques are applied to increase the diversity of training data and improve the generalization ability of the model.
[0019] The formula for image denoising is as follows:
[0020] [I_{\text{denoised}}=\text{DenoisingAlgorithm}(I)]
[0021] Wherein, the image I is denoised using a Gaussian filter or a median filter algorithm;
[0022] The formula to apply the rotation is as follows:
[0023] [I_{\text{rotated}}=\text{Rotate}(I,\theta)]
[0024] Among them, data enhancement is achieved by rotating the image I by an angle theta.
[0025] Furthermore, in step S3, a convolutional neural network deep learning architecture is used for feature extraction, the convolution layer identifies local features in the image through convolution operations, and the pooling layer reduces the data dimension, which helps to learn global features;
[0026] The formula for the convolution operation is as follows:
[0027] [C_{i,j}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{i+m,j+n}\cdot K_{m,n}]
[0028] Among them, in the convolution layer, the input image I is convolved by the convolution kernel k;
[0029] The formula for the pooling operation is as follows:
[0030] [P_{i,j}=\text{PoolingFunction}(I_{i:i+p-1,j:j+p-1})]
[0031] Among them, in the pooling layer, the pooling function is used to downsample the image I.
[0032] Furthermore, in step S4, a deep learning model is constructed and an appropriate network structure is selected. The design of the model should take into account the complexity of the defects on the inner wall of the boiler. A convolutional neural network structure can be used to ensure that the model can effectively learn image features.
[0033] Furthermore, in step S5, the prepared data set is divided into a training set and a validation set, and the model parameters are trained to minimize the loss function through a back propagation algorithm and an optimizer, with the goal of enabling the model to accurately classify normal states and various defects;
[0034] The formula of the loss function is as follows:
[0035] [\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}\left(y_i\cdot\log(\hat{y}_i)+(1-y_
[0036] i)\cdot\log(1-\hat{y}_i)\right)]
[0037] Among them, the cross entropy loss function is used to measure the difference between the model output and the actual label y.
[0038] Furthermore, in step S6, an independent test data set is used to evaluate the model performance, and the accuracy, recall, and precision indicators are calculated to evaluate the classification accuracy and generalization ability of the model. The performance consistency of the model on different sub-data sets is ensured through cross-validation to increase the reliability of the evaluation.
[0039] The formula for the gradient descent update rule is as follows:
[0040] [\theta=\theta-\alpha\cdot\frac{\partial\mathcal{L}}{\partial\theta}]
[0041] Among them, the gradient descent method is used to update the model parameters theta, and alpha is the learning rate.
[0042] Furthermore, in step S7, the model output is post-processed to further improve the recognition accuracy, including filtering and threshold processing techniques to remove noise in the output and ensure that the result is consistent with the actual situation;
[0043] The formula for threshold processing is as follows:
[0044] [I_{\text{thresheld}}(x,y)=\begin{cases}1,&\text{if}I(x,
[0045] y)>\text{Threshold}\0,&\text{otherwise}\end{cases}]
[0046] Among them, the threshold is used to binarize the model output and remove noise.
[0047] Furthermore, in step S8, the trained model is embedded into the real-time monitoring system, and the image data of the inner wall of the boiler is collected in real time. Real-time recognition and monitoring are performed through the model, and sensors and data acquisition equipment are integrated to ensure that the system can respond to defects and monitor them in a timely manner.
[0048] Furthermore, in step S9, the model is updated regularly to adapt to new data and environmental changes, feedback from actual applications is collected, and the algorithm and model are continuously optimized through feedback information to improve the stability and accuracy of the system.
[0049] Compared with the prior art, the present invention has the following beneficial effects:
[0050] 1. The deep learning model in the present invention can learn complex image features, thereby improving the ability to accurately identify inner wall defects. Through a large amount of labeled data and training, the model can better understand the normal state and various types of defects, improve the accuracy and reliability of recognition, reduce false positives and false negatives, and the system can work robustly under a wider range of conditions.
