Boiler anti-wear and explosion-proof detection method

Abstract

The present invention discloses a method for detecting wear and explosion prevention in boilers, comprising: S1: acquiring historical maintenance images of the boiler interior, identifying the images, and determining potential defect areas; S2: acquiring historical thickness data at the locations of the potential defect areas and establishing a thickness prediction model; S3: using the thickness prediction model in real time to estimate the thickness of the potential defect areas, obtaining an estimated thickness, and issuing an alarm signal when the estimated thickness falls below a predetermined threshold. This method can reduce the likelihood of missed detections and lower the cost of wear and explosion prevention inspections.

Description

Technical Field

[0001] The present invention relates to the technical field related to boiler detection, and more specifically, to a method for detecting boiler wear and explosion prevention. Background Art

[0002] Boilers experience wear and tear during use, causing the thickness of the boiler walls to decrease. If not promptly detected and addressed, tube bursts can occur. Currently, boiler wear and explosion prevention inspections are primarily manual, using ultrasonic thickness gauges to measure the thickness of various locations on the boiler walls, as demonstrated in patent application number 2011101362646. This method is costly, relies on the inspector's experience and commitment, and carries the risk of missed inspections. Therefore, a technical solution is needed to overcome these limitations. Summary of the Invention

[0003] An object of the present invention is to provide a boiler anti-wear and explosion-proof detection method, which can reduce the possibility of missed detection and reduce the cost of explosion-proof inspection.

[0004] In order to achieve these objects and other advantages of the present invention, according to one aspect of the present invention, the present invention provides a boiler anti-wear and explosion-proof detection method, including: S1: obtaining historical maintenance images inside the boiler, identifying the historical maintenance images, and obtaining potential defect areas; S2: obtaining historical thickness data of the location of the potential defect area, and establishing a thickness prediction model; S3: using the thickness prediction model in real time to infer the thickness of the potential defect area to obtain an estimated thickness, and issuing an alarm signal when the estimated thickness is less than a predetermined threshold.

[0005] Furthermore, the method further includes: after the alarm signal is issued, detecting the actual thickness of the potential defect area and comparing it with the estimated thickness; if they are inconsistent, correcting the thickness prediction model.

[0006] Furthermore, in S1, the historical maintenance image is divided into a plurality of blocks, the blocks are input into a pre-trained identification model to determine whether the blocks have defects, and the area formed by the connected blocks is used as the potential defect area.

[0007] Furthermore, the identification model is obtained by training a convolutional neural network.

[0008] Furthermore, a three-dimensional model is constructed for the boiler based on the design information, and the blocks occupied by the potential defect area are mapped to the three-dimensional model based on the reference points pre-selected on the boiler and the pixel distance between each block and the reference point, thereby determining the position of the potential defect area on the boiler.

[0009] Furthermore, the historical maintenance images are obtained by inserting an image acquisition device into the boiler during the maintenance period.

[0010] Furthermore, the historical thickness data is measured by an ultrasonic thickness gauge at the time of maintenance.

[0011] Furthermore, the thickness data at the T-3 maintenance time, the T-2 maintenance time, and the T-1 maintenance time, the boiler operating parameters, the shape parameters, and the position parameters are used as input, and the thickness data at the T maintenance time is used as output to train a long short-term memory network to obtain the thickness prediction model; if the actual thickness is inconsistent with the estimated thickness, the shape parameters and the position parameters are adjusted, and the long short-term memory network is retrained to obtain the revised thickness prediction model.

[0012] The present invention has at least the following beneficial effects:

[0013] The present invention obtains historical maintenance images of the inside of the boiler, identifies the historical maintenance images, obtains potential defect areas, obtains historical thickness data of the locations of the potential defect areas, and establishes a thickness prediction model. The thickness prediction model is used to infer the thickness of the potential defect areas in real time to obtain the estimated thickness, and an alarm signal is issued when the estimated thickness is less than a predetermined threshold. That is, the present invention uses historical maintenance images to identify potential defect areas that require key observation, uses the thickness data of the potential defect areas to establish a model, predicts the thickness at future times, and provides key inspection locations and inspection times for anti-wear and explosion prevention work planning, thereby improving inspection efficiency and reducing inspection costs.

[0014] Other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of an embodiment of the present application. DETAILED DESCRIPTION

[0016] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0017] It should be understood that terms such as "having," "comprising," and "including" used in the embodiments of this application do not exclude the presence or addition of one or more other elements or combinations thereof. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are intended only to explain the relative positional relationships and movement of components in a specific posture. If the specific posture changes, the directional indications will also change accordingly. When an element is referred to as being "fixed to" or "disposed on" another element, it can be directly on the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element through an intervening element. References to "first," "second," etc. in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features designated as "first" or "second" may explicitly or implicitly include at least one of such features.

