A special pump state real-time monitoring method based on image processing

Abstract

This invention discloses a real-time monitoring method for the status of special pumps based on image processing, belonging to the field of special pump monitoring technology. The method includes the following steps: acquiring surface images of the special pump to obtain surface image data; performing crack detection based on the surface image data to obtain crack location data; performing surface temperature analysis based on the crack location data to obtain temperature anomaly area location data; performing anomaly analysis based on the temperature anomaly area location data to obtain anomaly analysis results; and performing a health status assessment of the special pump based on the anomaly analysis results to obtain assessment results. This invention can simultaneously detect both micro-cracks and larger cracks, improving the comprehensive detection capability for cracks of different scales. It can effectively extract micro-cracks, eliminate noise interference, and improve crack detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of special pump monitoring technology, specifically to a method for real-time monitoring of the status of special pumps based on image processing. Background Technology

[0002] Specialty pumps, as crucial industrial equipment, play a vital role in many sectors such as chemical, energy, and oil and gas. Due to their operation under high pressure, high temperature, or harsh environments, specialty pumps are prone to various malfunctions, which can affect their stable operation, even leading to production stoppages and significant economic losses. Therefore, timely and effective monitoring of pump operating status is of paramount importance. With the development of intelligent technologies, real-time monitoring methods based on image processing have become an important direction for monitoring the health status of specialty pumps.

[0003] Existing crack detection technologies have shortcomings: most existing crack detection methods rely on simple edge detection and threshold segmentation techniques, which cannot effectively extract tiny cracks in complex backgrounds. Especially when there is noise or low image quality, false detections and false negatives are likely to occur, and they cannot simultaneously detect both large and micro cracks. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for real-time monitoring of the status of special pumps based on image processing, thereby solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for real-time monitoring of the status of a special pump based on image processing, comprising the following steps: S1. Acquisition of surface images of special pumps, obtaining surface image data of special pumps; S2. Crack detection is performed based on the surface image data of the special pump to obtain crack location data; S3. Perform surface temperature analysis based on crack location data to obtain location data of temperature anomaly areas; S4. Perform anomaly analysis based on the location data of the temperature anomaly area to obtain the anomaly analysis results; S5. Based on the anomaly analysis results, conduct a health status assessment of the special pump and obtain the assessment results.

[0006] To further optimize this technical solution, the crack detection in step S2 includes: Based on the obtained surface image data of the special pump, a crack detection model is used to extract edge information at different scales and process it in combination with adaptive morphological operations to separate cracks from background noise, thereby obtaining crack location data.

[0007] To further optimize this technical solution, the crack detection model includes: ; in: : The size of the image in the horizontal direction; : The size of the image in the vertical direction; : Image after morphological processing; : Morphological operations; : The merged edge image; : Control parameters for morphological operations.

[0008] To further optimize this technical solution, the fused edge image includes: ; in: The total number of scales; : Weighting coefficients at scale s; Edge image information at scale s; The fused edge image is obtained by weighted fusion based on the edge image information.

[0009] To further optimize this technical solution, the edge image information includes: ; in: Gradient calculation symbol; : Convolution operator; Original image; Gaussian filter kernel; Standard deviation of the Gaussian kernel; The original image is subjected to multi-scale Gaussian blurring and edge information is calculated to obtain edge image information at different scales.

[0010] To further optimize this technical solution, the surface temperature analysis in S3 includes: Based on the obtained crack location data, a temperature analysis model is used, combined with the thermal conductivity coefficient and the temperature deviation at the crack compared to the temperature without cracks, to conduct temperature analysis and obtain the location data of the temperature anomaly area.

[0011] To further optimize this technical solution, the temperature analysis model includes: ; in: Temperature anomaly at the crack location (x, y); The temperature difference between the cracked area and the area without cracks; : The thermal conductivity of the crack at location (x,y).

[0012] To further optimize this technical solution, the crack thermal conductivity coefficient includes: ; in: : Thermal conductivity at the crack; Thermal conductivity of normal material at crack-free locations; The magnitude of the thermal resistance of the medium inside the crack; The thermal resistance of a normal material without cracks; By combining the thermal resistance of the medium inside the crack with the thermal resistance of the normal material, and using the thermal conductivity coefficient of the material, a thermal resistance model is used to represent the thermal conductivity of the crack, thus obtaining the crack thermal conductivity coefficient.