[0051] 2. The present invention embeds the deep learning model into the real-time monitoring system, which can monitor the inner wall of the boiler in real time. The sensors and data acquisition equipment can capture image data in time, and the model can make judgments quickly, realizing real-time monitoring and early warning of inner wall defects, so that the system can take measures quickly when problems are found, reducing equipment damage or safety hazards caused by delays.
[0052] 3. The design of the present invention includes the steps of regularly updating the model and collecting feedback information from actual applications. By continuously optimizing the algorithm and model, the system can adapt to new data and environmental changes, improve the system's continuous optimization ability and adaptability, and ensure that a high level of performance is maintained in long-term operation. As time goes by, the system can continue to learn and adapt to new conditions and maintain efficient operation. BRIEF DESCRIPTION OF THE DRAWINGS
[0053] Figure 1 This is a schematic flow chart of a boiler inner wall defect recognition method based on deep learning and image processing technology in the present invention. DETAILED DESCRIPTION
[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0055] See also Figure 1 , the present invention provides a technical solution:
[0056] A method for identifying boiler inner wall defects based on deep learning and image processing technology comprises the following steps:
[0057] Step S1, data collection and preparation: collect a large amount of boiler inner wall image data, including normal state and different types of defects, annotate the image data, and mark the defective area in each image;
[0058] In this embodiment, a large amount of boiler inner wall image data is collected to ensure that the data contains normal conditions and various types of defects. The diversity and sufficiency of the data are crucial to the model performance. The images are manually annotated and the defective areas in each image are marked so as to train the model to learn the characteristics of the defects.
[0059] Step S2, data preprocessing: preprocessing the collected image data, including image denoising, size standardization, grayscale conversion, and using data enhancement techniques, including rotation, flipping, and scaling, to increase the diversity of training data;
[0060] In this embodiment, the collected image data is preprocessed, including denoising, size standardization and grayscale. Denoising helps to eliminate noise in the image, size standardization ensures that the input images have the same size, grayscale simplifies the processing process, and rotation, flipping, and scaling data enhancement techniques are applied to increase the diversity of training data and improve the generalization ability of the model.
[0061] The formula for image denoising is as follows:
[0062] [I_{\text{denoised}}=\text{DenoisingAlgorithm}(I)]
[0063] Wherein, the image I is denoised using a Gaussian filter or a median filter algorithm;
[0064] The formula to apply the rotation is as follows:
[0065] [I_{\text{rotated}}=\text{Rotate}(I,\theta)]
[0066] Among them, data enhancement is achieved by rotating the image I by an angle theta.
[0067] Step S3, feature extraction: use the convolutional neural network deep learning architecture to extract features from the preprocessed image, and learn local and global features in the image through multi-layer convolution and pooling operations;
[0068] In this embodiment, a convolutional neural network deep learning architecture is used for feature extraction. The convolution layer identifies local features in the image through convolution operations, and the pooling layer reduces the data dimension, which helps to learn global features.
[0069] The formula for the convolution operation is as follows:
[0070] [C_{i,j}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{i+m,j+n}\cdot K_{m,n}]
[0071] Among them, in the convolution layer, the input image I is convolved by the convolution kernel k;
[0072] The formula for the pooling operation is as follows:
[0073] [P_{i,j}=\text{PoolingFunction}(I_{i:i+p-1,j:j+p-1})]
[0074] Among them, in the pooling layer, the pooling function is used to downsample the image I.
[0075] Step S4, model design: using a convolutional neural network structure to build a deep learning model to better capture the spatiotemporal information of the image;
[0076] In this embodiment, a deep learning model is constructed and an appropriate network structure is selected. The design of the model should take into account the complexity of the defects on the inner wall of the boiler. A convolutional neural network structure can be used to ensure that the model can effectively learn image features.
[0077] Step S5, model training: divide the prepared data set into a training set and a validation set, use the labeled data to train the deep learning model, and optimize the model parameters to minimize the loss function;
[0078] In this embodiment, the prepared data set is divided into a training set and a validation set, and the model parameters are trained to minimize the loss function through a back-propagation algorithm and an optimizer, with the goal of enabling the model to accurately classify normal states and various defects;
[0079] The formula of the loss function is as follows:
[0080] [\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}\left(y_i\cdot\log(\hat{y}_i)+(1-y_
[0081] i)\cdot\log(1-\hat{y}_i)\right)]
[0082] Among them, the cross entropy loss function is used to measure the difference between the model output and the actual label y.