[0018] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in this field can implement it. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0019] like Figure 1 As shown, the embodiment of the present application provides a boiler anti-wear and explosion-proof detection method, comprising:

[0020] S1: Acquire historical maintenance images of the boiler interior, identify the historical maintenance images, and obtain potential defect areas;

[0021] For example, the boiler is scheduled for maintenance once a year, and maintenance may also be carried out during temporary shutdowns. During these maintenance periods, an image acquisition device can be used to capture images of the boiler interior, i.e., historical maintenance images.

[0022] Exemplarily, historical maintenance images are obtained by extending an image acquisition device into the boiler during maintenance, and moving and rotating the device;

[0023] For example, using image recognition technology, potential defect areas are identified based on the color and texture features of the defects;

[0024] It should be noted that the potential defect area in this step is constantly changing, and the inspection image obtained in each inspection is identified and the potential defect area is increased or decreased;

[0025] S2: Obtain historical thickness data of the location of the potential defect area and establish a thickness prediction model;

[0026] For example, the historical thickness data is measured at the time of maintenance using an ultrasonic thickness gauge;

[0027] For example, after the potential defect area is obtained in S1, it is projected onto the boiler, so that thickness measurement is performed at the corresponding position on the boiler to obtain thickness data;

[0028] For example, a thickness prediction model is constructed based on regression methods and machine learning methods using historical thickness data;

[0029] Exemplarily, historical thickness data is obtained for each potential defect area, and a thickness prediction model is established to monitor each potential defect area;

[0030] S3: In real time, the thickness of the potential defect area is estimated using the thickness prediction model to obtain an estimated thickness, and when the estimated thickness is less than a predetermined threshold, an alarm signal is issued to prompt the user to perform anti-wear and explosion-proof testing;

[0031] This step uses a thickness prediction model to predict the boiler wall thickness at multiple future moments to obtain an estimated thickness. When the estimated thickness is less than a predetermined threshold, an alarm signal is issued to remind the user to pay attention to potential defect areas and perform maintenance at the appropriate time. The predetermined threshold is determined based on experience or statistical data.

[0032] It can be seen that this embodiment uses historical maintenance images to identify most of the potential defect areas that need to be observed, thereby reducing the possibility of missed defects. The thickness data of the potential defect areas are used to establish a thickness prediction model to predict the thickness of each potential defect area at a future moment. This provides key locations and inspection opportunities for boiler anti-wear and explosion prevention work planning, reduces blind inspections, improves inspection efficiency, and reduces inspection costs.

[0033] In another embodiment, the method further comprises: after issuing the alarm signal, detecting the actual thickness of the potential defect area and comparing it with the estimated thickness, and if there is a discrepancy, revising the thickness prediction model;

[0034] In this embodiment, the inconsistency between the actual thickness and the estimated thickness means that the error between the two is greater than 15%. At this time, the thickness prediction model has low accuracy and needs to be corrected to reduce the error in the subsequent estimated thickness and improve the accuracy of the alarm.

[0035] In another embodiment, in S1, the historical inspection image is divided into a plurality of blocks, the blocks are input into a pre-trained identification model to determine whether the blocks have defects, and the area consisting of the connected blocks is used as the potential defect area;

[0036] For example, the block should completely cover the entire boiler inner wall image, and the size of the block is determined based on experience, as long as it can identify valid information;

[0037] For example, the identification model is trained by a convolutional neural network. There are obvious differences between normal blocks and defective blocks in color and texture, and the convolutional neural network can be used to better identify them.

[0038] For example, a labeled image of the inner wall of a boiler is obtained, a training set and a test set are established, the training set is input into a convolutional neural network for training, and the test set is used for verification to obtain an identification model;

[0039] For example, adjacent blocks identified as defects are connected to each other to remember potential defect areas, and scattered single blocks are generally not considered as potential defect areas.

[0040] In another embodiment, a three-dimensional model is constructed for the boiler based on the design information, and the blocks occupied by the potential defect area are mapped to the three-dimensional model based on pre-selected reference points on the boiler and the pixel distances between each block and the reference points, thereby determining the location of the potential defect area on the boiler;

[0041] For example, a three-dimensional model is constructed using the design dimensions and design drawings of the boiler;

[0042] For example, the three-dimensional model is placed in a three-dimensional coordinate system, the coordinates of the physical boundary of the boiler are determined, and the boundary coordinates of the potential defect area are determined based on the distance between each block of the potential defect area and the physical boundary of the boiler. Then, they are mapped to the three-dimensional model to intuitively display the potential defect area, and it is also convenient for users to measure the boiler wall thickness based on the displayed area.