[0013] To further optimize this technical solution, the internal thermal resistance of the crack includes: ; in: : The length of the crack; : Thermal conductivity coefficient of the medium inside the crack; : The width of the crack; The thermal resistance of the medium in the crack is calculated based on the length and width of the crack and the thermal conductivity of the medium in the crack.

[0014] To further optimize this technical solution, the special pump health status assessment in step S5 includes: Based on the anomaly analysis results, a health status assessment is conducted on the special pumps identified as having anomalies. Through failure mode analysis, the degree of equipment failure is determined, and the failure modes that cause the special pump failure are analyzed, providing a basis for whether the special pump needs maintenance or repair, and obtaining the assessment results of the special pump's health status.

[0015] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a method for real-time monitoring of the status of a special pump based on image processing as described in the first aspect of the present invention.

[0016] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a method for real-time monitoring of the status of a special pump based on image processing as described in the first aspect of the present invention.

[0017] Compared with the prior art, the present invention provides a method for real-time monitoring of the status of special pumps based on image processing, which has the following beneficial effects: This image processing-based real-time monitoring method for special pumps extracts edge information at multiple scales through a crack detection model, enabling simultaneous detection of both micro and large cracks. This enhances the comprehensive detection capability for cracks of different scales. Furthermore, the introduction of morphological manipulation effectively extracts micro cracks and eliminates noise interference, thereby improving the accuracy of crack detection. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a method for real-time monitoring of the status of special pumps based on image processing, as proposed in this invention. Figure 2 This is a flowchart illustrating the crack detection model of a special pump status real-time monitoring method based on image processing proposed in this invention. Figure 3 This is a flowchart illustrating the temperature analysis model of a special pump status real-time monitoring method based on image processing proposed in this invention. Figure 4 This is a flowchart illustrating the anomaly analysis model of a special pump status real-time monitoring method based on image processing proposed in this invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0023] Example 1: Reference Figures 1-4 This is the first embodiment of the present invention, which provides a method for real-time monitoring of the status of a special pump based on image processing, including the following steps: S1. Acquisition of surface images of special pumps, obtaining surface image data of special pumps.

[0024] In this embodiment, the acquisition of the surface image of the special pump includes: Traditional pump condition monitoring methods mainly rely on sensors to monitor parameters such as flow rate, pressure, and temperature. However, these methods have limitations in detecting pump surface defects (such as cracks, corrosion, and wear). Special pump surface image data, on the other hand, can provide real-time visual and temperature data of the pump surface. This not only detects surface problems but also provides sufficiently clear and detailed information for subsequent image processing. Therefore, it is necessary to use cameras to acquire images of the special pump surface.

[0025] The main purpose of this step is to capture real-time images of the special pump surface under different working conditions and environments (such as light, temperature, and humidity) using high-definition and infrared cameras, obtaining high-resolution image data of the pump body surface, thus providing raw data for subsequent image processing and analysis. Acquiring images of the special pump surface via cameras avoids physical contact with the sensors, reduces measurement errors caused by sensor damage or wear, increases the comprehensiveness of the detection, enables continuous monitoring of the pump's operating status, provides real-time feedback, and allows for constant monitoring of the pump's operational health.

[0026] The specific methods for acquiring surface images of special pumps in this step include: Camera selection: Choose a camera with high resolution, good image quality, and fast response speed to ensure that it can capture tiny cracks or damage on the surface of the special pump body, and the frame rate of the camera needs to be high enough to capture subtle changes in the pump during operation.

[0027] Camera placement: Based on the structure of the special pump, the positions of the cameras should be arranged reasonably. Multiple cameras should be installed at different angles of the pump (such as the side, top, and bottom) to ensure full coverage of the key parts of the pump body and avoid missing important information due to blind spots. When placing the cameras, the operating environment of the pump, such as the impact of vibration and temperature on the equipment, should be taken into account.

[0028] Lighting system configuration: Use an LED lighting system to supplement the illumination of the special pump body to avoid insufficient ambient light affecting image quality. The LED lights need to be placed in a suitable position to ensure uniform illumination and avoid strong shadows or light spots that affect image clarity.