[0083] Step S6, model evaluation: Use an independent test data set to evaluate the performance of the trained model, including accuracy, recall, and precision indicators, and consider cross-validation to verify the robustness of the model;
[0084] In this embodiment, an independent test data set is used to evaluate the model performance, and the accuracy, recall, and precision indicators are calculated to evaluate the classification accuracy and generalization ability of the model. The performance consistency of the model on different sub-data sets is ensured through cross-validation to increase the reliability of the evaluation.
[0085] The formula for the gradient descent update rule is as follows:
[0086] [\theta=\theta-\alpha\cdot\frac{\partial\mathcal{L}}{\partial\theta}]
[0087] Among them, the gradient descent method is used to update the model parameters theta, and alpha is the learning rate.
[0088] Step S7, post-processing: post-processing the model output, which may include filtering and threshold processing to further improve recognition accuracy;
[0089] In this embodiment, the model output is post-processed to further improve the recognition accuracy, including filtering and threshold processing techniques to remove noise in the output and ensure that the results are consistent with the actual situation;
[0090] The formula for threshold processing is as follows:
[0091] [I_{\text{thresheld}}(x,y)=\begin{cases}1,&\text{if}I(x,
[0092] y)>\text{Threshold}\0,&\text{otherwise}\end{cases}]
[0093] Among them, the threshold is used to binarize the model output and remove noise.
[0094] Step S8, real-time monitoring system integration: embed the trained model into the real-time monitoring system to achieve real-time monitoring of the inner wall of the boiler, integrate sensors and data acquisition equipment, and ensure that the system can respond to defects in a timely manner;
[0095] In this embodiment, the trained model is embedded in the real-time monitoring system, and the image data of the inner wall of the boiler is collected in real time. Real-time recognition and monitoring are performed through the model, and sensors and data acquisition equipment are integrated to ensure that the system can respond to defects and monitor them in a timely manner.
[0096] Step S9, continuous optimization: regularly update the model to adapt to new data and environmental changes, collect feedback from actual applications, continuously optimize the algorithm and model, and improve the stability and accuracy of the system;
[0097] In this embodiment, the model is updated regularly to adapt to new data and environmental changes, feedback from actual applications is collected, and the algorithm and model are continuously optimized through feedback information to improve the stability and accuracy of the system.
[0098] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.
[0099] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for identifying boiler inner wall defects based on deep learning and image processing technology, characterized in that: The following steps are involved: Step S1, data collection and preparation: collect a large amount of boiler inner wall image data, including normal state and different types of defects, annotate the image data, and mark the defective area in each image; Step S2, data preprocessing: preprocessing the collected image data, including image denoising, size standardization, grayscale conversion, and using data enhancement techniques, including rotation, flipping, and scaling, to increase the diversity of training data; Step S3, feature extraction: use the convolutional neural network deep learning architecture to extract features from the preprocessed image, and learn local and global features in the image through multi-layer convolution and pooling operations; Step S4, model design: using a convolutional neural network structure to build a deep learning model to better capture the spatiotemporal information of the image; Step S5, model training: divide the prepared data set into a training set and a validation set, use the labeled data to train the deep learning model, and optimize the model parameters to minimize the loss function; Step S6, model evaluation: Use an independent test data set to evaluate the performance of the trained model, including accuracy, recall, and precision indicators, and consider cross-validation to verify the robustness of the model; Step S7, post-processing: post-processing the model output, which may include filtering and threshold processing to further improve recognition accuracy; Step S8, real-time monitoring system integration: embed the trained model into the real-time monitoring system to achieve real-time monitoring of the inner wall of the boiler, integrate sensors and data acquisition equipment, and ensure that the system can respond to defects in a timely manner; Step S9, continuous optimization: Update the model regularly to adapt to new data and environmental changes, collect feedback from actual applications, continuously optimize algorithms and models, and improve the stability and accuracy of the system.
2. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1 is characterized in that: In step S1, a large amount of boiler inner wall image data is collected to ensure that the data contains normal conditions and various types of defects. The diversity and sufficiency of the data are crucial to the model performance. The images are manually annotated to mark the defective areas in each image so as to train the model to learn the characteristics of the defects.
3. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1 is characterized in that: In the step S2, the collected image data is preprocessed, including denoising, size standardization and grayscale conversion. Denoising helps to eliminate noise in the image, size standardization ensures that the input images have the same size, grayscale conversion simplifies the processing process, and rotation, flipping and scaling data enhancement techniques are applied to increase the diversity of training data and improve the generalization ability of the model. The formula for image denoising is as follows: [I_{\text{denoised}}=\text{DenoisingAlgorithm}(I)] Wherein, the image I is denoised using a Gaussian filter or a median filter algorithm; The formula to apply the rotation is as follows: [I_{\text{rotated}}=\text{Rotate}(I,\theta)] Among them, data enhancement is achieved by rotating the image I by an angle theta.
4. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1 is characterized in that: In step S3, a convolutional neural network deep learning architecture is used for feature extraction. The convolution layer identifies local features in the image through convolution operations, and the pooling layer reduces the data dimension, which helps to learn global features. The formula for the convolution operation is as follows: [C_{i,j}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{i+m,j+n}\cdot K_{m,n}] In the convolution layer, the input image I is convolved by the convolution kernel k; The formula for the pooling operation is as follows: [P_{i,j}=\text{PoolingFunction}(I_{i:i+p-1,j:j+p-1})] Among them, in the pooling layer, the pooling function is used to downsample the image I.
5. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S4, a deep learning model is constructed and an appropriate network structure is selected. The design of the model should take into account the complexity of the defects on the inner wall of the boiler. A convolutional neural network structure can be used to ensure that the model can effectively learn image features.
6. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S5, the prepared data set is divided into a training set and a validation set, and the model parameters are trained to minimize the loss function through a back propagation algorithm and an optimizer, with the goal of enabling the model to accurately classify normal states and various defects; The formula of the loss function is as follows: [\mathcal{L}=-\frac{1}{N}\sum_{i=1}^{N}\left(y_i\cdot\log(\hat{y}_i)+(1-y_i)\cdot\log(1-\hat{y}_i)\right)] Among them, the cross entropy loss function is used to measure the difference between the model output and the actual label y.
7. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S6, an independent test data set is used to evaluate the model performance, and the accuracy, recall, and precision indicators are calculated to evaluate the classification accuracy and generalization ability of the model. The performance consistency of the model on different sub-data sets is ensured through cross-validation to increase the reliability of the evaluation; The formula for the gradient descent update rule is as follows: [\theta=\theta-\alpha\cdot\frac{\partial\mathcal{L}}{\partial\theta}] Among them, the gradient descent method is used to update the model parameters theta, and alpha is the learning rate.
8. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S7, the model output is post-processed to further improve the recognition accuracy, including filtering and threshold processing techniques to remove noise in the output and ensure that the result conforms to the actual situation; The formula for threshold processing is as follows: [I_{\text{thresheld}}(x,y)=\begin{cases}1,&\text{if}I(x, y)>\text{Threshold}\0,&\text{otherwise}\end{cases}] Among them, the threshold is used to binarize the model output and remove noise.
9. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S8, the trained model is embedded into the real-time monitoring system, and the image data of the inner wall of the boiler is collected in real time. Real-time recognition and monitoring are performed through the model, and sensors and data acquisition equipment are integrated to ensure that the system can respond to defects and monitor them in a timely manner.
10. The method for identifying boiler inner wall defects based on deep learning and image processing technology according to claim 1, characterized in that: In step S9, the model is updated regularly to adapt to new data and environmental changes, feedback from actual applications is collected, and the algorithm and model are continuously optimized through feedback information to improve the stability and accuracy of the system.