[0043] In another embodiment, the thickness data at the T-3 maintenance time, the T-2 maintenance time, and the T-1 maintenance time, boiler operating parameters, shape parameters, and position parameters are used as input, and the thickness data at the T maintenance time is used as output to train a long short-term memory network (LSTM) to obtain the thickness prediction model; if the actual thickness is inconsistent with the estimated thickness, the shape parameters and position parameters are adjusted, and the long short-term memory network is retrained to obtain a revised thickness prediction model;

[0044] This embodiment proposes a preferred solution for the thickness prediction model, which is to use a long short-term memory network to build, collect maintenance records of multiple similar boilers, and obtain thickness data at the time of maintenance. T-3, T-2, T-1, and T are the maintenance times respectively, and establish a training set. The training set includes thickness data at each maintenance time, boiler operating parameters, shape parameters, and position parameters. Boiler operating parameters include coal quantity, coal sulfur content, air supply volume, furnace temperature, wind pressure, etc. Shape parameters and position parameters are used to consider the influence of the shape and position of potential defect areas on thickness changes, such as the thinning speed of water-cooled walls and superheaters compared to that of boilers. Other positions are faster. The shape parameters are the area and shape of the potential defect area (determined by the number and distribution of blocks). Generally, the larger the potential defect area, the faster the corrosion rate. The shape parameters and position parameters are given initial values in advance based on experience, and trained together with the thickness data and boiler operating parameters. When the actual thickness is inconsistent with the estimated thickness, adjustments are made to improve the accuracy of the thickness prediction model. After the thickness prediction model is established, the latest detected thickness data is input to estimate the thickness at multiple moments in the future. When it is less than the predetermined threshold, an alarm signal is issued, providing a reference for anti-wear and explosion prevention work planning.

[0045] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for detecting boiler wear and explosion prevention, characterized in that: include: S1: Acquire historical maintenance images of the boiler interior, identify the historical maintenance images, and obtain potential defect areas; In S1, the historical inspection image is divided into a plurality of blocks, the blocks are input into a pre-trained identification model to determine whether the blocks have defects, and the area formed by the connected blocks is regarded as the potential defect area; S2: Obtaining historical thickness data of the location of the potential defect area and establishing a thickness prediction model; constructing a three-dimensional model of the boiler based on the design information, and mapping the blocks occupied by the potential defect area to the three-dimensional model based on pre-selected reference points on the boiler and the pixel distances between each block and the reference points, thereby determining the location of the potential defect area on the boiler; The thickness data at the time of maintenance T-3, T-2 and T-1, boiler operating parameters, shape parameters and position parameters are used as input, and the thickness data at the time of maintenance T is used as output to train a long short-term memory network to obtain the thickness prediction model; wherein, maintenance records of multiple similar boilers are collected to obtain thickness data at the time of maintenance, and a training set is established, which includes thickness data at each maintenance time, boiler operating parameters and shape parameters, and position parameters. The boiler operating parameters include coal quantity, coal sulfur content, air supply volume, furnace temperature and air pressure. The shape parameters and position parameters are used to distinguish the influence of the shape and position of the potential defect area on the thickness change. The position includes water-cooled wall and superheater. The shape parameter is the area and shape of the potential defect area. The shape parameter and position parameter are given initial values in advance based on experience and trained together with the thickness data and boiler operating parameters; S3: Calculate the thickness of the potential defect area in real time using the thickness prediction model to obtain an estimated thickness, and issue an alarm signal when the estimated thickness is less than a predetermined threshold.

2. The boiler anti-wear and explosion-proof detection method according to claim 1, characterized in that: Also includes: After the alarm signal is issued, the actual thickness of the potential defect area is detected and compared with the estimated thickness. If the actual thickness is inconsistent with the estimated thickness, the shape parameters and position parameters are adjusted, and the long short-term memory network is retrained to obtain the revised thickness prediction model.

3. The boiler anti-wear and explosion-proof detection method according to claim 1, characterized in that: The identification model is obtained by training a convolutional neural network.

4. The boiler anti-wear and explosion-proof detection method according to claim 1, characterized in that: The historical maintenance images are obtained by inserting an image acquisition device into the boiler during maintenance.

5. The boiler anti-wear and explosion-proof detection method according to claim 1, characterized in that: The historical thickness data is measured by an ultrasonic thickness gauge at the time of maintenance.

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