[0029] Image Acquisition: The camera acquires images during the operation of the special pump. It is necessary to ensure the stability of the images during the acquisition process and avoid the impact of factors such as pump vibration on image quality. By adjusting the camera's shutter speed and exposure time, it can adapt to different operating environments (such as high temperature, high humidity, low light, etc.) to ensure the acquisition of clear image data.

[0030] S2. Crack detection is performed based on the surface image data of the special pump to obtain crack location data.

[0031] In this embodiment, the crack detection includes: In step S1, an image of the surface of the special pump body is acquired using a high-definition camera. The goal of this step is to identify the location of possible cracks and damaged areas on the surface of the pump body based on these images through crack detection.

[0032] Based on the obtained surface image data of the special pump, a crack detection model is used to extract edge information at different scales and process it in combination with adaptive morphological operations to separate cracks from background noise, thereby improving the accuracy of crack identification and obtaining crack location data.

[0033] Furthermore, the crack detection model includes: ; in: : The size of the image in the horizontal direction; : The size of the image in the vertical direction; : Image after morphological processing; Morphological manipulation, including operations such as expansion and erosion, can strengthen cracked areas and remove noise; The fused edge image is obtained by weighted fusion of edge images at multiple scales, which integrates edge information at different scales and includes various edge features from large cracks to small cracks. : Control parameters for morphological operations, used to adjust the intensity of morphological operations so that different crack regions can be treated differently according to local characteristics.

[0034] Furthermore, the fused edge image includes: ; in: The total number of scales; : Weighting coefficients at scale s. Different scales have different crack characteristics. The weighting coefficients are set according to the significance of the crack at each scale. Edge image information at scale s, used to capture edge information at that scale; The fused edge image is obtained by weighted fusion based on the edge image information.

[0035] Furthermore, the edge image information includes: ; in: Gradient calculation symbol, used to calculate the gradient of an image and identify image edges; The convolution operator is used to calculate the weighted sum of each pixel value and its neighboring pixel values ​​in an image, and to process certain features of the image, such as smoothing, sharpening, and edge extraction. The original image, the image after grayscale conversion, and the image after denoising are used for subsequent analysis; Gaussian filter kernel: used for Gaussian blur processing of images, smoothing images, removing small noise, and extracting features at different scales; The standard deviation of the Gaussian kernel is used to control the degree of blurring; different sizes correspond to different scales of blurring operations. The original image is subjected to multi-scale Gaussian blurring and edge information is calculated to obtain edge image information at different scales.

[0036] This model describes how to detect cracks based on edge information extracted from images of the surface of a special pump at different scales.

[0037] Traditional crack detection methods typically use sensors to detect parameters such as temperature and vibration on the pump body surface, but they cannot directly observe tiny cracks or surface damage, nor can they accurately locate the crack position. In contrast, this model extracts edge information at different scales and processes it using adaptive morphology technology. It can adapt to cracks of different sizes and dynamically adjust parameters according to the local features of the crack, improving adaptability and accurately locating the crack area, thus improving the precision and accuracy of crack detection.

[0038] The steps for using this model include: Edge extraction: on the original image Gaussian blurring at multiple scales is applied to obtain blurred images at different scales. Gradient calculation is then applied to each scale image to compute edge information, thus obtaining edge image information at different scales. ; Edge information fusion: edge image information at each scale Weighted and fused images are obtained. ; Crack location: For the fused edge image Image enhancement is achieved by using morphological operations, such as dilation. In this image, the grayscale value of the crack is more prominent than that of the surrounding normal area. The area in the image whose grayscale value is significantly different from that of other areas is the crack area. For example, the grayscale value of the crack is 1, while the grayscale value of the non-crack area is 0. This allows for the precise location of the crack area and the acquisition of crack location data.

[0039] S3. Perform surface temperature analysis based on crack location data to obtain location data of temperature anomaly areas.

[0040] In this embodiment, the surface temperature analysis includes: The location data of the crack is obtained from step S2. Cracks are usually closely related to overheating, fatigue or mechanical stress of the equipment. Therefore, by combining crack information with temperature data for comprehensive analysis, it is possible to more accurately detect whether the special pump has an abnormal temperature and provide early warning of possible equipment failure.

[0041] Based on the obtained crack location data, a temperature analysis model is used, combined with the thermal conductivity coefficient and the temperature deviation at the crack compared to the temperature without cracks, to perform temperature analysis, calculate the impact value of the crack on the temperature, and then compare it with the temperature deviation threshold to obtain the location data of the area where the impact value is greater than the threshold, i.e., the temperature anomaly area.

[0042] Furthermore, the temperature analysis model includes: ; in: Temperature anomaly value at the crack location (x,y). The larger the value, the higher the degree of temperature anomaly and the more likely it is to be in a temperature anomaly region. The temperature difference between the cracked area and the area without cracks is calculated by using infrared wavelength data obtained from infrared images to determine the surface temperature difference. : The thermal conductivity of the crack at location (x,y).

[0043] Furthermore, the crack thermal conductivity includes: ; in: : Thermal conductivity at the crack; The thermal conductivity of a normal material without cracks can be found in the material's physical property table. The magnitude of the thermal resistance of the medium inside the crack, such as air or other media, can be found in the material's physical property table; The thermal resistance of a normal material without cracks can be found in the material's physical property table. By combining the thermal resistance of the medium inside the crack with the thermal resistance of the normal material, and using the thermal conductivity coefficient of the material, a thermal resistance model is used to represent the thermal conductivity of the crack, thus obtaining the crack thermal conductivity coefficient.

[0044] Furthermore, the internal thermal resistance of the crack includes: ; in: : The length of the crack; The thermal conductivity of the medium inside the crack, such as air or other media, can be found in the material's physical property table. : The width of the crack; The thermal resistance of the medium in the crack is calculated based on the length and width of the crack and the thermal conductivity of the medium in the crack.

[0045] This model describes how to calculate the effect of a crack on temperature based on the temperature difference between the cracked area and the area without a crack, thereby determining the location of the temperature anomaly region.

[0046] Traditional surface temperature analysis methods typically use a single temperature monitoring method. This method relies solely on temperature data obtained from a temperature sensor and determines the presence of anomalies by setting a temperature threshold. However, this method cannot effectively identify temperature changes caused by structural damage such as cracks, especially when there are multiple cracks on the pump body surface, where the feedback from the temperature sensor is not accurate enough. This model combines the thermal conductivity of the cracked area and the temperature difference between the cracked area and other normal areas to calculate the impact of cracks on the surface temperature of special pumps, thereby improving the accuracy and precision of temperature anomaly detection.

[0047] The steps for using the above model include: Data acquisition: The crack length is obtained from the crack location data obtained in step S2. and width The thermal conductivity coefficient of a normal material without cracks was obtained by searching the literature. The thermal resistance of normal materials without cracks Thermal conductivity of the medium inside the crack ; Parameter calculation: Based on the infrared image obtained in step S1, the temperature difference between the cracked area and the area without cracks is calculated according to the relationship between infrared wavelength and temperature. The thermal resistance of the medium inside the crack was calculated based on the obtained data. Thermal conductivity at the crack Temperature anomalies at the crack location ; Surface temperature analysis: Based on the obtained temperature anomaly value, it is compared with the set special pump surface temperature threshold. If the temperature anomaly value is greater than the threshold, it is determined that this is a temperature anomaly area. Adjustments are made based on the crack location image. If the image position (x,y) is a temperature anomaly area, the gray value here is set to 1, otherwise it is set to 0, thereby accurately locating the temperature anomaly area and obtaining the temperature anomaly area location data.

[0048] S4. Perform anomaly analysis based on the location data of the temperature anomaly area to obtain the anomaly analysis results.

[0049] In this embodiment, the anomaly analysis includes: Based on the obtained temperature anomaly area location data, an anomaly analysis model is used, combined with vibration data, such as frequency characteristics and amplitude changes, to perform anomaly analysis, determine the operating status of the special pump, and further confirm whether there are any anomalies, thus obtaining the anomaly analysis results.

[0050] Furthermore, the anomaly analysis model includes: ; in: : Anomaly detection value, used to determine whether there is an anomaly in the special pump; : A set of pixel locations representing the distribution area of ​​temperature anomalies; The number of pixel locations in the temperature anomaly area; The dominant frequency component reflects the main frequency characteristics of the signal. : Vibration amplitude fluctuation, indicating the intensity of vibration; : Weighting coefficient for the number of pixel positions in temperature anomaly areas, used to adjust the impact of the number of pixel positions in temperature anomaly areas on anomaly detection; The weighting coefficient of the main frequency component is used to adjust the influence of the main frequency component on anomaly detection. The weighting coefficient for vibration amplitude fluctuation is used to adjust the influence of vibration amplitude fluctuation on anomaly detection.

[0051] Furthermore, the vibration amplitude fluctuation includes: ; in: : Duration of the vibration signal; The amplitude of the vibration signal of a special pump as a function of time t; The vibration amplitude fluctuation is obtained by calculating the root mean square of the vibration signal amplitude, thereby determining the intensity of the vibration.

[0052] Furthermore, the dominant frequency component includes: ; in: Imaginary unit; Frequency magnitude; The vibration signal is converted to the frequency domain using Fast Fourier Transform (FFT), and the frequency at which the vibration amplitude reaches its maximum value in the spectrum is extracted, which is the main frequency component.

[0053] This model describes how to perform anomaly analysis on temperature anomaly regions based on vibration amplitude fluctuation and the dominant frequency component of the vibration signal, and obtain the anomaly analysis results.

[0054] Traditional anomaly analysis methods typically process only a single data point from temperature and vibration data, which can easily lead to misjudgments due to the failure of a single parameter. Furthermore, they often use fixed weights, making them unable to adapt to new environments. In contrast, this model integrates temperature and vibration data, enabling more comprehensive anomaly analysis, reducing the risk of misjudgments and omissions, and improving the accuracy of anomaly detection. Moreover, this model can adjust the weight coefficients through training, thus improving its adaptability.

[0055] The steps for using the model include: Data collection: Obtain pixel location data of the temperature anomaly area from step S4, and obtain a set of pixel locations of the temperature anomaly area. The number of pixel locations in temperature anomaly areas Vibration data is obtained through sensors. ; Parameter calculation: Calculate the vibration amplitude fluctuation based on the obtained data. and the dominant frequency component of the vibration signal ; Anomaly detection: Calculate the anomaly detection value based on the obtained data. The value is compared with the set abnormality judgment threshold of the special pump. If the abnormality judgment value is greater than the set threshold, the special pump is judged to be abnormal; otherwise, it is judged to be normal.

[0056] S5. Based on the anomaly analysis results, conduct a health status assessment of the special pump and obtain the assessment results.

[0057] In this embodiment, the health status assessment of the special pump includes: The key purpose of health status assessment is to ensure the stability and reliability of special pumps in practical applications. Long-term operation of pump equipment is affected by various factors such as the external environment and workload, leading to varying degrees of failure. Health status assessment can promptly identify potential problems, preventing equipment downtime due to malfunctions and avoiding sudden shutdown accidents during production.

[0058] The purpose of this step is to analyze the health status of the pump equipment based on the anomaly analysis results obtained in step S4, and to provide an objective health assessment result. This provides decision support for determining whether the equipment needs maintenance or repair, helps managers or maintenance personnel judge the operating status of the equipment, and take timely repair or adjustment measures to maintain and replace parts. This ensures that the equipment can operate in the best condition, avoids equipment damage or failure, reduces downtime, and thus effectively extends the service life of the equipment, reduces the failure rate, improves overall production efficiency, increases maintenance efficiency and saves costs, and ensures production safety.

[0059] To conduct a health status assessment, temperature and vibration data for abnormal areas are first obtained. Then, fault mode analysis (FMA) is performed on the identified abnormal pumps. Common fault modes include mechanical faults (such as bearing damage or impeller imbalance, typically manifested as increased vibration amplitude), temperature anomalies (such as pump overheating or insufficient lubrication, typically manifested as localized temperature increases), and electrical faults (such as poor motor contact, typically manifested as both vibration and temperature anomalies). FMA methods usually rely on historical data and experience, combined with the obtained temperature and vibration data, to identify the specific fault mode of the equipment. Then, a fault judgment value is obtained by weighting the temperature and vibration data to determine the degree of equipment failure. If the judgment value is greater than the set severe fault threshold, the equipment is considered to be faulty, and immediate repair is recommended. If the judgment value is between the minor and severe fault thresholds, the equipment may have a minor fault, and regular inspection is recommended. If the judgment value is less than the threshold, the equipment is operating well and does not require immediate maintenance.

[0060] Example 2: This embodiment also provides a computer device applicable to a real-time monitoring method for the status of a special pump based on image processing, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the real-time monitoring method for the status of a special pump based on image processing as proposed in the above embodiment.

[0061] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements a real-time monitoring method for the status of a special pump based on image processing as proposed in the above embodiment.

[0062] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0063] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0065] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0066] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for real-time monitoring of the status of a special pump based on image processing, characterized in that, Includes the following steps: S1. Acquisition of surface images of special pumps, obtaining surface image data of special pumps; S2. Crack detection is performed based on the surface image data of the special pump to obtain crack location data; S3. Perform surface temperature analysis based on crack location data to obtain location data of temperature anomaly areas; Surface temperature analysis includes: Based on the obtained crack location data, a temperature analysis model is used, combined with the thermal conductivity coefficient and the temperature deviation at the crack compared to the temperature at the crack-free area, to conduct temperature analysis and obtain the location data of the temperature anomaly area. The temperature analysis model includes: ； in: Temperature anomaly at the crack location (x, y); The temperature difference between the cracked area and the area without cracks; : Thermal conductivity of the crack at location (x,y); The crack thermal conductivity includes: ； in: : Thermal conductivity at the crack; Thermal conductivity coefficient of normal material at crack-free locations; The magnitude of the thermal resistance of the medium inside the crack; The thermal resistance of a normal material without cracks; By combining the thermal resistance of the medium inside the crack with the thermal resistance of the normal material, and the thermal conductivity of the material, a thermal resistance model is used to approximate the thermal conductivity of the crack, and the thermal conductivity of the crack is obtained. The internal thermal resistance of the crack includes: ； in: : The length of the crack; : Thermal conductivity coefficient of the medium inside the crack; : The width of the crack; The thermal resistance of the medium in the crack is calculated based on the length and width of the crack and the thermal conductivity of the medium in the crack. S4. Perform anomaly analysis based on the location data of the temperature anomaly area to obtain the anomaly analysis results; S5. Based on the anomaly analysis results, conduct a health status assessment of the special pump and obtain the assessment results.

2. The method for real-time monitoring of the status of a special pump based on image processing according to claim 1, characterized in that, The crack detection in step S2 includes: Based on the obtained surface image data of the special pump, a crack detection model is used to extract edge information at different scales and process it in combination with adaptive morphological operations to separate cracks from background noise, thereby obtaining crack location data.

3. The method for real-time monitoring of the status of a special pump based on image processing according to claim 2, characterized in that, The crack detection model includes: ； in: : The size of the image in the horizontal direction; : The size of the image in the vertical direction; : Image after morphological processing; : Morphological operations; : The merged edge image; : Control parameters for morphological operations.

4. The method for real-time monitoring of the status of a special pump based on image processing according to claim 3, characterized in that, The fused edge image includes: ； in: The total number of scales; : Weighting coefficients at scale s; Edge image information at scale s; The fused edge image is obtained by weighted fusion based on the edge image information.

5. The method for real-time monitoring of the status of a special pump based on image processing according to claim 4, characterized in that, The edge image information includes: ； in: Gradient calculation symbol; : Convolution operator; Original image; Gaussian filter kernel; Standard deviation of the Gaussian kernel; The original image is subjected to multi-scale Gaussian blurring and edge information is calculated to obtain edge image information at different scales.

6. The method for real-time monitoring of the status of a special pump based on image processing according to claim 1, characterized in that, The special pump health status assessment in step S5 includes: Based on the anomaly analysis results, a health status assessment is conducted on the special pumps identified as having anomalies. Through failure mode analysis, the degree of equipment failure is determined, and the failure modes that cause the special pump failure are analyzed, providing a basis for whether the special pump needs maintenance or repair, and obtaining the assessment results of the special pump's health status.

Images